Defining high-value information for COVID-19 decision-makingpreprint
Аннотация: Abstract Initial projections from the first generation of COVID-19 models focused public attention on worst-case scenarios in the absence of decisive policy action. These underscored the imperative for strong and immediate measures to slow the spread of infection. In the coming weeks, however, as policymakers continue enlisting models to inform decisions on COVID-19, answers to the most difficult and pressing policy questions will be much more sensitive to underlying uncertainties. In this study, we demonstrate a model-based approach to assessing the potential value of reducing critical uncertainties most salient to COVID-19 decision-making and discuss priorities for acquiring new data to reduce these uncertainties. We demonstrate how information about the impact of non-pharmaceutical interventions could narrow prediction intervals around hospitalizations over the next few weeks, while information about the prevalence of undetected cases could narrow prediction intervals around the timing and height of the peak of the epidemic.
Год издания: 2020
Авторы: Alyssa Bilinski, Ruthie Birger, Samantha Burn, Melanie H. Chitwood, Emma Clarke‐Deelder, Tyler Copple, Jeffrey W. Eaton, Hanna Y. Ehrlich, Margret Erlendsdottir, Soheil Eshghi, Monica Farid, Meagan C. Fitzpatrick, John Giardina, Gregg Gonsalves, Yuli Lily Hsieh, Suzan Iloglu, Yu-Han Kao, Evan MacKay, Nick Menzies, Bianca Mulaney, A. David Paltiel, Stephanie Perniciaro, Maile Phillips, Katherine M. Rich, Joshua A. Salomon, Raphael Sherak, Kayoko Shioda, Nicole A. Swartwood, Christian Testa, Thomas Thornhill, Elizabeth White, Anne Williamson, Anna York, Jinyi Zhu, Lin Zhu
Издательство: Cold Spring Harbor Laboratory
Источник: medRxiv (Cold Spring Harbor Laboratory)
Ключевые слова: COVID-19 epidemiological studies, Viral Infections and Outbreaks Research, Misinformation and Its Impacts
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