Large-scale Epidemiological modeling: Scanning for Mosquito-Borne Diseases Spatio-temporal Patterns in Brazil

TL;DR

This study introduces the Episcanner pipeline to analyze long-term spatio-temporal patterns of dengue and chikungunya across Brazil, revealing climate influences on epidemic dynamics and providing a comprehensive dataset for public health insights.

Contribution

It presents a novel computational pipeline and dataset for estimating transmission parameters across Brazil, enabling detailed analysis of climate-driven disease patterns over 14 years.

Findings

Identified distinct geographical and temporal patterns of disease incidence.

Demonstrated climate factors like temperature and precipitation influence epidemic timing.

Provided a reusable dataset for future epidemiological research.

Abstract

The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offers a new perspective on the long-term dynamics of climate-driven mosquito-borne diseases and their geographical specificities linked to the effects of global temperature fluctuations such as those captured by the ENSO index.