Quantitative analysis methods used to investigate the effects of weather and climate on infectious diseases

The potential impacts of climate change on infectious diseases are an increasingly important area of research. Climate change is likely to increase these risks associated with extreme temperatures and changing rainfall. We... [ view full abstract ]

The potential impacts of climate change on infectious diseases are an increasingly important area of research. Climate change is likely to increase these risks associated with extreme temperatures and changing rainfall. We performed a systematic review of statistical and modelling analysis methods available to investigate the effects of weather/climate on water-associated diseases. We considered English language papers in the main databases. Search terms included: water-associated disease, weather/climate indicators, and methods used. 201 papers met our inclusion criteria. The most commonly used quantitative analysis methods can be grouped into three main clusters: process-based models (PBM); empirical statistical models (ESM); and spatial analysis and methods based on geographic information systems (GIS). PBM are applied when the bio-physical mechanism of the pathogen under study is relatively known (e.g. cholera); ESM and GIS tend to be used when the roles of specific environmental drivers are unclear. The most common limitations were: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behaviour) in the methods, reporting bias, poor data quality, and correlation among predictor variables. Methodologies often do not distinguish among the multiple sources of time-lags (e.g. patient’s physiology, reporting mechanisms) between environmental exposures and disease onset. The review identified important limitations, missed opportunities and knowledge gaps in the analysis of climate change and infectious disease associations. Key areas of future research include disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), linking weather data to individual cases longitudinally and distinguishing multiple sources of exposures at different time-lags.