An agent-based tool for impact assessment of non-pharmaceutical interventions against COVID-19

Keywords: COVID-19 epidemic contention, Non-Pharmaceutical Interventions assessment, Agent-based simulation and tools


Non-Pharmaceutical Interventions (NPI) are currently the only mechanism governments can use to mitigate the impact of the COVID-19 epidemic. Similarly, to the actual spread of the disease, the dynamics of the contention patterns emerging from the application of NPIs are complex and depend on interactions between people within a specific region as well as other stochastic factors associated to demographic, geographic, political and economical conditions. Agent-based models simulate microscopic rules of simultaneous interactions of multiple agents within a population in an attempt to reproduce the complex dynamics of the effect of the contention measures. In this way it is possible to design individual behaviors along with NPI scenarios, measuring how the simulation dynamics is affected and therefore, yielding rapid insights to perform a broad assessment of the potential of composite interventions at different stages of the epidemic. In this paper we describe a model and a tool to experiment with this kind of analysis, considering a number of widely-applied NPIs such as social distancing, case isolation, home quarantine, total lockdown, sentinel testing, mask wearing and a novel “zonal” enforcement requiring these interventions to be applied gradually to separated districts (zones). The choice of the most adequate interventions, or mixture of interventions, ultimately will depend on the socio-economic and health conditions of a particular territory and on further large-scale simulation and feasibility estimation of those scenarios yielding a potential mitigation impact, using the insights discovered with the simulation tool.


Author Biographies

Lindsay Álvarez Pomar , Universidad Distrital Francisco José de Caldas, Colombia

PhD. en Ingeniería, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá-Colombia,

Sergio Rojas Galeano*, Universidad Distrital Francisco José de Caldas, Colombia

PhD. en Ciencia Computacional, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá-Colombia,


S. Lai et al., “Effect of non-pharmaceutical interventions to contain COVID-19 in China,” nature, May. 2020.

N. M. Ferguson et al., “Report 9: impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand,” Imp. Coll. UK. Pp. 1-20, Mar. 2020.

N. M. Ferguson; D. A. T. Cummings; C. Fraser; J. C. Cajka; P. C. Cooley; D. S. Burke, “Strategies for mitigating an influenza pandemic,” Nature, vol. 442, no. 7101, pp. 448–452, Apr. 2006.

D. Fanelli; F. Piazza, “Analysis and forecast of COVID-19 spreading in China, Italy and France,” Chaos, Solitons & Fractals, vol. 134, pp. 109761, May. 2020.

N. Wilson; L. T. Barnard; A. Kvalsig; A. Verrall; M. G. Baker; M. Schwehm, “Modelling the potential health impact of the covid-19 pandemic on a hypothetical european country,” medRxiv, Mar. 2020.

M. Kantner; T. Koprucki, “Beyond just" flattening the curve": Optimal control of epidemics with purely non-pharmaceutical interventions,” arXiv Prepr. arXiv2004.09471, Jul. 2020.

G. Giordano et al., “Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy,” Nat. Med., vol. 26, pp. 855-860 Apr. 2020.

S. Ansumali; M. K. Prakash, “A Very Flat Peak: Why Standard SEIR Models Miss the Plateau of COVID-19 Infections and How it can be Corrected,” medRxiv, May. 2020.

W. Bock et al., “Mitigation and herd immunity strategy for COVID-19 is likely to fail,” medRxiv, May. 2020.

J. T. Tuomisto; J. Yrjölä; M. Kolehmainen; J. Bonsdorff; J. Pekkanen; T. Tikkanen, “An agent-based epidemic model REINA for COVID-19 to identify destructive policies,” medRxiv, pp. 1- 29, Apr. 2020.

J. Gomez; J. Prieto; E. Leon; A. Rodriguez, “INFEKTA: A General Agent-based Model for Transmission of Infectious Diseases: Studying the COVID-19 Propagation in Bogotá-Colombia,” medRxiv, pp. 1- 15, Apr. 2020.

S. F. Railsback; V. Grimm, Agent-based and individual-based modeling: a practical introduction. Princeton university press, 2011.

A. Ahmed; J. Greensmith; U. Aickelin, “Variance in system dynamics and agent-based modelling using the SIR model of infectious disease,” Proceedings of the 26th European Conference on Modelling and Simulation (ECMS), Koblenz, Germany, Oct. 2012, pp 9-15.

K. Lano; D. Clark, “Direct Semantics of Extended State Machines.,” J. Object Technol., vol. 6, no. 9, pp. 35–51, 2007.

M. Granada-Echeverri; A. Molina-Cabrera; P. Granada-Echeverri, “Spatio-temporal Projection of Covid-19 in Pereira,” TecnoLógicas., vol. 23, no. 49, Sept. 2020.

T. Obadia; R. Haneef; P.-Y. Boëlle, “The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks,” BMC Med. Inform. Decis. Mak., vol. 12, no. 1, pp. 147, Dec. 2012.

K. Dietz, “The estimation of the basic reproduction number for infectious diseases,” Stat. Methods Med. Res., vol. 2, no. 1, pp. 23–41, Mar. 1993.

R. Breban; R. Vardavas; S. Blower, “Theory versus data: how to calculate R0?,” PLoS One, vol. 2, no. 3, Mar. 2007.

S. T. Bakir, “Compound Interest Doubling Time Rule: Extensions and Examples from Antiquities,” Commun. Math. Financ., vol. 5, no. 2, pp. 1- 11, Sep. 2016.

U. Wilensky; W. Rand, An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Mit Press, 2015.

R. Verity et al., “Estimates of the severity of coronavirus disease 2019: a model-based analysis,” Lancet Infect. Dis., vol. 20, no. 6, pp. 669- 677, Jun. 2020.

How to Cite
L. Álvarez Pomar and S. Rojas-Galeano, “An agent-based tool for impact assessment of non-pharmaceutical interventions against COVID-19”, TecnoL., vol. 23, no. 49, pp. 201-221, Sep. 2020.


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