Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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CRAMER, EY, EL RAY, VK LOPEZ, J. BRACHER, A. BRENNEN, AJC RIVADENEIRA, A. GERDING, T. GNEITING, KH HOUSE, YX HUANG, D. JAYAWARDENA, AH KANJI, A. KHANDELWAL, K. LE, A. MUHLEMANN, J. NIEMI, A. SHAH, A. STARK, YJ WANG, N. WATTANACHIT, MW ZORN, Gu YY, S. JAIN, N. BANNUR, A. DEVA, M. KULKARNI, S. MERUGU, A. RAVAL, S. SHINGI, A. TIWARI, J. WHITE, NF ABERNETHY, S. WOODY, M. DAHAN, S. FOX, K. GAITHER, M. LACHMANN, LA MEYERS, JG SCOTT, M. TEC, A. SRIVASTAVA, GE GEORGE, JC CEGAN, ID DETTWILLER, WP ENGLAND, MW FARTHING, RH HUNTER, B. LAFFERTY, I. LINKOV, ML MAYO, MD PARNO, MA ROWLAND, BD TRUMP, Y. ZHANG-JAMES, S. CHEN, SV FARAONE, J. HESS, CP MORLEY, A. SALEKIN, DL WANG, SM CORSETTI, TM BAER, MC EISENBERG, K. FALB, YT HUANG, Martin ET, E. MCCAULEY, RL MYERS, T. SCHWARZ, D. SHELDON, GC GIBSON, R. YU, LY GAO, Y. MA, DX WU, XF YAN, XY JIN, YX WANG, YQ CHEN, LH GUO, YT ZHAO, QQ GU, JH CHEN, LX WANG, P. XU, WT ZHANG, DF ZOU, H. BIEGEL, J. LEGA, S. MCCONNELL, VP NAGRAJ, SL GUERTIN, C. HULME-LOWE, SD TURNER, YF SHI, XG BAN, R. WALRAVEN, QJ HONG, S. KONG, A. VAN DE WALLE, JA TURTLE, M. BEN-NUN, S. RILEY, P. RILEY, U. KOYLUOGLU, D. DESROCHES, P. FORLI, B. HAMORY, C. KYRIAKIDES, H. LEIS, J. MILLIKEN, M. MOLONEY, J. MORGAN, N. NIRGUDKAR, G. OZCAN, N. PIWONKA, M. RAVI, C. SCHRADER, E. SHAKHNOVICH, D. SIEGEL, R. SPATZ, C. STIEFELING, B. WILKINSON, A. WONG, S. CAVANY, G. ESPANA, S. MOORE, R. OIDTMAN, A. PERKINS, David KRAUS (203 Česká republika, garant, domácí), Andrea KRAUS (703 Slovensko, domácí), ZF GAO, J. BIAN, W. CAO, JL FERRES, CZ LI, TY LIU, X. XIE, S. ZHANG, S. ZHENG, A. VESPIGNANI, M. CHINAZZI, JT DAVIS, K. MU, APY PIONTTI, XY XIONG, A. ZHENG, J. BAEK, V. FARIAS, A. GEORGESCU, R. LEVI, D. SINHA, J. WILDE, G. PERAKIS, MA BENNOUNA, D. NZE-NDONG, D. SINGHVI, I. SPANTIDAKIS, L. THAYAPARAN, A. TSIOURVAS, A. SARKER, A. JADBABAIE, D. SHAH, N. DELLA PENNA, LA CELI, S. SUNDAR, R. WOLFINGER, D. OSTHUS, L. CASTRO, G. FAIRCHILD, I. MICHAUD, D. KARLEN, M. KINSEY, LC MULLANY, K. RAINWATER-LOVETT, L. SHIN, K. TALLAKSEN, S. WILSON, EC LEE, J. DENT, KH GRANTZ, AL HILL, J. KAMINSKY, K. KAMINSKY, LT KEEGAN, SA LAUER, JC LEMAITRE, J. LESSLER, HR MEREDITH, J. PEREZ-SAEZ, S. SHAH, CP SMITH, SA TRUELOVE, J. WILLS, M. MARSHALL, L. GARDNER, K. NIXON, JC BURANT, L. WANG, L. GAO, Gu