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. 2021 Apr 6;9(4):e24192.
doi: 10.2196/24192.

An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study

Alessio Staffini #  1   2   3 , Akiko Kishi Svensson  3   4   5 , Ung-Il Chung  3   6   7 , Thomas Svensson  3   4   6
Affiliations

An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study

Alessio Staffini et al. JMIR Med Inform. .

Abstract

Background: The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations.

Objective: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health.

Methods: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection.

Results: The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths.

Conclusions: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

Keywords: COVID-19; SARS-CoV-2; agent; agent-based modeling; computation; computational epidemiology; computational models; epidemiology; modeling; policy; public health; spread.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Simplified flowchart of interaction mechanisms in the model.
Figure 2
Figure 2
(a) Positive, hospitalized, in isolation, and asymptomatic figures for Italy. (b) Evolution of the contagion for Italy. The graphs consider the sum of the agents belonging to each category shown in the legend for each day of the simulation.
Figure 3
Figure 3
(a) R0 and Re indexes for Italy (with national borders reopening). (b) R0 and Re indexes for Italy (with no national borders reopening).
Figure 4
Figure 4
(a) Positive, hospitalized, in isolation, and asymptomatic figures for Germany. (b) Evolution of the contagion for Germany. The graphs consider the sum of the agents belonging to each category shown in the legend for each day of the simulation.
Figure 5
Figure 5
(a) Positive, hospitalized, in isolation, and asymptomatic figures for Sweden. (b) Evolution of the contagion for Sweden. The graphs consider the sum of the agents belonging to each category shown in the legend for each day of the simulation.
Figure 6
Figure 6
(a) R0 and Re indexes for Sweden. (b) Herd immunity rate for Sweden.
Figure 7
Figure 7
(a) Hospital saturation rate for Sweden. (b) Hospital saturation rate for Sweden (with no limit on the number of ICU beds). ICU: intensive care unit.
Figure 8
Figure 8
(a) Positive, hospitalized, in isolation, and asymptomatic figures for Brazil. (b) Evolution of the contagion for Brazil. The graphs consider the sum of the agents belonging to each category shown in the legend for each day of the simulation.
Figure 9
Figure 9
(a) Hospital saturation rate for Brazil. (b) Hospital saturation rate for Brazil (with no limit on the number of ICU beds). ICU: intensive care unit.
See this image and copyright information in PMC

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