Nowcasting and forecasting COVID-19 waves: The recursive and stochastic nature of transmission

V. V.L. Albani, R. A.S. Albani, E. Massad, J. P. Zubelli

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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 São 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.

Original languageBritish English
Article number220489
JournalRoyal Society Open Science
Volume9
Issue number8
DOIs
StatePublished - 24 Aug 2022

Keywords

  • COVID-19
  • Epidemiological Models
  • Forecasting
  • Model calibration
  • Nowcasting

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