Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Cramer, EYORCID logo; Ray, ELORCID logo; Lopez, VKORCID logo; Bracher, JORCID logo; Brennen, A; Castro Rivadeneira, AJ; Gerding, A; Gneiting, TORCID logo; House, KH; Huang, Y; +285 more...Jayawardena, D; Kanji, AH; Khandelwal, A; Le, K; Mühlemann, A; Niemi, JORCID logo; Shah, A; Stark, A; Wang, Y; Wattanachit, N; Zorn, MW; Gu, Y; Jain, S; Bannur, N; Deva, A; Kulkarni, M; Merugu, S; Raval, A; Shingi, S; Tiwari, A; White, JORCID logo; Abernethy, NF; Woody, SORCID logo; Dahan, M; Fox, SORCID logo; Gaither, KORCID logo; Lachmann, M; Meyers, LAORCID logo; Scott, JG; Tec, MORCID logo; Srivastava, A; George, GEORCID logo; Cegan, JCORCID logo; Dettwiller, ID; England, WP; Farthing, MW; Hunter, RHORCID logo; Lafferty, BORCID logo; Linkov, I; Mayo, MLORCID logo; Parno, MD; Rowland, MAORCID logo; Trump, BD; Zhang-James, Y; Chen, SORCID logo; Faraone, SV; Hess, J; Morley, CP; Salekin, AORCID logo; Wang, D; Corsetti, SMORCID logo; Baer, TM; Eisenberg, MC; Falb, KORCID logo; Huang, YORCID logo; Martin, ET; McCauley, E; Myers, RL; Schwarz, T; Sheldon, DORCID logo; Gibson, GC; Yu, R; Gao, L; Ma, Y; Wu, D; Yan, X; Jin, X; Wang, Y; Chen, Y; Guo, LORCID logo; Zhao, Y; Gu, QORCID logo; Chen, J; Wang, L; Xu, PORCID logo; Zhang, W; Zou, D; Biegel, H; Lega, JORCID logo; McConnell, SORCID logo; Nagraj, V; Guertin, SL; Hulme-Lowe, C; Turner, SDORCID logo; Shi, YORCID logo; Ban, X; Walraven, RORCID logo; Hong, Q; Kong, S; van de Walle, AORCID logo; Turtle, JAORCID logo; Ben-Nun, MORCID logo; Riley, SORCID logo; Riley, P; Koyluoglu, UORCID logo; DesRoches, D; Forli, P; Hamory, B; Kyriakides, C; Leis, H; Milliken, J; Moloney, M; Morgan, J; Nirgudkar, N; Ozcan, G; Piwonka, N; Ravi, M; Schrader, C; Shakhnovich, E; Siegel, D; Spatz, R; Stiefeling, C; Wilkinson, B; Wong, A; Cavany, SORCID logo; España, GORCID logo; Moore, SORCID logo; Oidtman, RORCID logo; Perkins, AORCID logo; Kraus, DORCID logo; Kraus, A; Gao, Z; Bian, J; Cao, WORCID logo; Lavista Ferres, JORCID logo; Li, C; Liu, T; Xie, X; Zhang, S; Zheng, S; Vespignani, AORCID logo; Chinazzi, M; Davis, JT; Mu, K; Pastore Y Piontti, A; Xiong, X; Zheng, A; Baek, J; Farias, V; Georgescu, A; Levi, R; Sinha, DORCID logo; Wilde, J; Perakis, GORCID logo; Bennouna, MAORCID logo; Nze-Ndong, D; Singhvi, D; Spantidakis, IORCID logo; Thayaparan, L; Tsiourvas, AORCID logo; Sarker, AORCID logo; Jadbabaie, AORCID logo; Shah, DORCID logo; Della Penna, N; Celi, LAORCID logo; Sundar, S; Wolfinger, R; Osthus, DORCID logo; Castro, L; Fairchild, GORCID logo; Michaud, I; Karlen, D; Kinsey, M; Mullany, LCORCID logo; Rainwater-Lovett, KORCID logo; Shin, L; Tallaksen, K; Wilson, S; Lee, ECORCID logo; Dent, JORCID logo; Grantz, KH; Hill, ALORCID logo; Kaminsky, J; Kaminsky, K; Keegan, LTORCID logo; Lauer, SA; Lemaitre, JCORCID logo; Lessler, J; Meredith, HR; Perez-Saez, J; Shah, S; Smith, CP; Truelove, SAORCID logo; Wills, JORCID logo; Marshall, M; Gardner, L; Nixon, K; Burant, JC; Wang, L; Gao, LORCID logo; Gu, ZORCID logo; Kim, M; Li, X; Wang, G; Wang, Y; Yu, SORCID logo; Reiner, RC; Barber, R; Gakidou, E; Hay, SIORCID logo; Lim, S; Murray, CORCID logo; Pigott, D; Gurung, HL; Baccam, P; Stage, SAORCID logo; Suchoski, BT; Prakash, BAORCID logo; Adhikari, B; Cui, J; Rodríguez, AORCID logo; Tabassum, A; Xie, JORCID logo; Keskinocak, PORCID logo; Asplund, J; Baxter, AORCID logo; Oruc, BEORCID logo; Serban, N; Arik, SO; Dusenberry, M; Epshteyn, A; Kanal, E; Le, LT; Li, C; Pfister, T; Sava, D; Sinha, RORCID logo; Tsai, T; Yoder, NORCID logo; Yoon, J; Zhang, LORCID logo; Abbott, S; Bosse, NI; Funk, SORCID logo; Hellewell, J; Meakin, SRORCID logo; Sherratt, KORCID logo; Zhou, M; Kalantari, R; Yamana, TKORCID logo; Pei, SORCID logo; Shaman, JORCID logo; Li, MLORCID logo; Bertsimas, DORCID logo; Skali Lami, OORCID logo; Soni, SORCID logo; Tazi Bouardi, HORCID logo; Ayer, T; Adee, M; Chhatwal, J; Dalgic, OO; Ladd, MA; Linas, BP; Mueller, P; Xiao, J; Wang, YORCID logo; Wang, Q; Xie, S; Zeng, D; Green, A; Bien, J; Brooks, L; Hu, AJ; Jahja, M; McDonald, DORCID logo; Narasimhan, B; Politsch, CORCID logo; Rajanala, SORCID logo; Rumack, AORCID logo; Simon, N; Tibshirani, RJORCID logo; Tibshirani, R; Ventura, V; Wasserman, L; O'Dea, EB; Drake, JMORCID logo; Pagano, R; Tran, QT; Ho, LSTORCID logo; Huynh, H; Walker, JW; Slayton, RBORCID logo; Johansson, MAORCID logo; Biggerstaff, MORCID logo; Reich, NGORCID logo and (2022) Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119 (15). e2113561119-. ISSN 0027-8424 DOI: 10.1073/pnas.2113561119
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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.


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