http://www.simulace.info/api.php?action=feedcontributions&feedformat=atom&user=Stek15 2026-07-27T11:25:19Z User contributions MediaWiki 1.31.1 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20709 2021-01-20T13:43:38Z

<p>Stek15: </p> <hr /> <div>{{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from WebMD (2021).<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from Health Service Executive, Ireland (2021).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 2 specifically preset runs will be used to illustrate.<br /> <br /> '''A.''' First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> '''B.''' Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to first diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully, she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> The main issue has, obviously, been data collection. As has been stated under Problem definition, the current sources are not only scarce, but also unreliable. However, I do believe that the positive side of my simulation is that it may be easily adjusted (symptoms added, thresholds edited, ''suspected-positivity-line'' moved, etc.) once we learn more about the disease. For now it may serve as a skeleton and, frankly, may be quite as reliable for the purposes of self-diagnosis as many of the suspiciously expensive home-testing kits available online.<br /> <br /> Expanding the model using specific weights for the individual links between symptoms, if such are discovered during the ongoing COVID-19 research, could also bear very interesting fruits. <br /> <br /> =6. Code=<br /> <br /> [[File:Covid-19 symptoms.nlogo]]<br /> <br /> =Sources=<br /> <br /> WebMD. (2021). Signs of Coronavirus (COVID-19). [online] Available at: [https://www.webmd.com/lung/covid-19-symptoms#1].<br /> <br /> Health Service Executive, Ireland. (2021). Symptoms of coronavirus. [online] Available at: [https://www2.hse.ie/conditions/coronavirus/symptoms.html].<br /> <br /> Who.int. (2019). 2019 Novel Coronavirus. [online] Available at: [https://www.who.int/emergencies/diseases/novel-coronavirus-2019?gclid=CjwKCAiAxp-ABhALEiwAXm6IyeEKwqfMbMGS5b2fuwqvIietIJKlp1bQhD6HQ55HHV9kDxW1Ow15KRoCybsQAvD_BwE].<br /> <br /> Health Infomation and Quality Authority, Ireland. (2020). Evidence summary for COVID-19 viral load over course of infection. [online] Available at: [https://www.hiqa.ie/sites/default/files/2020-06/Evidence-Summary_SARS-CoV-2-duration-of-infectivity.pdf].<br /> <br /> ‌</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20706 2021-01-20T13:41:29Z

<p>Stek15: </p> <hr /> <div>{{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from WebMD (2021).<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from Health Service Executive, Ireland (2021).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 2 specifically preset runs will be used to illustrate.<br /> <br /> '''A.''' First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> '''B.''' Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to first diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully, she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> The main issue has, obviously, been data collection. As has been stated under Problem definition, the current sources are not only scarce, but also unreliable. However, I do believe that the positive side of my simulation is that it may be easily adjusted (symptoms added, thresholds edited, ''suspected-positivity-line'' moved, etc.) once we learn more about the disease. For now it may serve as a skeleton and, frankly, may be quite as reliable for the purposes of self-diagnosis as many of the suspiciously expensive home-testing kits available online.<br /> <br /> Expanding the model using specific weights for the individual links between symptoms, if such are discovered during the ongoing COVID-19 research, could also bear very interesting fruits. <br /> <br /> =7. Code=<br /> <br /> [[File:Covid-19 symptoms.nlogo]]<br /> <br /> =Sources=<br /> <br /> WebMD. (2021). Signs of Coronavirus (COVID-19). [online] Available at: [https://www.webmd.com/lung/covid-19-symptoms#1].<br /> <br /> Health Service Executive, Ireland. (2021). Symptoms of coronavirus. [online] Available at: [https://www2.hse.ie/conditions/coronavirus/symptoms.html].<br /> <br /> Who.int. (2019). 2019 Novel Coronavirus. [online] Available at: [https://www.who.int/emergencies/diseases/novel-coronavirus-2019?gclid=CjwKCAiAxp-ABhALEiwAXm6IyeEKwqfMbMGS5b2fuwqvIietIJKlp1bQhD6HQ55HHV9kDxW1Ow15KRoCybsQAvD_BwE].<br /> <br /> Health Infomation and Quality Authority, Ireland. (2020). Evidence summary for COVID-19 viral load over course of infection. [online] Available at: [https://www.hiqa.ie/sites/default/files/2020-06/Evidence-Summary_SARS-CoV-2-duration-of-infectivity.pdf].<br /> <br /> ‌</div>

