Incorporating underreporting of epidemiological burden in COVID-19 models: a targeted literature review

Underreporting of infections, hospitalizations, and deaths can pose challenges to accurately estimating the true burden of COVID-19. Consequently, health burden assessments and economic evaluations may underestimate the public health impact of interventions such as vaccination. This targeted literature review summarized economic evaluations of COVID-19 that reported having adjusted for underreporting of epidemiological burden. Searches were performed in PubMed through 08/31/2025 with no geographic restrictions. Key study characteristics extracted: country, time period, population, parameters adjusted for underreporting, and the adjustment multipliers used. A high-level quality assessment of evidence was conducted, building on Drummond checklist and CHEERS. Given the qualitative nature of the question and the expected heterogeneity in study designs, the results were summarized qualitatively. A total of 20 studies met the inclusion criteria. Of these, 14 (70%) reported numerical adjustment factors, and the remaining 30% did not report a numerical factor. The studies covered diverse geographic regions and time frames, with adjustments applied to parameters such as infections, hospitalizations, and mortality. The study quality was moderate to high. The multipliers used ranged widely across studies: 1 to 5 for mortality, 1 to 5 for hospitalizations, and 1 to 10 for infections, where a value higher than 1.0 reflects an adjustment factor for underreporting. The methodologies used to estimate underreporting varied, including comparisons to excess mortality data, Monte Carlo simulations, and validation against external datasets. Most studies used pandemic time horizons. This review identified 14 modelling studies reporting numerical adjustment factors. The studies used diverse approaches and adjustment factors, reflecting variability in data availability and estimation methods. Recognizing and standardizing these adjustments is crucial for improving the accuracy and comparability of health economic analyses that inform policy decisions. Further research could refine underreporting estimates and assess their impact on economic model outcomes.