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Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in Germany

Batista, Martín Lotto et al.
eBioMedicine, Volume 115, 105701

This study used spatiotemporal Bayesian modeling to analyze the impact of climate and environmental factors on Lyme borreliosis (LB) dynamics in Germany. The research found that precipitation, temperature, green spaces, and forest cover positively influenced LB incidence, while human population density had a negative impact. The study also identified increasing trends in LB risk, particularly in northern regions, and highlighted the role of climate in shaping LB distribution and transmission.

DOI: 10.1016/j.ebiom.2025.105701

Uncovering temperature sensitivity of West Nile virus transmission: Novel computational approaches to mosquito-pathogen trait responses

Heidecke et al. – PLOS Computational Biology, Volume 19, e1012866

This study introduces a computational framework to assess temperature sensitivity in mosquito-pathogen interactions, focusing on West Nile virus (WNV). Using experimental data across 15 mosquito species, the authors estimate temperature response functions for key traits influencing WNV transmission. They find an optimal transmission temperature around 24°C for Culex species and highlight key areas for future research to improve transmission models under climate change.

DOI: 10.1371/journal.pcbi.1012866

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies

Lowe R, Codeço CT.
Annu Rev Entomol. 2025 Jan;70(1):337-358.

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

DOI: 10.1146/annurev-ento-040124-015101