Stek15 http://www.simulace.info/index.php?title=File:Covid-19_symptoms.nlogo&diff=20702 2021-01-20T13:35:53Z

<p>Stek15: Covid-19_symptoms.nlogo</p> <hr /> <div>== Summary ==<br /> Covid-19_symptoms.nlogo</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20696 2021-01-20T13:22:14Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from WebMD (2021).<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from Health Service Executive, Ireland (2021).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 2 specifically preset runs will be used to illustrate.<br /> <br /> '''A.''' First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> '''B.''' Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to first diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully, she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> The main issue has, obviously, been data collection. As has been stated under Problem definition, the current sources are not only scarce, but also unreliable. However, I do believe that the positive side of my simulation is that it may be easily adjusted (symptoms added, thresholds edited, ''suspected-positivity-line'' moved, etc.) once we learn more about the disease. For now it may serve as a skeleton and, frankly, may be quite as reliable for the purposes of self-diagnosis as many of the suspiciously expensive home-testing kits available online.<br /> <br /> To improve the model, it would be good to <br /> <br /> =7. Code=<br /> <br /> =Sources=<br /> <br /> WebMD. (2021). Signs of Coronavirus (COVID-19). [online] Available at: https://www.webmd.com/lung/covid-19-symptoms#1.<br /> Health Service Executive, Ireland. (2021). Symptoms of coronavirus. [online] Available at: https://www2.hse.ie/conditions/coronavirus/symptoms.html.</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20682 2021-01-20T13:10:08Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 2 specifically preset runs will be used to illustrate.<br /> <br /> '''A.''' First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> '''B.''' Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully, she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> The main issue has, obviously, been data collection. As has been stated under Problem definition, the current sources are not only scarce, but also unreliable. However, I do believe that the positive side of my simulation is that it may be easily adjusted (symptoms added, thresholds edited, ''suspected-positivity-line'' moved, etc.) once we learn more about the disease. For now it may serve as a skeleton and, frankly, is quite as reliable for the purposes of self-diagnosis as many of the suspiciously expensive home-testing kits available online.<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20678 2021-01-20T13:04:18Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 2 specifically preset runs will be used to illustrate.<br /> <br /> '''A.''' First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> '''B.''' Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully, she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model4.png&diff=20677 2021-01-20T13:04:10Z

<p>Stek15: Stek15 uploaded a new version of File:Stek15-model4.png</p> <hr /> <div>== Summary ==<br /> Stek15-model4</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model4.png&diff=20676 2021-01-20T13:03:55Z

<p>Stek15: Stek15 uploaded a new version of File:Stek15-model4.png</p> <hr /> <div>== Summary ==<br /> Stek15-model4</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model4.png&diff=20675 2021-01-20T13:03:32Z

<p>Stek15: Stek15 uploaded a new version of File:Stek15-model4.png</p> <hr /> <div>== Summary ==<br /> Stek15-model4</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model4.png&diff=20674 2021-01-20T13:02:19Z

<p>Stek15: Stek15 uploaded a new version of File:Stek15-model4.png</p> <hr /> <div>== Summary ==<br /> Stek15-model4</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20671 2021-01-20T12:58:09Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> To demonstrate the use of this simulation, 3 specifically preset runs will be used to illustrate.<br /> <br /> A. First, we have a young man coming to the simulation clinic. He has a ''Fever'' (1.0), a ''DryCough'' (0.6) and is experiencing some ''ShortnessOfBreath'' (0.5). He has no other symptoms. He is otherwise healthy, active and a non-smoker, therefore his ''contraction-susceptibility'' has been set to 0.0. If we gradually increase his ''exposure-to-virus'' over about 30 ticks, we can watch other symptoms develop at a steady rate. Around 12 ticks the man has been first diagnosed as suspected positive by the model. <br /> <br /> [[File:Stek15-results1.png]]<br /> <br /> B. Second, an elderly lady arrives at the clinic. She has recently been experiencing ''LossOfAppetite'' (1.0) and her ''BodyAches'' (0.8). Otherwise, she has no other symptoms. Due to her age and a smoking habit, her ''contraction-susceptibility'' has been set to 0.7. Let us gradually increase her ''exposure-to-virus'' over about 30 ticks to compare with the man from case A:<br /> <br /> [[File:Stek15-results2.png]]<br /> <br /> We can see that not only the symptoms developed much faster (we were already able to diagnose the lady at about 2 ticks), but the development has also been much steeper, causing the lady to experience almost all of the 7 main symptoms for the majority of the time. Hopefully she got all better soon. <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-results2.png&diff=20669 2021-01-20T12:56:14Z

