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AbstractInfectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases such as malaria and tuberculosis. Yet a single coding bug may bias results, leading to incorrect conclusions and wrong actions that could cause avoidable harm. We are ethically obliged to ensure our code is as free of error as possible. Unit testing is a coding method to avoid such bugs, but unit testing is rarely used in epidemiology. We demonstrate through simple examples how unit testing can handle the particular quirks of infectious disease models.

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