Nowcasting and forecasting COVID-19 waves: the recursive and stochastic nature of transmission
Carregando...
Citações na Scopus
5
Tipo de produção
article
Data de publicação
2022
Título da Revista
ISSN da Revista
Título do Volume
Editora
ROYAL SOC
Autores
Citação
ROYAL SOCIETY OPEN SCIENCE, v.9, n.8, article ID 220489, 13p, 2022
Resumo
We propose a parsimonious, yet effective, susceptible-exposed-infected-removed-type model that incorporates the time change in the transmission and death rates. The model is calibrated by Tikhonov-type regularization from official reports from New York City (NYC), Chicago, the State of Sao Paulo, in Brazil and British Columbia, in Canada. To forecast, we propose different ways to extend the transmission parameter, considering its estimated values. The forecast accuracy is then evaluated using real data from the above referred places. All the techniques accurately provided forecast scenarios for periods 15 days long. One of the models effectively predicted the magnitude of the four waves of infections in NYC, including the one caused by the Omicron variant for periods of 45 days using out-of-sample data.
Palavras-chave
epidemiological models, nowcasting, forecasting, COVID-19, model calibration
Referências
- Albani V., 2021, MEDRXIV
- Albani V, 2021, BMC INFECT DIS, V21, DOI 10.1186/s12879-021-06780-7
- Albani V, 2019, INVERSE PROBL IMAG, V13, P211, DOI 10.3934/ipi.2019012
- Albani VVL, 2021, VACCINE, V39, P6088, DOI 10.1016/j.vaccine.2021.08.098
- Albani VVL, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-88281-w
- Alleman TW, 2021, EPIDEMICS-NETH, V37, DOI 10.1016/j.epidem.2021.100505
- Anastassopoulou C, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0230405
- Calvetti D, 2020, FRONT PHYS-LAUSANNE, V8, DOI 10.3389/fphy.2020.00261
- Campos EL, 2021, INFECT DIS MODEL, V6, P751, DOI 10.1016/j.idm.2021.05.003
- Canada, COVID 19 DAT CAN
- Candido DS, 2020, SCIENCE, V369, P1255, DOI 10.1126/science.abd2161
- Chicago, COVID 19 DAT CHIC
- Chowell Gerardo, 2017, Infect Dis Model, V2, P379, DOI 10.1016/j.idm.2017.08.001
- Coelho FC, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0238214
- Dehning J, 2020, SCIENCE, V369, P160, DOI 10.1126/science.abb9789
- Gatheral J., 2006, VOLATILITY SURFACE P
- Gatto M, 2020, P NATL ACAD SCI USA, V117, P10484, DOI 10.1073/pnas.2004978117
- Gaudilliere J.-P., 2020, GLOBAL HLTH NEW WORL
- Gnanvi JE, 2021, INFECT DIS MODEL, V6, P258, DOI 10.1016/j.idm.2020.12.008
- Goyal A, 2022, J R SOC INTERFACE, V19, DOI 10.1098/rsif.2021.0811
- Grasselli G, 2020, JAMA-J AM MED ASSOC, V323, P1574, DOI 10.1001/jama.2020.5394
- Gray A, 2011, SIAM J APPL MATH, V71, P876, DOI 10.1137/10081856X
- Hamilton J.D., 1994, TIME SERIES ANAL, V2
- Ioannidis JPA, 2022, INT J FORECASTING, V38, P423, DOI 10.1016/j.ijforecast.2020.08.004
- Kermack WO, 1927, P R SOC LOND A-CONTA, V115, P700, DOI 10.1098/rspa.1927.0118
- Kishore N, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-86297-w
- Larrosa JMC, 2021, J MED VIROL, V93, P2252, DOI 10.1002/jmv.26659
- Lauer SA, 2020, ANN INTERN MED, V172, P577, DOI 10.7326/M20-0504
- Lipton A., 2020, RISK MAGAZINE APR, DOI [10.2139/ssrn.3580185, DOI 10.2139/SSRN.3580185]
- NYC, COV 19 DAT NYC
- Rohani P., 2007, MODELING INFECT DIS
- SEADE, COVID 19 DAT SAO PAU
- Thomas MT., 2021, MOLAB INVENTORY MOBI, DOI [10.48509/molab.8077, DOI 10.48509/MOLAB.8077]
- Tomori DV, 2021, BMC MED, V19, DOI 10.1186/s12916-021-02139-6
- Trian N, 2020, YEAR PROTESTS CIVIL
- Vogel KP., NEW YORK TIMES
- Wu JM, 2022, CRIT REV FOOD SCI, V62, P783, DOI [10.1080/10408398.2020.1828813, 10.1038/s41591-020-0822-7]
- Wu JT, 2020, LANCET, V395, P689, DOI 10.1016/S0140-6736(20)30260-9