![]() ![]() patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Furthermore, the methods often did not distinguish among the multiple sources of time-lags ( e. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. The most common limitations comprised: non-inclusion of key factors ( e. Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. Vibrio cholerae) TS-SE tended to be used when the specific environmental mechanisms were unclear ( e. In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known ( e. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). We found 102 full text papers that met our criteria and were included in the analysis. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. ![]() There have been little critical analyses of the methodological approaches. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Infectious diseases attributable to unsafe water supply, sanitation and hygiene ( e. ![]()
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