<p>Stek15: Stek15-results2</p> <hr /> <div>== Summary ==<br /> Stek15-results2</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-results1.png&diff=20664 2021-01-20T12:47:11Z

<p>Stek15: Stek15-results1</p> <hr /> <div>== Summary ==<br /> Stek15-results1</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20662 2021-01-20T12:27:16Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing on it's own (not being caused/followed by other symptoms).<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20661 2021-01-20T12:25:55Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> [[File:Stek15-table1.png]]<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-table1.png&diff=20660 2021-01-20T12:25:38Z

<p>Stek15: Stek15-table1</p> <hr /> <div>== Summary ==<br /> Stek15-table1</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20659 2021-01-20T12:21:55Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses over time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability of becoming activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20657 2021-01-20T12:19:04Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an affected person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of the symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses in time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability come activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing the symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20656 2021-01-20T12:15:51Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created to be adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on newly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an ill person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of said symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current settings, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses in time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability come activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing said symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20655 2021-01-20T12:12:49Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel required are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on freshly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an ill person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of said symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current setting, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses in time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability come activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing said symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20654 2021-01-20T12:11:40Z

<p>Stek15: </p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =1. Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel spent are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on freshly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =2. Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =3. Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an ill person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of said symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current setting, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses in time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability come activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing said symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =4. Results=<br /> <br /> <br /> <br /> =5. Conclusion=<br /> <br /> =7. Code=<br /> <br /> =Sources=</div>

Stek15 http://www.simulace.info/index.php?title=WS_2020/2021&diff=20653 2021-01-20T12:08:10Z

<p>Stek15: </p> <hr /> <div>Semestral papers from winter term 2020/2021. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2020/2021|assignment approved]]<br /> <br /> [http://www.simulace.info/index.php/Impact_of_late_lockdown_and_hospital_capacity_on_19-COVID_spread Impact of late lockdown and hospital capacity on 19-COVID spread] , Thomas BAEUMLIN [[User:Toscool|Toscool]] ([[User talk:Toscool|talk]]) 16:15, 7 January 2020 (CET)<br /> <br /> [[Comparing_multiple_airplane_boarding_methods]], Adam Jedlička [[User:Jeda00|jeda00]] 20:45, 17 January 2020 (CET)<br /> <br /> [http://www.simulace.info/index.php/Effects_of_VAT_exemption_abolition_to_imported_low_value_consignments Effects of VAT exemption abolition to imported low value consignments], Adela Barchankova [[User:bara15|bara15]] ([[User talk:bara15|talk]]) 21:00, 18 January 2020 (CET)<br /> <br /> [[Retirement Planning]], [[User:Louis|Louis]] ([[User talk:Louis|talk]]) 02:08, 19 January 2021 (CET)<br /> <br /> [[Covid 19 - Contacts]], [[User:praa03|praa03]] 17:08, 19 January 2021 (CET)<br /> <br /> [[Corrupted Blood Incident]], [[User:Frym06|Frym06]] ([[User talk:Frym06|talk]]) 19:32, 19 January 2021 (CET)<br /> <br /> WIP [[Car acquisition]], [[Mico00|Mico00]] 19:59, 19 January 2021 (CET)<br /> <br /> [[Widening the spread between rich and poor]], [[User:Sára|Sára]] 21:40, 19 January 2021 (CET)<br /> <br /> [[Development of real estate prices in Prague]], [[User:netp02|netp02]] 23:41, 19 January 2021 (CET)<br /> <br /> [[IT Team simulation]], [[Antonsverlov]] , 20 January 2021 (CET)<br /> <br /> [[Evolution of retirement pay in the Czech Republic]], Arnošt Sychra [[User:Syca00|Syca00]] 03:30, 20 January 2020 (CET)<br /> <br /> [http://www.simulace.info/index.php/COVID-19_symptoms COVID-19 symptoms simulation] , Kamila Steinbartová [[User:stek15|stek15]] ([[User talk:stek15|talk]]) 13:07, 20 January 2020 (CET)</div>

Stek15 http://www.simulace.info/index.php?title=COVID-19_symptoms&diff=20652 2021-01-20T12:06:08Z

<p>Stek15: Created page with &quot;In progress! {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}} '''Name:''' COVID-19 symptoms simulation '''Author:''' Kamila Steinbartová, stek15, J...&quot;</p> <hr /> <div>In progress!<br /> <br /> {{DISPLAYTITLE: Covid-19 symptoms simulation (NetLogo)}}<br /> <br /> '''Name:''' COVID-19 symptoms simulation<br /> <br /> '''Author:''' Kamila Steinbartová, [[User:stek15|stek15]], Jan 2021<br /> <br /> '''Used tool:''' NetLogo 6.1.1<br /> <br /> =Problem definition=<br /> We are all familiar with the present pandemic situation. One of the main factors of the financial impact of the disease is the need for testing. Be it individual or nation-wide, the time, resources, and personnel spent are far beyond what could be manageable long-term. Securely identifying the symptoms and tracking them could allow for more accurate quarantine measures. <br /> <br /> The available information on the COVID-19 disease is being constantly updated and developed. To take this into consideration, the model was created adaptable and easily editable. Even during the course of model creation, which only took a couple of weeks, the most commonly listed symptoms have been updated based on freshly available information several times. The final list of 7 of the most common symptoms among patients hospitalized with COVID-19 as stated by Chinese researchers has been taken from https://www.webmd.com/lung/covid-19-symptoms#1.<br /> <br /> The table below lists said symptoms, their percentual incidence in patients hospitalized with COVID-19, and the added probability '''P(A∪B∪C)''' of occurrence in any sick patient (considering COVID-19, the common cold, and the flu), calculated from data taken from https://www2.hse.ie/conditions/coronavirus/symptoms.html).<br /> <br /> 1. Fever ; 99% ; 99.1%<br /> <br /> 2. Shortness of breath ; 31% ; 90.199%<br /> <br /> 3. Fatigue ; 70% ; 99.5%<br /> <br /> 4. Dry cough ; 59% ; 99.25%<br /> <br /> 5. Loss of appetite ; 40% ; 91.9%<br /> <br /> 6. Body aches ; 35% ; 99.9%<br /> <br /> 7. Mucus or phlegm ; 31% ; 97.5%<br /> <br /> The use of these parameters in my simulation is explained in the following sections.<br /> <br /> =Method=<br /> <br /> The tool used for this simulation is NetLogo 6.1.1.<br /> <br /> =Model=<br /> This model is a representation of the COVID-19 virus, or rather of the effects said virus has on an ill person. Each node of the model represents one of the 7 symptoms listed above. The model simulates the development of said symptoms in a patient. If the symptom turns red, it became activated (experienced). Upon deactivation, it turns black again. Each symptom is connected to all the others in order to create a conclusive network.<br /> <br /> [[File:Stek15-model1.png]]<br /> <br /> Before the simulation is started, the user is to choose the initial settings for the model using the two sliders on the left side of the model: ''contraction-susceptibility'' and ''exposure-to-virus''. With the current setting, a patient is suspected positive, if they are experiencing 4 or more symptoms.<br /> <br /> [[File:Stek15-model2.png]]<br /> <br /> The plot shows the number of activated symptoms over time (per tick). As the ''exposure-to-virus'' slider is adjusted, the green line moves up or down accordingly. The same goes for ''contraction-susceptibility'' and the grey line. A person with very low ''contraction-susceptibility'' (e.g. a young, healthy, active person, or, quite newly, also a person that has been vaccinated) will be much less likely to cross the black suspected-positivity-line, even if the ''exposure-to-virus'' rate is very high. Similarly, an elderly person with long-term respiratory issues would be considered very contraction susceptible, and even a low ''exposure-to-virus'' rate might cause such person to cross the ''suspected-positivity-line''.<br /> <br /> [[File:Stek15-model4.png]]<br /> <br /> With the sliders per symptom on the right side of the model, it is possible to intervene on a specific symptom by curing it (by lowering it to 0.0). Similarly, it is possible to first set up the network with several symptoms (of identical or differing values) and see how such a patient progresses in time.<br /> <br /> [[File:Stek15-model3.png]]<br /> <br /> The probability come activated for each symptom is represented as '''1 / 1 + e^(a(b - A))'''.<br /> <br /> Here, '''A''' represents the total amount of stress on a symptom (slider of symptom + ''exposure-to-virus'' + the activity of it's neighbors). In other words, A shows us how much pressure there is on the symptom to become activated. <br /> <br /> '''a''' controls the steepness of the probability function and represents how much will a change in A affect the chance of activating the symptom. The parameter a has been adjusted according to the probability of a hospitalized COVID-19 patient developing said symptom. <br /> <br /> And finally, '''b''' represents the threshold of a symptom - the higher b, the more activity in the neighbors such symptom needs to become activated. To take in consideration false positivity, the parameter b has been adjusted according to the combined probabilities of a patient developing said symptom while sick with the COVID-19 virus, the common flu, or cold. For example, for a symptom with the probability of development of 0.90199, the threshold has been set to 0.09801. The more common a symptom is, the higher are the chances of it appearing alone, not being caused/followed by other symptoms.<br /> <br /> =Results=<br /> <br /> <br /> <br /> =Conclusion=<br /> <br /> =Code=</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model4.png&diff=20651 2021-01-20T11:58:45Z

