ARBO: Arbovirus modeling and uncertainty quantification toolbox.

Michel Tosin,Eber Dantas,Americo Cunha,Rebecca E Morrison

doi:10.1016/j.simpa.2022.100252

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.

Highlights

Arboviruses have been emerging in different parts of the world over the past several decades
Even though COVID-19 has of late dominated the agendas of global health institutions, the grave and harmful effects of arboviruses should not be neglected [3,4]
ARBO provides a toolbox for vector-borne dynamics on a host population, encompassing: (i) an initial value problem (IVP) solver; (ii) an inverse problem module for model calibration, potentially using real data; (iii) a model enrichment module which constructs a discrepancy operator to compensate for epistemic deficiencies in the structure of the mathematical model; and (iv) an uncertainty quantification module which propagates the aleatory uncertainties from the model parameters and initial conditions through the mathematical model to its response

Summary

Introduction

Arboviruses have been emerging in different parts of the world over the past several decades. After the connection between Zika infection and microcephaly in newborns was revealed, the World Health Organization declared the disease a global medical emergency in 2016. Explored in the literature, this kind of homogeneous model provides a good balance between interpretability and simplicity. In this vein, the ARBO package was developed to model and analyze the Zika virus outbreak that occurred in Brazil in 2016 [9], but can be adapted to guide studies in other regions and for other arboviruses such as Dengue or Chikungunya. The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.

Software details

Impact overview

Final remarks