Extending the corona virus model with the social impact theory to evaluate the robustness

Abstract

COVID-19 is a life-changing pandemic. It is very important to reduce the number of deaths by COVID-19 with measures to avoid overloading hospitals. Choosing the right measures is hard because of the missing information we don’t know in advance. For example, we don’t know how many people will obey to these measures and how much these measures will reduce the number of deaths. Therefore, models are designed to simulate different scenarios to predict the results of these measures. Such a model is the Sanaa model [1].

In this thesis the Sanaa model will be studied and extent with social impact according to the social impact theory [2]. To do so, first the scenarios in the Sanaa paper [1] will be run 50 times with Behaviorspace [20] in Netlogo [36], which results in more reliable mean values of the deaths in each scenario. Then, code will be added to the Sanaa model [1] to improve the model. This leads to a higher number of deaths. Finally, code will be added to the Sanaa model [1] to implement the social impact of agents inspired by the smoking model [4]. In this model, they already did implement social impact with a distinction between intrinsically and extrinsically motivated agents. All the results of the steps named above will be compared with the results of the Sanaa paper [1]. Now, it can be concluded; there is a significant difference between these results but staying home and isolation policies in addition to preventing travel between cities still are the measures that reduce the number of deaths the best. In addition, having a higher average of people isolating or hospitalizing will cause a reduction in the number of deaths due to COVID-19.