<p>Stek15: Stek15-model4</p> <hr /> <div>== Summary ==<br /> Stek15-model4</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model3.png&diff=20650 2021-01-20T11:50:29Z

<p>Stek15: Stek15-model3</p> <hr /> <div>== Summary ==<br /> Stek15-model3</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model2.png&diff=20649 2021-01-20T11:47:33Z

<p>Stek15: Stek15-model2</p> <hr /> <div>== Summary ==<br /> Stek15-model2</div>

Stek15 http://www.simulace.info/index.php?title=File:Stek15-model1.png&diff=20648 2021-01-20T11:45:51Z

<p>Stek15: stek15-model1</p> <hr /> <div>== Summary ==<br /> stek15-model1</div>

Stek15 http://www.simulace.info/index.php?title=Assignments_WS_2020/2021&diff=20042 2020-12-18T10:31:20Z

<p>Stek15: Adding my semestral project proposal</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> == Spread of 19-COVID == <br /> '''Author''' : [[User:Toscool|Toscool]] ([[User talk:Toscool|talk]]) 11:32, 11 December 2020 (CET) <br /> <br /> '''Simulation''' : I will build a dynamic model of an epidemic his the dynamic issues. I will focus on the spread of Covid-19 in a population and Specially I will try to modelize the effect of a lockdown on the number of infections and deaths. <br /> I will start with a susceptible population, with one infected people that infect 10 people with a certain probability. When you are infected you go the doctors and with a certain probability you go in the recovery population or in the hospital. Then you have a percentage of chance to die in this hospital or to recover. The lockdown will triggered when the ratio of people who where to the doctor and total population exceed a certain percentage. We lockdown and by reducing the number of people you infect in the beginning.<br /> <br /> '''Goal of simulation''' : I aim to model the impact of a lockdown on the spread of COVID on a population. It will also show the difference between countries, for example if we lockdown too late. What will be the impact of these decisions.<br /> <br /> <br /> '''Method''' : I will use VensimPLE for this simulation, as it is a dynamic system, and In my home university I know that I will need to use VensimPLE for a future project and course so it will help me a lot.<br /> <br /> ::The topic souds good - the question is, on what data sources will you base your simulation (equations) on? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:13, 12 December 2020 (CET)<br /> :::I will use the case of Switzerland and use data from this website (https://corona-data.ch). I will try to reproduce the number of cases and the speed of the spread.[[User:Toscool|Toscool]] ([[User talk:Toscool|talk]]) 13:39, 13 December 2020 (CET)<br /> ::: OK. '''Approved''' but do not forget to include in the report for the simulation how you have derived the equations in the simulation from the data. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:49, 14 December 2020 (CET)<br /> <br /> == NFL Free Agency ==<br /> '''Author''' : [[User:TimWalenczak|TimWalenczak]] ([[User talk:TimWalenczak|talk]]) 15:02, 11 December 2020 (CET) <br /> <br /> '''Simulation''' : Free agency is a period of time during the off-season in the National Football League in the US (few weeks). During that period, all 32 teams in the league can sign active players that have no contract with any other team, so called free agents.<br /> The process goes as follows: at the beginning of free agency a certain amount of players become free agents due to expiring contracts. All players can play one certain position and want to receive a certain salary. While all the teams are looking for players to play a particular position and have a certain amount of money they can spend on players. Demand and supply of a specific position group influences their value. A certain supply-and-demand-factor for each position group adjusts a player's desired salary according to his position group. Players and teams get in touch when they are located next to each other. When the position needs matches the offer and a player’s salary fits the money a team offers, that player is signed by the team and disappears from free agency market. If the offer doesn’t fit the need, a player walks on to the next team. With every round/tick/signing the supply-and-demand-factor and thus the value of players in each position group change and so does the amount of money teams are able to spend on further players. Free agency ends after a distinct number of ticks or even earlier if all players are signed or teams don’t have any money left to spend.