Forecasting COVID-19 cases in US states using reconstructed incidence data

Rebecca K Nash, Sangeeta Bhatia, Jack Wardle, Anne Cori and Pierre Nouvellet

PLOS ONE, 2026, vol. 21, issue 8, 1-13

Abstract: Branching process models are commonly used in infectious disease forecasting and often rely on daily incidence data, but their utility can be restricted if incidence is not reported daily or if reporting becomes less frequent during prolonged outbreaks. In this study, an Expectation Maximisation algorithm is used to reconstruct the daily incidence of COVID-19 cases from weekly case counts. Using data from 13 US states that maintained mostly daily reporting of COVID-19 cases from March 2020 to February 2022, we evaluate forecasting performance by comparing models using the true daily incidence with those using reconstructed daily incidence. Our results show that forecasts generated from reconstructed incidence perform equally well as those generated using true daily incidence. These findings demonstrate the viability of using reconstructed incidence data for real-time forecasting, which could be particularly useful in scenarios where maintaining daily reporting is unsustainable or in settings with limited surveillance capacity.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0353983 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 53983&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0353983

DOI: 10.1371/journal.pone.0353983

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().