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In an acute infectious disease outbreak, finding infectives is a necessary precursor to treatment administration and exposure mitigation. Enhanced surveillance (ES), i.e., expanding random testing above baseline, and contact-tracing (CT) are 2 prominent case-finding policies. Once an index case is found, CT involves testing their contacts and treating the infected. Evaluating their empirical effectiveness requires careful model calibration and cost-assessment of dynamic counterfactuals.
We use an epidemic model to show that the optimal dynamic coordinated case-finding policy that balances disease burden and policy-related expenditure only activates CT at maximal rate in a subset of the ES window, leading to a policy with up to 5 phases.
We use prevalence and contact-tracing data from the 2014-16 EVD outbreak in Liberia to build assumption-free estimators of the empirical case-finding policy and the optimal (counterfactual) one. We estimate infection and response costs using standard-of-care, wage, and disease progression data.
We find that the best such policy would have increased the ES window while curtailing CT. This finding is robust and matches on-the-ground observations.
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