<br /> <br /> '''Goal of simulation''' : is to find out how a player's value changes during the period of free agency. Especially interesting is, whether players that sign a new contract early in the period received higher payments or the ones signing late in the period. Maybe a certain strategy can be deducted for players depending on their initial situation (supply-and-demand-factor) in order to reach a highly-paid contract.<br /> <br /> '''Method''' : Since I don’t know VensimPLE yet, I think I will use NetLogo, because I see some similarities between my case and the “Escape building” case we modelled recently.<br /> <br /> : It is a bit unusual, but I can see something in common rather with Market Structure than with Building Escape. Please, just elaborate it into a greater detail and specify, how exactly should your simulation work. I am not sure you have a coherent idea about the solution. But if you work this out, I don't see a problem. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:45, 11 December 2020 (CET)<br /> <br /> <br /> ==COVID-19 mass testing== <br /> <br /> '''Author''' : [[User:Milan P|Milan P]] ([[User talk:MilanP|talk]]) 20:15, 13 December 2020 (CET) <br /> <br /> '''Simulation''' : I will create simulation for mass Covid-19 testing in Slovakian village or small city. In previous rounds of this real mass testing in Slovakia testing itself took place in one day and in one sampling point, this will be the same with this simulation. In this model roles (medical staff, administrative workers, and police officer), will have assigned costs to them in order to include financial aspect into the simulation. Duration of the simulation will be 4 hours. This will serve as base model which will be adjusted in two ways. Firstly, I will create another model in which I will duplicate sampling point so 2 sampling points will be in simulation. Second adjustment to the base model will be that simulation time will be prolongated to 8 hours. After running all 3 models, outputs will be compared and the optimal one will be selected.<br /> <br /> '''Goal of simulation''' : In the previous rounds of COVID-19 mass testing in Slovakia certain sampling points had experienced massive overload and were not prepared for the amount of citizens incoming to be tested. Therefore, this simulation would aim to simulate the scenario with data from previous testing rounds and might be helpful if the governments decides to repeat mass testing again.<br /> <br /> '''Method''' : Discrete simulation - SIMPROCESS<br /> <br /> : The topic looks generaly good, my only concern is that is could be too simple. What steps of the model do you plan? Where do you obtain the real data? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:54, 17 December 2020 (CET)<br /> <br /> : I plan to obtain data from this site https://www.svidnik.sk/oznamy/druhe-kolo-plosneho-testovania-vysledky.html - on this website number of patients to be tested and number of points/places that take samples can be found. I know that each point/place has to have: 1 healthcare worker, 2 administrative workers and 1 police officer. Data about hourly rate can be obtained from this site: https://www.platy.sk/platy. Maybe I would like to adjust my original proposal, there would not be 1 point/place taking samples but 9 and I would create another simulation for more or less points/places. Generally I like the idea of splitting patients into two groups and I might include that in my simulation as well. One would consist of people older than 60 and the other one would consist of the rest of population. I will split the population according to demographic structure of Slovakia https://sk.wikipedia.org/wiki/Demografia_Slovenska#Vekov%C3%A1_%C5%A1trukt%C3%BAra. Steps I can think of right now: Patients entering, splitting the patients, administrative work, waiting, taking samples, waiting, receiving results, again splitting patients based on the results (negative ones can go home with certificate and positive ones would require additional steps like receiving information what to do next etc.) and the the simulation would end. [[User:Milan P|Milan P]] ([[User talk:MilanP|talk]]) 00:05, 18 December 2020 (CET)<br /> <br /> ==Nanorobots vs. Cancer==<br /> <br /> '''Author''' : [[User:Dufa00|Dufa00]] 20:24, 14 December 2020 (CET)<br /> <br /> '''Simulation''' : Simply put: I want to simulate human body environment, where cancer is going to spread, but can be cured via nanorobotics. At the begining, observer will adjust parametres that will state the chance of cancer (bad vs. good life style etc.). Then the simulation of cancer will happen. With assigned button, observer can deploy nanorobots (which feed on the cancer) and watch how the robot or robots manage to heal the human body from cancer. If the observer does not press the deploy button, the human body will eventually succumb to the disease. I am still thinking about puting there a little twist, danger that comes with puting nanorobots into human body to see, if the treatment is worth the risk or not.