Commentary on the role of statisticians in pandemics

Abstract

I want to express my appreciation for the thoughtful article by Susan Ellenberg and John Morris that compares and contrasts aspects of HIV/AIDS and COVID-19 and the roles that statisticians are playing in addressing the challenges of these pandemics. 1 Public health emergencies require rapid response. Statistical contributions can profoundly inform public policy in health emergencies but only if the contributions are timely. The speed at which the COVID-19 crisis has unfolded has been staggering. The United States surpassed a cumulative COVID-19 death toll of 400 000 within 11 months of the first reported U.S. COVID case. In contrast, the HIV/AIDS cumulative death toll in the United States reached the 400 000 mark more than 16 years after the first AIDS reported case. Statisticians have risen to the enormous challenges presented by the COVID-19 pandemic and are making critical contributions on many fronts.One area that statisticians are contributing is making sense of the myriad of models that try to forecast where the COVID-19 pandemic is heading. All too often COVID-19 model predictions that have appeared both in journals and the popular press are at great odds with each other. 2 Similarly, in the early days of HIV/AIDS, there were widely varying model predictions. For example, one of the first projections of the HIV/AIDS used the normal density to extrapolate AIDS incidence trends. 3 The extrapolation produced the anomalous prediction of rapidly decreasing AIDS cases despite the fact that all the data showed rising AIDS cases and concluded that the projected total size of the AIDS epidemic in the United States would be about 200 000 cases, a number that subsequently was discredited. The authors of that fallacious report cited William Farr and his work with smallpox in the 1830s for justification. Farr's curves for smallpox epidemics looked like normal curves and the normal density was used only by analogy. But of course, smallpox in the 1800s was neither AIDS in the 1990s nor COVID-19 in 2020. One of the earliest prediction models for COVID-19, also based on empirical extrapolation of curves, forecast that COVID-19 deaths in the United States would drop to near zero by summer 2020, 4 a prediction that has also been discredited. 5 Lessons learned from both the COVID-19 and HIV/AIDS pandemics are that extrapolation of empirical trend curves, regardless how complicated the functional forms, can lead to very misleading results. Extrapolating COVID-19 death curves is especially perilous because of the relatively short time period between infection and death, and as such, death counts can take sharp abrupt turns just weeks after occurrence of events that are somewhat unpredictable such as community lockdowns, super-spreader events, and high travel days.One parallel between COVID-19 and HIV/AIDS is that case surveillance data is only measuring the tip of the iceberg of the infected population. Statistical approaches are needed to estimate the size of the "hidden population" which refers to asymptomatic infected perso...

Statistics in Times of Increasing Uncertainty

“…For the SARS‐CoV‐2 pandemic, the time axis is hugely compressed creating specific expectations on the delivery of statistical analyses. As pointed out by Brookmeyer (2021), the United States surpassed a cumulative Covid‐19 death toll of 400,000 within 11 months of the first reported US Covid case, while it took more than 16 years for the US cumulative death toll from AIDS to reach 400,000 from the first reported AIDS case.…”