Forecasting efforts from prior epidemics and COVID-19 predictions
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Forecasting efforts from prior epidemics and COVID‑19 predictions Pranay Nadella1 · Akshay Swaminathan2 · S. V. Subramanian3,4 Received: 30 May 2020 / Accepted: 1 July 2020 © Springer Nature B.V. 2020
Abstract Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action. Keywords Forecasting · Predictions · Pandemics · Ebola · COVID-19 Predictions of future cases, hospitalizations, and deaths have dominated the public discourse around COVID-19. Experts forecasted 20 to 60% of the world becoming infected and up to 2.2 million American deaths if the pandemic carries on unmitigated [1, 2]. Similarly, researchers predicted 510,000 British deaths and at least 300 million Indian cases [2, 3]. Many of these forecasts may not come true, so how can we use them effectively to inform policy? Similar prediction efforts undertaken by researchers during past epidemics can lend clarity to this question. For example, prominent experts forecasted up to 200 million Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10654-020-00661-0) contains supplementary material, which is available to authorized users. * S. V. Subramanian [email protected] Pranay Nadella [email protected] Akshay Swaminathan [email protected] 1
Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
2
Quantitative Sciences, Flatiron Health, New York, NY, USA
3
Center for Population and Development Studies, Harvard University, Cambridge, MA, USA
4
Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA
and 50,000 global deaths for H5N1 and mad cow disease respectively [4, 5]. However, these were drastic overpredictions, as only 455 and 177 deaths ensued [6, 7]. To systematically evaluate the success of prior forecasting models, we reviewed predictions from three twenty-first century epidemics: the 2002–2004 Severe acute respiratory syndrome (SARS) outbreak, the 2009 H1N1 influenza pandemic, and the 2014 Ebola virus disease outbreak. We found that during the SARS and H1N1 outbreaks, only a few studies attempted to predict future cases and were ultimately unsuccessful. During the Ebola epidemic, the number of forecasting studies increased dramatically, and mo
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