<br /> <br /> '''Goal of simulation''' : Main goal is to highlight the potential effectiveness of nanorobots. Sidequest might be to compare pros and cons that come along with deployment of the nanorobots.<br /> <br /> '''Method''' : NetLogo<br /> <br /> : An interesting idea. Unfortunately, because this is completely unrealistic, you cannot work with any reasonable data and it means that you are not able to prove the meaningfulness of the results. Please suggest something else. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 03:04, 17 December 2020 (CET)<br /> <br /> ==Widening the spread between rich and poor==<br /> <br /> '''Author''' : [[User:Sára|Sára]] 21:00, 14 December 2020 (CET)<br /> <br /> '''Simulation''' : After 2007-9 world economic crisis a new monetary policy, rather unconventional, was established to escape recessions/depressions. It is called quantitative easing. Although this tool succeeded in reverting the depression and proved helpful during coming years, it is highly discussed what side effects this tool has. A lot of researches suggest that it widens the gap between rich and poor people and make prices of bonds and stocks highly overheated.<br /> <br /> '''Goal of simulation''' : Based on macroeconomics theory and semantic experiences from quantitative easing and its influence on other macroeconomic agents like interest rate, inflation, monetary supply etc., i would like to simulate behavior of banks and people during quantitative easing and i would like to work with different values of macroeconomic agents and bank and people behavior in order to try to find out whether widening the spread between rich and poor is an unevitable effect, whether it is sustainable in long term and whether they are possibilities how to erase this side effect.<br /> <br /> '''Method''' : NetLogo<br /> <br /> : I like the topic (although quantitative easing is just an eufemism for money printing, hence the implications are quite predictable). Before I finally approve, please elaborate it deeper. How exactly you will simulate, what will be the agents, what will they do, etc. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:47, 17 December 2020 (CET)<br /> <br /> ==Rhino population in Africa==<br /> <br /> '''Author''' : [[User:Michal F.|Michal F.]] ([[User talk:Michal F.|talk]]) 17:23, 15 December 2020 (CET)<br /> <br /> '''Simulation''' : Rhinos once roamed many places in Europe, Asia, and Africa. At the beginning of the 20th century, about 500 000 rhinos roamed Africa and Asia. By 1970, rhino numbers dropped to 70,000, and today, around 27,000 rhinos remain in the wild. Africa's rhino population could face extinction within 10 years, animal welfare experts have warned. South Africa has the largest population of the species in the world however, the species still remains under threat from poaching for its horn and from habitat loss and degradation.<br /> Data: http://www.rhinoresourcecenter.com/pdf_files/119/1192816187.pdf, http://www.scielo.org.za/pdf/koedoe/v59n1/15.pdf, https://rhinos.org/about-rhinos/rhino-species/black-rhino/ <br /> <br /> '''Goal of simulation''' : Based on the available data, I would like to simulate future development of the rhino population (probably the black ones in Africa, cause its most documented). The population is threatened by poaching and the loss of the natural environment. The shrinking population increases people's efforts to protect it from poachers but increases the demand for rhino horns.<br /> <br /> '''Method''' : VensimPLE / Insight Maker<br /> <br /> :: Topic is interesting. Can you specify the simulation in more detail - what would be the input parameters, variables, what all factors, that have an influence on rhino population, will be incorporated? