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Background Neglected tropical diseases are responsible for considerable morbidity and mortality in low-income populations. International efforts have reduced their global burden, but transmission is persistent and case-finding-based interventions rarely target asymptomatic individuals. Methods We develop a generic mathematical modeling framework for analyzing the dynamics of visceral leishmaniasis in the Indian sub-continent (VL), gambiense sleeping sickness (gHAT), and Chagas disease and use it to assess the possible contribution of asymptomatics who later develop disease (pre-symptomatics) and those who do not (non-symptomatics) to the maintenance of infection. Plausible interventions, including active screening, vector control, and reduced time to detection, are simulated for the three diseases. Results We found that the high asymptomatic contribution to transmission for Chagas and gHAT and the apparently high basic reproductive number of VL may undermine long-term control. However, the ability to treat some asymptomatics for Chagas and gHAT should make them more controllable, albeit over relatively long time periods due to the slow dynamics of these diseases. For VL, the toxicity of available therapeutics means the asymptomatic population cannot currently be treated, but combining treatment of symptomatics and vector control could yield a quick reduction in transmission. Conclusions Despite the uncertainty in natural history, it appears there is already a relatively good toolbox of interventions to eliminate gHAT, and it is likely that Chagas will need improvements to diagnostics and their use to better target pre-symptomatics. The situation for VL is less clear, and model predictions could be improved by additional empirical data. However, interventions may have to improve to successfully eliminate this disease. [ABSTRACT FROM AUTHOR]

The seasonality of cholera and its spatial variability remain unexplained. Uncovering the role of environmental drivers in these seasonal patterns is critical to understand temporal variability at longer time scales, including trends and interannual variability. Rainfall has been proposed as a key driver of the seasonality of cholera. To address this hypothesis, we examine the association between rainfall and cholera in both time and space using the extensive historical records for the districts of Madras in former British India (1901–1940). We show the existence of two main spatial clusters that differ not just in the effect of rainfall but also in the seasonal pattern and frequency of periods with and without cholera mortality. The results support a model of cholera seasonality with two different routes of transmission: one is enhanced by increasing rainfall (in areas with abundant water), the other is buffered by increasing water. We discuss how the dual nature of the influence of rainfall creates different temporal patterns in regions where cholera is either “endemic” or “epidemic.” [ABSTRACT FROM AUTHOR]

Cholera dynamics in endemic regions display regular seasonal cycles and pronounced interannual variability. We review here the current quantitative evidence for the influence of climate on cholera dynamics with reference to the early literature on the subject. We also briefly review the incipient status of mathematical models for cholera and argue that these models are important for understanding climatic influences in the context of the population dynamics of the disease. A better understanding of disease risk related to the environment should further underscore the need for changing the socioeconomic conditions conducive to cholera.