ZL, M. KIM, XY LI, GN WANG, YY WANG, S. YU, RC REINER, R. BARBER, E. GAKIDOU, Hay SI, S. LIM, C. MURRAY, D. PIGOTT, HL GURUNG, P. BACCAM, SA STAGE, BT SUCHOSKI, BA PRAKASH, B. ADHIKARI, JM CUI, A. RODRIGUEZ, A. TABASSUM, JJ XIE, P. KESKINOCAK, J. ASPLUND, A. BAXTER, BE ORUC, N. SERBAN, SO ARIK, M. DUSENBERRY, A. EPSHTEYN, E. KANAL, Le LT, CL LI, T. PFISTER, D. SAVA, R. SINHA, T. TSAI, N. YODER, J. YOON, LY ZHANG, S. ABBOTT, NI BOSSE, S. FUNK, J. HELLEWELL, SR MEAKIN, K. SHERRATT, MY ZHOU, R. KALANTARI, TK YAMANA, S. PEI, J. SHAMAN, ML LI, D. BERTSIMAS, OS LAMI, S. SONI, HT BOUARDI, T. AYER, M. ADEE, J. CHHATWAL, OO DALGIC, MA LADD, BP LINAS, P. MUELLER, J. XIAO, YJ WANG, QX WANG, SH XIE, DL ZENG, A. GREEN, J. BIEN, L. BROOKS, AJ HU, M. JAHJA, D. MCDONALD, B. NARASIMHAN, C. POLITSCH, S. RAJANALA, A. RUMACK, N. SIMON, RJ TIBSHIRANI, R. TIBSHIRANI, V. VENTURA, L. WASSERMAN, EB O'DEA, JM DRAKE, R. PAGANO, QT TRAN, LST HO, H. HUYNH, JW WALKER, RB SLAYTON, MA JOHANSSON, M. BIGGERSTAFF a NG REICH
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Proceedings of the National Academy of Sciences of the United States of America, WASHINGTON, National Academy of Sciences, 2022, 0027-8424
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 naive 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.
CRAMER, EY, EL RAY, VK LOPEZ, J. BRACHER, A. BRENNEN, AJC RIVADENEIRA, A. GERDING, T. GNEITING, KH HOUSE, YX HUANG, D. JAYAWARDENA, AH KANJI, A. KHANDELWAL, K. LE, A. MUHLEMANN, J. NIEMI, A. SHAH, A. STARK, YJ WANG, N. WATTANACHIT, MW ZORN, Gu YY, S. JAIN, N. BANNUR, A. DEVA, M. KULKARNI, S. MERUGU, A. RAVAL, S. SHINGI, A. TIWARI, J. WHITE, NF ABERNETHY, S. WOODY, M. DAHAN, S. FOX, K. GAITHER, M. LACHMANN, LA MEYERS, JG SCOTT, M. TEC, A. SRIVASTAVA, GE GEORGE, JC CEGAN, ID DETTWILLER, WP ENGLAND, MW FARTHING, RH HUNTER, B. LAFFERTY, I. LINKOV, ML MAYO, MD PARNO, MA ROWLAND, BD TRUMP, Y. ZHANG-JAMES, S. CHEN, SV FARAONE, J. HESS, CP MORLEY, A. SALEKIN, DL WANG, SM CORSETTI, TM BAER, MC EISENBERG, K. FALB, YT HUANG, Martin ET, E. MCCAULEY, RL MYERS, T. SCHWARZ, D. SHELDON, GC GIBSON, R. YU, LY GAO, Y. MA, DX WU, XF YAN, XY JIN, YX WANG, YQ CHEN, LH GUO, YT ZHAO, QQ GU, JH CHEN, LX WANG, P. XU, WT ZHANG, DF ZOU, H. BIEGEL, J. LEGA, S. MCCONNELL, VP NAGRAJ, SL GUERTIN, C. HULME-LOWE, SD TURNER, YF SHI, XG BAN, R. WALRAVEN, QJ HONG, S. KONG, A. VAN DE WALLE, JA TURTLE, M. BEN-NUN, S. RILEY, P. RILEY, U. KOYLUOGLU, D. DESROCHES, P. FORLI, B. HAMORY, C. KYRIAKIDES, H. LEIS, J. MILLIKEN, M. MOLONEY, J. MORGAN, N. NIRGUDKAR, G. OZCAN, N. PIWONKA, M. RAVI, C. SCHRADER, E. SHAKHNOVICH, D. SIEGEL, R. SPATZ, C. STIEFELING, B. WILKINSON, A. WONG, S. CAVANY, G. ESPANA, S. MOORE, R. OIDTMAN, A. PERKINS, David KRAUS, Andrea KRAUS, ZF GAO, J. BIAN, W. CAO, JL FERRES, CZ LI, TY LIU, X. XIE, S. ZHANG, S. ZHENG, A. VESPIGNANI, M. CHINAZZI, JT DAVIS, K. MU, APY PIONTTI, XY XIONG, A. ZHENG, J. BAEK, V. FARIAS, A. GEORGESCU, R. LEVI, D. SINHA, J. WILDE, G. PERAKIS, MA BENNOUNA, D. NZE-NDONG, D. SINGHVI, I. SPANTIDAKIS, L. THAYAPARAN, A. TSIOURVAS, A. SARKER, A. JADBABAIE, D. SHAH, N. DELLA PENNA, LA CELI, S. SUNDAR, R. WOLFINGER, D. OSTHUS, L. CASTRO, G. FAIRCHILD, I. MICHAUD, D. KARLEN, M. KINSEY, LC MULLANY, K. RAINWATER-LOVETT, L. SHIN, K. TALLAKSEN, S. WILSON, EC LEE, J. DENT, KH GRANTZ, AL HILL, J. KAMINSKY, K. KAMINSKY, LT KEEGAN, SA LAUER, JC LEMAITRE, J. LESSLER, HR MEREDITH, J. PEREZ-SAEZ, S. SHAH, CP SMITH, SA TRUELOVE, J. WILLS, M. MARSHALL, L. GARDNER, K. NIXON, JC BURANT, L. WANG, L. GAO, Gu ZL, M. KIM, XY LI, GN WANG, YY WANG, S. YU, RC REINER, R. BARBER, E. GAKIDOU, Hay SI, S. LIM, C. MURRAY, D. PIGOTT, HL GURUNG, P. BACCAM, SA STAGE, BT SUCHOSKI, BA PRAKASH, B. ADHIKARI, JM CUI, A. RODRIGUEZ, A. TABASSUM, JJ XIE, P. KESKINOCAK, J. ASPLUND, A. BAXTER, BE ORUC, N. SERBAN, SO ARIK, M. DUSENBERRY, A. EPSHTEYN, E. KANAL, Le LT, CL LI, T. PFISTER, D. SAVA, R. SINHA, T. TSAI, N. YODER, J. YOON, LY ZHANG, S. ABBOTT, NI BOSSE, S. FUNK, J. HELLEWELL, SR MEAKIN, K. SHERRATT, MY ZHOU, R. KALANTARI, TK YAMANA, S. PEI, J. SHAMAN, ML LI, D. BERTSIMAS, OS LAMI, S. SONI, HT BOUARDI, T. AYER, M. ADEE, J. CHHATWAL, OO DALGIC, MA LADD, BP LINAS, P. MUELLER, J. XIAO, YJ WANG, QX WANG, SH XIE, DL ZENG, A. GREEN, J. BIEN, L. BROOKS, AJ HU, M. JAHJA, D. MCDONALD, B. NARASIMHAN, C. POLITSCH, S. RAJANALA, A. RUMACK, N. SIMON, RJ TIBSHIRANI, R. TIBSHIRANI, V. VENTURA, L. WASSERMAN, EB O'DEA, JM DRAKE, R. PAGANO, QT TRAN, LST HO, H. HUYNH, JW WALKER, RB SLAYTON, MA JOHANSSON, M. BIGGERSTAFF a NG REICH. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences of the United States of America. WASHINGTON: National Academy of Sciences, 2022, roč. 119, č. 15, s. "e2113561119", 12 s. ISSN 0027-8424. Dostupné z: https://dx.doi.org/10.1073/pnas.2113561119.
@article{1875231, author = {Cramer, EY and Ray, EL and Lopez, VK and Bracher, J. and Brennen, A. and Rivadeneira, AJC and Gerding, A. and Gneiting, T. and House, KH and Huang, YX and Jayawardena, D. and Kanji, AH and Khandelwal, A. and Le, K. and Muhlemann, A. and Niemi, J. and Shah, A. and Stark, A. and Wang, YJ and Wattanachit, N. and Zorn, MW and YY, Gu and Jain, S. and Bannur, N. and Deva, A. and Kulkarni, M. and Merugu, S. and Raval, A. and Shingi, S. and Tiwari, A. and White, J. and Abernethy, NF and Woody, S. and Dahan, M. and Fox, S. and Gaither, K. and Lachmann, M. and Meyers, LA and Scott, JG and Tec, M. and Srivastava, A. and George, GE and Cegan, JC and Dettwiller, ID and England, WP and Farthing, MW and Hunter, RH and Lafferty, B. and Linkov, I. and Mayo, ML and Parno, MD and Rowland, MA and Trump, BD and ZhangandJames, Y. and Chen, S. and Faraone, SV and Hess, J. and Morley, CP and Salekin, A. and Wang, DL and Corsetti, SM and Baer, TM and Eisenberg, MC and Falb, K. and Huang, YT and ET, Martin and McCauley, E. and Myers, RL and Schwarz, T. and Sheldon, D. and Gibson, GC and Yu, R. and Gao, LY and Ma, Y. and Wu, DX and Yan, XF and Jin, XY and Wang, YX and Chen, YQ and Guo, LH and Zhao, YT and Gu, QQ and Chen, JH and Wang, LX and Xu, P. and Zhang, WT and Zou, DF and Biegel, H. and Lega, J. and McConnell, S. and Nagraj, VP and Guertin, SL and HulmeandLowe, C. and Turner, SD and Shi, YF and Ban, XG and Walraven, R. and Hong, QJ and Kong, S. and van de Walle, A. and Turtle, JA and BenandNun, M. and Riley, S. and Riley, P. and Koyluoglu, U. and DesRoches, D. and Forli, P. and Hamory, B. and Kyriakides, C. and Leis, H. and Milliken, J. and Moloney, M. and Morgan, J. and Nirgudkar, N. and Ozcan, G. and Piwonka, N. and Ravi, M. and Schrader, C. and Shakhnovich, E. and Siegel, D. and Spatz, R. and Stiefeling, C. and Wilkinson, B. and Wong, A. and Cavany, S. and Espana, G. and Moore, S. and Oidtman, R. and Perkins, A. and Kraus, David and Kraus, Andrea and Gao, ZF and Bian, J. and Cao, W. and Ferres, JL and Li, CZ and Liu, TY and Xie, X. and Zhang, S. and Zheng, S. and Vespignani, A. and Chinazzi, M. and Davis, JT and Mu, K. and Piontti, APY and Xiong, XY and Zheng, A. and Baek, J. and Farias, V. and Georgescu, A. and Levi, R. and Sinha, D. and Wilde, J. and Perakis, G. and Bennouna, MA and NzeandNdong, D. and Singhvi, D. and Spantidakis, I. and Thayaparan, L. and Tsiourvas, A. and Sarker, A. and Jadbabaie, A. and Shah, D. and Della Penna, N. and Celi, LA and Sundar, S. and Wolfinger, R. and Osthus, D. and Castro, L. and Fairchild, G. and Michaud, I. and Karlen, D. and Kinsey, M. and Mullany, LC and RainwaterandLovett, K. and Shin, L. and Tallaksen, K. and Wilson, S. and Lee, EC and Dent, J. and Grantz, KH and Hill, AL and Kaminsky, J. and Kaminsky, K. and Keegan, LT and Lauer, SA and Lemaitre, JC and Lessler, J. and Meredith, HR and PerezandSaez, J. and Shah, S. and Smith, CP and Truelove, SA and Wills, J. and Marshall, M. and Gardner, L. and Nixon, K. and Burant, JC and Wang, L. and Gao, L. and ZL, Gu and Kim, M. and Li, XY and Wang, GN and Wang, YY and Yu, S. and Reiner, RC and Barber, R. and Gakidou, E. and SI, Hay and Lim, S. and Murray, C. and Pigott, D. and Gurung, HL and Baccam, P. and Stage, SA and Suchoski, BT and Prakash, BA and Adhikari, B. and Cui, JM and Rodriguez, A. and Tabassum, A. and Xie, JJ and Keskinocak, P. and Asplund, J. and Baxter, A. and Oruc, BE and Serban, N. and Arik, SO and Dusenberry, M. and Epshteyn, A. and Kanal, E. and LT, Le and Li, CL and Pfister, T. and Sava, D. and Sinha, R. and Tsai, T. and Yoder, N. and Yoon, J. and Zhang, LY and Abbott, S. and Bosse, NI and Funk, S. and Hellewell, J. and Meakin, SR and Sherratt, K. and Zhou, MY and Kalantari, R. and Yamana, TK and Pei, S. and Shaman, J. and Li, ML and Bertsimas, D. and Lami, OS and Soni, S. and Bouardi, HT and Ayer, T. and Adee, M. and Chhatwal, J. and Dalgic, OO and Ladd, MA and Linas, BP and Mueller, P. and Xiao, J. and Wang, YJ and Wang, QX and Xie, SH and Zeng, DL and Green, A. and Bien, J. and Brooks, L. and Hu, AJ et al.}, article_location = {WASHINGTON}, article_number = {15}, doi = {http://dx.doi.org/10.1073/pnas.2113561119}, keywords = {forecasting; COVID-19; ensemble forecast; model evaluation}, language = {eng}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, title = {Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States}, url = {http://dx.doi.org/10.1073/pnas.2113561119}, volume = {119}, year = {2022} }
TY - JOUR ID - 1875231 AU - Cramer, EY - Ray, EL - Lopez, VK - Bracher, J. - Brennen, A. - Rivadeneira, AJC - Gerding, A. - Gneiting, T. - House, KH - Huang, YX - Jayawardena, D. - Kanji, AH - Khandelwal, A. - Le, K. - Muhlemann, A. - Niemi, J. - Shah, A. - Stark, A. - Wang, YJ - Wattanachit, N. - Zorn, MW - YY, Gu - Jain, S. - Bannur, N. - Deva, A. - Kulkarni, M. - Merugu, S. - Raval, A. - Shingi, S. - Tiwari, A. - White, J. - Abernethy, NF - Woody, S. - Dahan, M. - Fox, S. - Gaither, K. - Lachmann, M. - Meyers, LA - Scott, JG - Tec, M. - Srivastava, A. - George, GE - Cegan, JC - Dettwiller, ID - England, WP - Farthing, MW - Hunter, RH - Lafferty, B. - Linkov, I. - Mayo, ML - Parno, MD - Rowland, MA - Trump, BD - Zhang-James, Y. - Chen, S. - Faraone, SV - Hess, J. - Morley, CP - Salekin, A. - Wang, DL - Corsetti, SM - Baer, TM - Eisenberg, MC - Falb, K. - Huang, YT - ET, Martin - McCauley, E. - Myers, RL - Schwarz, T. - Sheldon, D. - Gibson, GC - Yu, R. - Gao, LY - Ma, Y. - Wu, DX - Yan, XF - Jin, XY - Wang, YX - Chen, YQ - Guo, LH - Zhao, YT - Gu, QQ - Chen, JH - Wang, LX - Xu, P. - Zhang, WT - Zou, DF - Biegel, H. - Lega, J. - McConnell, S. - Nagraj, VP - Guertin, SL - Hulme-Lowe, C. - Turner, SD - Shi, YF - Ban, XG - Walraven, R. - Hong, QJ - Kong, S. - van de Walle, A. - Turtle, JA - Ben-Nun, M. - Riley, S. - Riley, P. - Koyluoglu, U. - 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, S. - Espana, G. - Moore, S. - Oidtman, R. - Perkins, A. - Kraus, David - Kraus, Andrea - Gao, ZF - Bian, J. - Cao, W. - Ferres, JL - Li, CZ - Liu, TY - Xie, X. - Zhang, S. - Zheng, S. - Vespignani, A. - Chinazzi, M. - Davis, JT - Mu, K. - Piontti, APY - Xiong, XY - Zheng, A. - Baek, J. - Farias, V. - Georgescu, A. - Levi, R. - Sinha, D. - Wilde, J. - Perakis, G. - Bennouna, MA - Nze-Ndong, D. - Singhvi, D. - Spantidakis, I. - Thayaparan, L. - Tsiourvas, A. - Sarker, A. - Jadbabaie, A. - Shah, D. - Della Penna, N. - Celi, LA - Sundar, S. - Wolfinger, R. - Osthus, D. - Castro, L. - Fairchild, G. - Michaud, I. - Karlen, D. - Kinsey, M. - Mullany, LC - Rainwater-Lovett, K. - Shin, L. - Tallaksen, K. - Wilson, S. - Lee, EC - Dent, J. - Grantz, KH - Hill, AL - Kaminsky, J. - Kaminsky, K. - Keegan, LT - Lauer, SA - Lemaitre, JC - Lessler, J. - Meredith, HR - Perez-Saez, J. - Shah, S. - Smith, CP - Truelove, SA - Wills, J. - Marshall, M. - Gardner, L. - Nixon, K. - Burant, JC - Wang, L. - Gao, L. - ZL, Gu - Kim, M. - Li, XY - Wang, GN - Wang, YY - Yu, S. - Reiner, RC - Barber, R. - Gakidou, E. - SI, Hay - Lim, S. - Murray, C. - Pigott, D. - Gurung, HL - Baccam, P. - Stage, SA - Suchoski, BT - Prakash, BA - Adhikari, B. - Cui, JM - Rodriguez, A. - Tabassum, A. - Xie, JJ - Keskinocak, P. - Asplund, J. - Baxter, A. - Oruc, BE - Serban, N. - Arik, SO - Dusenberry, M. - Epshteyn, A. - Kanal, E. - LT, Le - Li, CL - Pfister, T. - Sava, D. - Sinha, R. - Tsai, T. - Yoder, N. - Yoon, J. - Zhang, LY - Abbott, S. - Bosse, NI - Funk, S. - Hellewell, J. - Meakin, SR - Sherratt, K. - Zhou, MY - Kalantari, R. - Yamana, TK - Pei, S. - Shaman, J. - Li, ML - Bertsimas, D. - Lami, OS - Soni, S. - Bouardi, HT - Ayer, T. - Adee, M. - Chhatwal, J. - Dalgic, OO - Ladd, MA - Linas, BP - Mueller, P. - Xiao, J. - Wang, YJ - Wang, QX - Xie, SH - Zeng, DL - Green, A. - Bien, J. - Brooks, L. - Hu, AJ et al. PY - 2022 TI - Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States JF - Proceedings of the National Academy of Sciences of the United States of America VL - 119 IS - 15 SP - "e2113561119" EP - "e2113561119" PB - National Academy of Sciences SN - 00278424 KW - forecasting KW - COVID-19 KW - ensemble forecast KW - model evaluation UR - http://dx.doi.org/10.1073/pnas.2113561119 N2 - 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 naive 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. ER -
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