Rachel E. Baker, Ayesha S. Mahmud, Caroline E. Wagner, Wenchang Yang, Virginia E. Pitzer, Cecile Viboud, Gabriel A. Vecchi, C. Jessica E. Metcalf, and Bryan T. Grenfell
The impact of climate on viral epidemiology has been documented in the past, particularly in the field of influenza, however it has remained poorly understood in RSV, and a robust model for the influence of factors such as climate on epidemiology has remained elusive. This study aims to address this need and model the effect of climate on yearly RSV epidemics. This study leverages a data set containing weekly bronchiolitis observations from over 300 sites across diverse climates in the United States and Mexico, alongside high resolution climate data for these same locations.
The most prominent finding of this study is the effect of humidity and rainfall on time of outbreaks, with humidity in more tropical regions accounting for around 52% of the observed variability in the dataset with lower humidity coinciding with outbreaks. In regions with more biennial outbreaks, precipitation is the most predictive climate factor in determining the timing of outbreaks.
This study also aimed to model future epidemics based on model of future climate development, and predict that increasing humidity across tropical regions and bordering temperate regions may result in less seasonality and more consistent year round transmission. There was more difficulty in modelling future changes in precipitation.
Statistical modelling to both understand factors influencing yearly epidemics and to predict those epidemics are a clear next step for epidemiology, and this study provides useful frameworks that can be applied to other diseases or iterated upon.
Full article on PubMed.