Rainfall as a Driver of Waterborne Disease: Ecohydrological Perspectives

The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented epidemiological relevance. For example, several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Equally, endemic waterborne (WB) disease spread shows unequivocal feedback with patterns of rainfall in space and time via the explanatory power of different modeling structures and formal model comparison (for deterministic and stochastic models basically referred to a common overarching approach of the type susceptible-infected-recovered-bacteria (SIRB).

This matter is not devoid of practical implications of social and economic importance. In fact, computational models of WB disease transmission provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. There exist, however, efforts of teams from academia, field research and humanitarian organizations to model in near real-time acute infections. One, of particular importance, is the Haitian cholera outbreak after Hurricane Matthew in October 2016, where joint efforts were directed at assessing risk and quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Projections, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort, about which a report will be given in this case, shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among meteorologists, modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak.

Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated. In brief, a whole field of applied research across meteorology, hydrology, ecology and epidemiology seems to be open to a coherent use of tools currently well-known and used in the single disciplines but rather rarely employed to exploit their possible synergies towards the reduction of the impact of water-related disease worldwide – a moral imperative.