ARBO: Arbovirus modeling and uncertainty quantification toolbox

Michel Tosin (1) , Eber Dantas (2) , Americo Cunha Jr (1) , Rebecca Morrison (3)

1 UERJ - Universidade do Estado do Rio de Janeiro [Brasil] = Rio de Janeiro State University [Brazil] = Université d'État de Rio de Janeiro [Brésil]
2 UFRJ - Universidade Federal do Rio de Janeiro [Brasil] = Federal University of Rio de Janeiro [Brazil] = Université fédérale de Rio de Janeiro [Brésil]
3 University of Colorado [Boulder]

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.

Domaines: Systèmes dynamiques [math.DS]; Probabilités [math.PR]; Statistiques [math.ST]; Modélisation et simulation; Machine Learning [stat.ML]; Science non linéaire [physics]; Santé publique et épidémiologie; Maladies infectieuses; Maladies émergentes

Michel Tosin, Eber Dantas, Americo Cunha Jr, Rebecca Morrison. ARBO: Arbovirus modeling and uncertainty quantification toolbox. Software Impacts, 2022, 12, pp.100252. ⟨10.1016/j.simpa.2022.100252⟩. ⟨hal-03595854⟩