Assessing Dengue Risk Globally Using Non-Markovian Models

TL;DR

This paper introduces a novel non-Markovian mechanistic model for the Aedes mosquito life cycle, integrating temperature and precipitation data to create a global dengue risk map, improving outbreak prediction accuracy.

Contribution

It advances existing models by accurately capturing the non-Markovian dynamics of mosquito development and provides a framework for global dengue risk assessment.

Findings

Developed a non-Markovian model fitted to dengue case data

Created a global spatiotemporal dengue risk map

Demonstrated reduction to differential equations for analysis

Abstract

Dengue is a vector-borne disease transmitted by Aedes mosquitoes. The worldwide spread of these mosquitoes and the increasing disease burden have emphasized the need for a spatio-temporal risk map capable of assessing dengue outbreak conditions and quantifying the outbreak risk. Given that the life cycle of Aedes mosquitoes is strongly influenced by habitat temperature, numerous studies have utilized temperature-dependent development rates of these mosquitoes to construct virus transmission and outbreak risk models. In this study, we advance existing research by developing a mechanistic model for the mosquito life cycle that accurately accounts for the non-Markovian nature of the process. By fitting the model to data on human dengue cases, we estimate several model parameters, allowing the development of a global spatiotemporal dengue risk map. This risk model employs temperature and precipitation data to assess the environmental suitability for dengue outbreaks in a given area. Furthermore, we demonstrate how to reduce the model to the corresponding differential equations, enabling us to utilize existing methods for analyzing the system and fitting the model to observations. This approach can be further applied to similar non-Markovian processes that are currently described with less accurate Markovian models.