ARBO: Arbovirus modeling and uncertainty quantification toolbox

Michel Tosin; Eber Dantas; Americo Cunha Jr; Rebecca E Morrison

doi:10.1016/j.simpa.2022.100252

ARBO: Arbovirus modeling and uncertainty quantification toolbox

Softw Impacts. 2022 May:12:100252. doi: 10.1016/j.simpa.2022.100252. Epub 2022 Feb 16.

Authors

Michel Tosin¹, Eber Dantas², Americo Cunha Jr¹, Rebecca E Morrison³

Affiliations

¹ Rio de Janeiro State University, Rio de Janeiro, Brazil.
² Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
³ University of Colorado Boulder, Boulder, USA.

Abstract

The ongoing pandemic of COVID-19 has highlighted the importance of mathematical tools to understand and predict outbreaks of severe infectious diseases, including arboviruses such as Zika. To this end, we introduce ARBO, a package for simulation and analysis of arbovirus nonlinear dynamics. The implementation follows a minimalist style, and is intuitive and extensible to many settings of vector-borne disease outbreaks. This paper outlines the main tools that compose ARBO, discusses how recent research works about the Brazilian Zika outbreak have explored the package's capabilities, and describes its potential impact for future works on mathematical epidemiology.

Keywords: Arbovirus; Mathematical epidemiology; Model calibration; Model discrepancy; Uncertainty quantification.