The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March - April, 2020

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creativework.keywords - en

COVID-19 / epidemiology*

COVID-19 / prevention & control

Canada / epidemiology

Forecasting / methods*

Humans

Incidence

Models, Statistical*

Pandemics

Public Health

SARS-CoV-2

dc.contributor.author

Smith, Ben A.

Bancej, Christina

Fazil, Aamir

Mullah, Muhammad

Yan, Ping

Zhang, Shenghai

dc.date.accessioned

2024-06-06T20:22:07Z

dc.date.available

2024-06-06T20:22:07Z

dc.date.issued

2021-06-01

dc.description.abstract - en

BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally. Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning. METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard’s model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model. Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis. RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1, 2020 and May 1, 2020 would be 11,156 (90 % prediction interval: 9,156−13,905) and 54,745 (90 % prediction interval: 54,252−55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation. Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances. The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed. CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada. Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures. Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.

dc.identifier.doi

https://doi.org/10.1016/j.epidem.2021.100457

dc.identifier.issn

1878-0067

dc.identifier.pubmedID

33857889

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/2557

dc.language.iso

en

dc.publisher - en

Elsevier B.V.

dc.publisher - fr

Elsevier B.V.

dc.rights - en

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

dc.rights - fr

Creative Commons Attribution - Pas d'utilisation commerciale - Pas de modification 4.0 International (CC BY-NC-ND 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by-nc-nd/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by-nc-nd/4.0/deed.fr

dc.subject - en

Health

dc.subject - fr

Santé

dc.subject.en - en

Health

dc.subject.fr - fr

Santé

dc.title - en

The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March - April, 2020

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

100457

local.article.journaltitle

Epidemics

local.article.journalvolume

35

local.pagination

1-10

local.peerreview - en

Yes

local.peerreview - fr

Oui

local.requestdoi - en

No

local.requestdoi - fr

No

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