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Escudie, J.-B.

Publications and source records attributed to Escudie, J.-B..

2 recordsLinked to original sources

The temperature sensitivity of arboviral disease extrinsic incubation periods: a systematic review

BackgroundArboviral diseases are an increasing global public health concern, driven by both human and environmental factors. A key parameter shaping transmission is the extrinsic incubation period (EIP)--the time between a mosquito acquiring a virus and becoming infectious--which is strongly influenced by temperature. However, the temperature-EIP relationship remains poorly characterized across arboviruses and mosquito species. MethodsWe conducted the first systematic review of laboratory studies evaluating temperature effects on EIP-related outcomes across all reported mosquito-arbovirus pairings. We searched three databases and extracted data on transmission efficiency (TE), applying linear regression models adjusted for diurnal temperature range and viral dose. Studies where TE could not be extracted were synthesized narratively. ResultsOur synthesis included 60 studies covering 17 arboviral diseases and 20 mosquito species. We found substantial heterogeneity in temperature effects on TE. While CHIKV, ZIKV, DENV, and WNV generally showed increased TE and shorter EIP at higher temperatures, many viruses--such as SINV, USUV, and BATV--exhibited no clear trends, often due to limited data and small sample sizes. Even across different vectors of the same virus, findings varied widely, reflecting both biological differences and inconsistent experimental designs. ConclusionsThese findings reveal major gaps in our understanding of climate-sensitive arbovirus transmission. Standardization of vector competence experiments and expanded research on neglected viruses are urgently needed. Environmental cofactors beyond temperature--such as humidity and variability--should also be incorporated to improve modelling and support climate-resilient intervention strategies. Author summaryThe burden of disease caused by arboviruses is increasing globally, largely driven by rapid urbanization, climate change, and global mobility. The transmission dynamics of arboviruses are modulated by a parameter known as the extrinsic incubation period, which is driven by environmental factors, especially temperature. However, the extent and direction of this effect has not been well characterized for all viruses, as data is limited and has not been synthesized for many neglected tropical diseases. Our study is the first systematic review to examine the existing evidence of laboratory studies across all studied mosquito species and virus pairings and to provide a comprehensive dataset for future modelling studies. We find that there is significant heterogeneity, not only between viruses, but also within individual vector-virus combinations, and that many arboviruses show no temperature effect. Based on these results, we propose that existing research efforts be standardized, and further research is conducted on emerging and re-emerging arboviruses for better intervention strategies and modelling predictions.

microbiology↗

SIMPLICITY: an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.

Computational tools are frequently used to describe pathogen evolutionary dynamics either within infected hosts or at the population level. However, there is a lack of models that capture the complex interplay between within-host and between-host evolutionary dynamics, leaving a knowledge gap with regard to realistic evolutionary dynamics as observed within molecular surveillance programs. We present SIMPLICITY, a multi-scale mathematical model that combines within-host disease progression and viral evolution, with a population-level model of virus transmission and immune evasion. We parameterize SIMPLICITY based on SARS-CoV-2 within-host viral dynamics, observed evolutionary rates, as well as dynamics of immune waning. We then apply SIMPLICITY to study the dynamics and mechanisms driving SARS-CoV-2 evolution at the population level. We compare a model of gradually increasing transmission fitness with an adaptive fitness landscape model that accounts for temporal changes in transmission fitness due to infection history and immune waning in the population. Our simulations demonstrate that escape from population immunity generates evolutionary dynamics encompassing selective sweeps, which resembles actual SARS-CoV-2 evolution. To the contrary, the model of gradual fitness fails to resemble realistic SARS-CoV-2 evolutionary dynamics. Overall, this demonstrates that infection history and immune waning should be considered in models of viral fitness, including machine learning approaches. In short, SIMPLICITY can be used to investigate mechanisms driving viral evolution and constitutes a versatile tool for creating synthetic datasets of viral evolution to e.g. benchmark and challenge phylogenetic pipelines used in outbreak investigation.

systems biology↗