COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions

Silva, Petrônio Cândido de Lima e; Batista, Paulo Vitor do Carmo; Lima, Hélder Seixas; Alves, Marcos Antônio; Guimarães, Frederico Gadelha; Silva, Rodrigo C. P.

doi:10.1016/j.chaos.2020.110088

Cited by 330 publications

(275 citation statements)

References 47 publications

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“…In the case of apparition of the symptoms related to the disease, they are isolated, tested and hospitalized. Several mathematical models related to the COVID-19 epidemic have been studied (see [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]). In [18], Imai et al conducted computational modeling to establish the size of the disease outbreak in Wuhan.…”

Section: Introductionmentioning

confidence: 99%

Modeling the effects of contact tracing on COVID-19 transmission

Traoré

Konané

2020

Adv Differ Equ

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In this paper, a mathematical model for COVID-19 that involves contact tracing is studied. The contact tracing-induced reproduction number $\mathcal{R}_{q}$ R q and equilibrium for the model are determined and stabilities are examined. The global stabilities results are achieved by constructing Lyapunov functions. The contact tracing-induced reproduction number $\mathcal{R}_{q}$ R q is compared with the basic reproduction number $\mathcal{R}_{0}$ R 0 for the model in the absence of any intervention to assess the possible benefits of the contact tracing strategy.

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Section: Introductionmentioning

confidence: 99%

Modeling the effects of contact tracing on COVID-19 transmission

Traoré

Konané

2020

Adv Differ Equ

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“…A number of papers have appeared in recent literature using AB models to examine COVID-19 outbreak. The papers include: an AB model integrated with mobility data to evaluate intervention measures such as testing, social distancing, contact tracing and quarantine for a potential second wave of the COVID-19 pandemic in the Boston metropolitan area [ 1 ]; a general framework of an AB simulator for COVID-19 using geo-spatial data to evaluate intervention measures such as closure of community locations, shops, social distancing, face mask, isolation, among others [ 34 ]; health and economic impacts of social interventions for COVID-19 [ 42 ]; a probabilistic approach using an AB model to simulate COVID-19 transmission and evaluate mitigation strategies in a closed built environment [ 15 ]; and a granular AB model of COVID-19 in Australia to compare school closures and varying levels of intervention strategies such as air travel, case isolation, quarantine, and social distancing [ 9 ].…”

Section: Methodsologymentioning

confidence: 99%

Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region

Tatapudi

Das

2020

Global Epidemiology

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Purpose Social intervention strategies to mitigate COVID-19 are examined using an agent-based simulation model. Outbreak in a large urban region, Miami-Dade County, Florida, USA is used as a case study. Results are intended to serve as a planning guide for decision makers. Methods The simulation model mimics daily social mixing behavior of the susceptible and infected generating the spread. Data representing demographics of the region, virus epidemiology, and social interventions shapes model behavior. Results include daily values of infected, reported, hospitalized, and dead. Results Results show that early implementation of complete stay-at-home order is effective in flattening and reversing the infection growth curve in a short period of time. Whereas, using Florida's Phase II plan alone could result in 75% infected and end of pandemic via herd immunity. Universal use of face masks reduced infected by 20%. A further reduction of 66% was achieved by adding contact tracing with a target of identifying 50% of the asymptomatic and pre-symptomatic. Conclusions In the absence of a vaccine, the strict stay-at-home order, though effective in curbing a pandemic outbreak, leaves a large proportion of the population susceptible. Hence, there should be a strong follow up plan of social distancing, use of face mask, contact tracing, testing, and isolation of infected to minimize the chances of large-scale resurgence of the disease. However, as the economic cost of the complete stay-at-home-order is very high, it can perhaps be used only as an emergency first response, and the authorities should be prepared to activate a strong follow up plan as soon as possible. The target level for contact tracing was shown to have a nonlinear impact on the reduction of the percentage of population infected. Increase in contact tracing target from 20% to 30% appeared to provide the largest incremental benefit.

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“…Tout en étant très flexible dans sa conception, le temps de calcul d’un MIC peut devenir un facteur limitant si le nombre d’individus simulés est trop grand ou si les mécanismes d’interactions individuelles sont trop complexes. Ainsi, d’autres MIC ont été proposés pour évaluer en continu l’impact épidémiologique et économique de différentes combinaisons d’interventions non-pharmaceutiques à l’échelle d’une zone métropolitaine [ [30] , [31] , [32] , [33] - 34 ]. Complémentaires aux modèles compartimentaux, les MICs constituent une approche de choix pour améliorer notre connaissance et générer de nouvelles hypothèses quant aux mécanismes spatiaux et comportementaux de l’épidémie de Covid-19.…”

Section: Modèles Individus-centrésunclassified