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Mathematical models of cholera and waterborne disease vary widely in theirstructures, in terms of transmission pathways, loss of immunity, and otherfeatures. These differences may yield different predictions and parameterestimates from the same data. Given the increasing use of models to informpublic health decision-making, it is important to assess distinguishability(whether models can be distinguished based on fit to data) and inferencerobustness (whether model inferences are robust to realistic variations inmodel structure). We examined the effects of uncertainty in model structure inepidemic cholera, testing a range of models based on known features of choleraepidemiology. We fit to simulated epidemic and long-term data, as well as datafrom the 2006 Angola epidemic. We evaluated model distinguishability based ondata fit, and whether parameter values and forecasts can accurately be inferredfrom incidence data. In general, all models were able to successfully fit toall data sets, even if misspecified. However, in the long-term data, the bestmodel fits were achieved when the loss of immunity form matched those of themodel that simulated the data. Two transmission and reporting parameters wereaccurately estimated across all models, while the remaining showed broadvariation across the different models and data sets. Forecasting efforts werenot successful early, but once the epidemic peak had been achieved, most modelsshowed similar accuracy. Our results suggest that we are unlikely to be able toinfer mechanistic details from epidemic case data alone, underscoring the needfor broader data collection. Nonetheless, with sufficient data, conclusionsfrom forecasting and some parameter estimates were robust to variations in themodel structure, and comparative modeling can help determine how variations inmodel structure affect conclusions drawn from models and data.

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity