Timely epidemic monitoring in the presence of reporting delays: anticipating the COVID-19 surge in New York City, September 2020.

BACKGROUND: During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response. METHODS: We describe a nonparametric statistical method, originally applied to the reporting of AIDS cases in the 1980s, to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City during the late summer and early fall of 2020. RESULTS: During August 15-September 26, the estimated mean delay in reporting was 3.3 days, with 87% of cases reported by 5 days from diagnosis. Relying upon the estimated reporting-delay distribution, we projected COVID-19 incidence during the most recent 3 weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. Applying our delay-corrected estimates to case counts reported as of September 26, we projected a surge in new diagnoses that had already occurred but had yet to be reported. Our projections were consistent with counts of confirmed cases subsequently reported by November 7. CONCLUSION: The projected estimate of recently diagnosed cases could have had an impact on timely policy decisions to tighten social distancing measures. While the recent advent of widespread rapid antigen testing has changed the diagnostic testing landscape considerably, delays in public reporting of SARS-CoV-2 case counts remain an important barrier to effective public health policy.

Information and behavioral responses during a pandemic: Evidence from delays in Covid-19 death reports.

Providing information is important for managing epidemics, but issues with data accuracy may hinder its effectiveness. Focusing on Covid-19 in Mexico, we ask whether delays in death reports affect individuals' beliefs and behavior. Exploiting administrative data and an online survey, we provide evidence that behavior, and consequently the evolution of the pandemic, are considerably different when death counts are presented by date reported rather than by date occurred, due to non-negligible reporting delays. We then use an equilibrium model incorporating an endogenous behavioral response to illustrate how reporting delays lead to slower individual responses, and consequently, worse epidemic outcomes.