Simulating the spread of a viral disease under non-pharmaceutical interventions from a social science perspective (original) (raw)
Related papers
2020
The spreading of viruses in a population depends not only on biological factors and the availability of effective pharmaceutical products, but also on human behaviour. Since in the case of Covid-19, there are currently no pharmaceutical measures available, the discussion focuses on non-pharmaceutical measures such as social distancing and quarantines. However, whether and to what extent these measures achieve the desired effects, does not only depend on medical parameters but also on human behaviour: A sufficient number of individuals need to change their social behaviour to successfully contain the spreading of Covid-19. We offer an interactive computer simulation that allows us to compare different policy measures and social rules such as social distancing and quarantine in terms of their potential for virus containment. In the following text, we describe the basic structure as well as some implications of a simulation designed to model the effectiveness of different measures to c...
Social Simulations for Intelligently Beating COVID-19
2020
The COVID-19 virus has led to a world-wide crisis that requires governments and stakeholders to take far-reaching decisions with limited knowledge of their consequences. This paper presents the ASSOCC model as a valuable decision-support tool for anticipating the consequences of possible measures by considering many interwoven aspects at the individual, group and societal level. Moreover, this paper illustrates how this model can be applied to study the effects of different testing strategies on the spread of the virus and the healthcare system. We found that excluding age groups from random testing was ineffective, while prioritizing testing healthcare and education workers was effective, in combination with isolating the household of an infected person. Keywords— COVID-19, Agent-Based Simulation, Decision Support, Values, Needs
We have created a model based on artificial agents that describes some aspects of the diffusion dynamics of COVID-19, especially aspects related to the link between social behaviour and contagion dynamics. Starting from the first part of this article [1], published on April 2, we investigated how we can link a particular socio-cultural attitude to some effects on the lethality of the epidemic. As a starting data, we show the effect that the epidemic had in terms of difference between the number of deaths in the first quarter of 2019 and the first quarter of 2020, in 1,698 Italian municipalities [2]. Once we have established how the incidence of the virus was very irregular, we assume that one of the causes may be some social behaviour, through the simulation model described in the previous publication [1], which has been appropriately revised.
Simulating the Evolution of Infectious Agents Through Human Interaction
2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME)
Human interaction and behavior were proven to be decisive factors for disease spread among the members of a community. Recent events, determined by the COVID-19 outbreak, forced us to develop methods of control, in order to lower the reproductive number and overall, reduce the number of cases. We propose a software simulation method centered on in what manner a virus spreads inside a closed space. This approach required the implementation of a simplified model of human behavior, while considering the factors that influence the rate of transmission between individuals. Agent-based simulation allowed us to identify different control and prevention means, which are efficient in reducing the natural evolution of a pandemic. The results of the analysis offered insight into the rate of spread and human behavior, or habits that led to a significant increase in the total number of infected agents. Recent literature discussed the simulation of disease spread among the inhabitants of larger areas or studied the way that the pathogen can be spread from person to person through droplets containing the virus. Though we strongly consider that previous studies offer important insight, preventing the spread among smaller groups can let us maintain our activity in a safe and supervised manner. The model has been validated by a series of simulations with respect to COVID-19 real data.
To Isolate or Not To Isolate: The Impact of Changing Behavior on COVID-19 Transmission
2020
The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. Neither vaccines nor therapeutic drugs are currently available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects succ...
Minds and Machines, 2020
During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.
Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation
Multi-Agent-Based Simulation XXII, 2022
Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.
Modelling the role of optimal social distancing on disease prevalence of COVID-19 epidemic
International Journal of Dynamics and Control
COVID-19 first spread from Wuhan, China in December 2019 but it has already created one of the greatest pandemic situations ever witnessed. According to the current reports, a situation has arisen when people need to understand the importance of social distancing and take enough precautionary measures more seriously. Maintaining social distancing and proper hygiene, staying at isolation or adopting the self-quarantine strategy are some common habits which people should adopt to avoid from being infected. And the growing information regarding COVID-19, its symptoms and prevention strategies help the people to take proper precautions. In this present study, we have considered a SAIRS epidemiological model on COVID-19 transmission where people in the susceptible environment move into asymptotically exposed class after coming contact with asymptotically exposed, symptomatically infected and even hospitalised people. The numerical study indicates that if more people from asymptotically exposed class move into quarantine class to prevent further virus transmission, then the infected population decreases significantly. The disease outbreak can be controlled only if a large proportion of individuals become immune, either by natural immunity or by a proper vaccine. But for COVID-19, we have to wait until a proper vaccine is developed and hence natural immunity and taking proper precautionary measures is very important to avoid from being infected. In the latter part, a corresponding optimal control problem has been set up by implementing control strategies to reduce the cost and count of overall infected individuals. Numerical figures show that the control strategy, which denotes the social distancing to reduce disease transmission, works with a higher intensity almost after one month of implementation and then decreases in the last few days. Further, the control strategy denoting the awareness of susceptible population regarding precautionary measures first increases up to one month after implementation and then slowly decreases with time. Therefore, implementing control policies may help to reduce the disease transmission at this current pandemic situation as these controls reduce the overall infected population and increase the recovered population.
Scientific Reports
It is imperative that resources are channelled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the following research question: what is the role of community compliance as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities—examples, social distancing, face mask use, and sanitizing—coupled with efforts by health authorities in areas of vaccine provision and effective quarantine—showed that the best intervention in addition to implementing vaccination programs and effective quarantine measures, is the active incorporation of individuals’ collective ...