Challenges of COVID-19 Case Forecasting in the US, 2020–2021
Authors: Lopez Velma K. , Cramer Estee Y. , Pagano Robert , Drake John M. , O’Dea Eamon B. , Adee Madeline , Ayer Turgay , Chhatwal Jagpreet , Dalgic Ozden O. , Ladd Mary A. , Linas Benjamin P. , Mueller Peter P. , Xiao Jade , Bracher Johannes , Castro Rivadeneira Alvaro J. , Gerding Aaron , Gneiting Tilmann , Huang Yuxin , Jayawardena Dasuni , Kanji Abdul H. , Le Khoa , Mühlemann Anja , Niemi Jarad , Ray Evan L. , Stark Ariane , Wang Yijin , Wattanachit Nutcha , Zorn Martha W. , Pei Sen , Shaman Jeffrey , Yamana Teresa K. , Tarasewicz Samuel R. , Wilson Daniel J. , Baccam Sid , Gurung Heidi , Stage Steve , Suchoski Brad , Gao Lei , Gu Zhiling , Kim Myungjin , Li Xinyi , Wang Guannan , Wang Lily , Wang Yueying , Yu Shan , Gardner Lauren , Jindal Sonia , Marshall Maximilian , Nixon Kristen , Dent Juan , Hill Alison L. , Kaminsky Joshua , Lee Elizabeth C. , Lemaitre Joseph C. , Lessler Justin , Smith Claire P. , Truelove Shaun , Kinsey Matt , Mullany Luke C. , Rainwater-Lovett Kaitlin , Shin Lauren , Tallaksen Katharine , Wilson Shelby , Karlen Dean , Castro Lauren , Fairchild Geoffrey , Michaud Isaac , Osthus Dave , Bian Jiang , Cao Wei , Gao Zhifeng , Lavista Ferres Juan , Li Chaozhuo , Liu Tie-Yan , Xie Xing , Zhang Shun , Zheng Shun , Chinazzi Matteo , Davis Jessica T. , Mu Kunpeng , Pastore y Piontti Ana , Vespignani Alessandro , Xiong Xinyue , Walraven Robert , Chen Jinghui , Gu Quanquan , Wang Lingxiao , Xu Pan , Zhang Weitong , Zou Difan , Gibson Graham Casey , Sheldon Daniel , Srivastava Ajitesh , Adiga Aniruddha , Hurt Benjamin , Kaur Gursharn , Lewis Bryan , Marathe Madhav , Peddireddy Akhil Sai , Porebski Przemyslaw , Venkatramanan Srinivasan , Wang Lijing , Prasad Pragati V. , Walker Jo W. , Webber Alexander E. , Slayton Rachel B. , Biggerstaff Matthew , Reich Nicholas G. , Johansson Michael A. 
Published: May 2024

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.