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:55, 16 December 2020 (CET)<br /> <br /> ==Garbage collection==<br /> <br /> '''Author''' : [[User:Dobj02|Dobj02]] ([[User talk:Dobj02|talk]]) 23:07, 16 December 2020 (CET) <br /> <br /> '''Simulation''' : My aim is to simulate garbage collection in a city from the city counciler's point of view. I will focus on 3 main standpoints, specifically Waste production, Waste collection and Waste processing. The amount of produced waste is dependent on the population of the city and the rate of waste production of 1 person per day. Waste collection will be dependent on the amount of garbage trucks and their capacity. The number of employees (drivers and garbage collectors) will also play a role in how many trucks per day can be deployed. The final standpoint, Waste processing, consists of waste incineration and recycling. Incineration and recycling facilities can process different amount of waste per day. The scope of simulation can be either in weeks or months. <br /> <br /> '''Goal of simulation''' : Finding balance between the amount of produced waste and its disposal by regulating the number of employees, trucks, their capacity, and the number of facilities which can either incinerate or recycle waste depending on the type of waste. The population of the city and the waste production of 1 person will be also adjustable. <br /> <br /> '''Method''' : VensimPLE<br /> <br /> ::Sounds complex (maybe too complex) - but on what real data will you base the above described simulation on, so that the results would make sense? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:14, 18 December 2020 (CET)<br /> <br /> ==Financial budget of football club==<br /> <br /> '''Author''' : [[User:netp02|netp02]] ([[User talk:netp02|talk]]) 22:30, 17 December 2020 (CET) <br /> <br /> '''Simulation''' : <br /> <br /> The purpose of this simulation is to simulate important financial aspects of the football club. How much sponsors, players, transfers of players, money for tickets, number of sold club´s souvenir influence the budget of the clubs. <br /> <br /> <br /> '''Goal of simulation''' : <br /> <br /> Finding a balance between incomes and expenses, so the football club could work without any money donation from owner of the club.<br /> <br /> <br /> '''Method''' : VensimPLE<br /> <br /> :: On what real data will you base the above described simulation on, so that the results of your simulation would make sense? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:16, 18 December 2020 (CET)<br /> <br /> ==IT Team simulation==<br /> <br /> '''Simulation:'''<br /> <br /> I would like to create a simulation, which is based on a Process Diagram and which would describe the process of solving business requirements within the IT team in which I now work. The simulation would show the entire course of the process from the origin of the request to the passage of all phases To Develop - Code review - Testing - Acceptance - UAT - Done.<br /> <br /> '''Goal:'''<br /> <br /> By simulation, I would deal with limited resources and limited specialization in the team. The goal would be to find out how many requests and in what rhythm our team would be able to deliver. As for the data basement - It would be based on the situation for the last two months. We solve projects in Jira and so I would be able to calculate how long the request lasted and when it returned to the previous stages + some average morbidity of team members, etc.<br /> <br /> '''Method:''' SimProcess simulation<br /> <br /> ==COVID 19 symptoms==<br /> <br /> '''Author''' : [[User:Stek15|Stek15]] ([[User talk:Stek15|talk]]) 11:31, 18 December 2020 (CET)<br /> <br /> '''Simulation''' : <br /> <br /> The model will represent COVID 19. The nodes in the model will represent the common symptoms of COVID 19. According to the World Health Organisation (WHO, 2020) there are 8 common symptoms: fever, dry cough, fatigue, shortness of breath, loss of appetite, confusion, persistent pain or pressure in the chest, high temperature (above 38 °C). The symptoms of said disease are thought to have direct causal relations with one another. If a person develops a symptom of COVID 19 (e.g. shortness of breath), this increases the likelihood of them developing other symptoms (e.g. loss of appetite, fever). Similarly, if one symptom disappears, the others might as well. The greatest danger is present for people vulnerable to the disease. The model will predict whether a person is vulnerable (due to high age) or resistant to the disease and take this in consideration.<br /> <br /> '''Goal of simulation''' : <br /> <br /> Each tick the probability of a symptom being developed will be calculated. The probability will depend on certain parameters + the total activation of the neighbouring symptoms at the previous tick. These parameters will be the supposed connection between the nodes (slider), influences unrelated to the disease (slider; said age or other respiratory diseases) and the threshold of each symptom.<br /> <br /> '''Method''' : NetLogo</div>

Stek15