•Accurate prevalence estimation is crucial for preventing and mitigating emerging and seasonal diseases.•Optimal pooled testing design can lead to efficient prevalence estimation, saving testing costs and improving estimation accuracy.•When prior information on the disease of interest is unreliable or unavailable, robust optimal pooled testing designs can significantly improve estimation accuracy, in comparison to deterministic optimal pooled testing designs. Accurate estimation of disease prevalence is essential for mitigation efforts. Due to limited testing resources, prevalence estimation is often conducted via pooled testing, in which multiple specimens are combined and tested via a single test. The pool design, i.e., the number and sizes of testing pools, has a substantial impact on estimation accuracy. Determining an optimal pool design is challenging, especially for emerging or seasonal diseases for which information on the status of the disease is unreliable or unavailable prior to testing. We develop novel optimization models for testing pool design under uncertainty and limited resources, and characterize structural properties of optimal pool designs. We apply our models to estimate the prevalence of West Nile virus in mosquitoes (the main vector of transmission to humans). Our findings suggest that estimation accuracy can be substantially improved over the status quo through the proposed optimal pool designs.