![]() Why Modeling the Spread of COVID-19 Is So Damn Hard. Available online: (accessed on 10 October 2020). Agent-Based COVID-19 Simulation Model: Application to the Argentine Case. The Transdisciplinarity Manifiesto Du Rocher: Paris, France, 1996. A Dynamic Sustainability Analysis of Energy Landscapes in Egypt: A Spatial Agent-Based Model Combined with Multi-Criteria Decision Analysis. Investigating dynamics of COVID-19 spread and containment with agent-based modeling. Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the Ancestral Pueblo. Coupled contagion dynamics of fear and disease: Mathematical and computational explorations. Available online: (accessed on 8 September 2021). World Economic Situation and Prospects as of Mid-2021. Impact on the Labour Market and Income in Latin America and the Caribbean.Bulletin of Discussion: COVID-19 Impact in Peru and Latin America.Evolution and early government responses to COVID-19 in South America. The authors declare no conflict of interest. Research Contribution and Future Direction Finally, it can be seen from the graphs presented that if the days of quarantine increase, the probability of becoming infected with the virus decreases, and the slowdown in the contagion of the virus increases, therefore, it can be concluded that, if the mobility of agents, the increase in contagion decreases while the quarantine is less and less. These graphs are closely related to the health graphs of the population ( Figure 6, Figure 9 and Figure 12), repeating these similar circumstances for both the second and third scenarios. Furthermore, the simulation results in Figure 8, Figure 11 and Figure 14 demonstrate that the elderly are the most affected, as they have a lower probability of surviving the contagion of the virus. Social isolation is the measure that has the greatest impact on the behavior of the spread of the virus and, therefore, the one that most helps to prevent and slow down the spread of the infection.Īs depicted in Figure 7, Figure 10 and Figure 13, among the infected population based on different age groups, the most infected are young people, as they have greater social interaction. This makes it possible to understand the country’s situation, the complex dynamics of the pandemic and simulate in a multidimensional context the non-linear effect of explanatory variables on the evolution of COVID-19. Four scenarios for the evolution of COVID-19 in Peru are investigated, with different levels of restriction on population mobility. The model is implemented in NetLogo to simulate different hypothetical scenarios that approximate the real behavior of the interaction between the virus, humans and their environment, adjusting demographic, medical, social, and institutional parameters associated with the evolution and spread of the virus. This study presents the simulation of multiple agents showing the emerging dynamics of the interaction and influence of a subset of biological and social factors in the development of the COVID-19 pandemic in Peru. The COVID-19 pandemic in Peru began during March 2020, generating a multidimensional crisis that has claimed 198,621 lives as of 8 September 2021. ![]()
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