Assessing dengue risk globally using non-Markovian models

Authors: VAJDI, ARAM - Kansas State University; Cohnstaedt, Lee; SCOGLIO, CATERINA - Kansas State University

Submitted to: Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/27/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Dengue is a vector-borne disease transmitted by Aedes genera mosquitoes. The worldwide spread of these mosquitoes and the increasing disease burden have emphasized the necessity for risk maps that demonstrate where people are most likely to get dengue. Using the impact of temperature and rainfall on the life cycle or development rate of the mosquito that transmits the virus timing of outbreaks can be predicted. This is a new method for modeling these curves. This mechanistic model is more accurate and can account for smaller changes than existing models because it more closely describes the biology rather than approximating it. The method is evaluated in several locations globally.

Technical 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 necessity for a spatio-temporal risk map capable of assessing the environmental suitability for dengue outbreaks. Given that the life cycle of Aedes mosquitoes is strongly influenced by habitat temperature, numerous studies have utilized temperature-dependent entomological parameters 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. 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. 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.