Prediction of SARS-CoV-2 transmission dynamics based on population-level cycle threshold values: A Machine Learning and mechanistic modeling study Article Swipe
Afraz A. Khan
,
Hind Sbihi
,
Michael A. Irvine
,
Agatha N. Jassem
,
Yayuk Joffres
,
Braeden Klaver
,
Naveed Z. Janjua
,
Aamir Bharmal
,
Carmen Ng
,
Amanda Wilmer
,
John Kenneth Galbraith
,
Marc G. Romney
,
Bonnie Henry
,
Linda Hoang
,
Mel Krajden
,
Catherine A. Hogan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.06.23286837
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.06.23286837
Background Polymerase chain reaction (PCR) cycle threshold (Ct) values can be used to estimate the viral burden of Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2) and predict population-level epidemic trends. We investigated the use of machine learning (ML) and epidemic transmission modeling based on Ct value distribution for SARS-CoV-2 incidence prediction during an Omicron-predominant period. Methods Using simulated data, we developed a ML model to predict the reproductive number based on Ct value distribution, and validated it on out-of-sample province-level data. We also developed an epidemiological model and fitted it to province-level data to accurately predict incidence. Results Based on simulated data, the ML model predicted the reproductive number with highest performance on out-of-sample province-level data. The epidemiological model was validated on outbreak data, and fitted to province-level data, and accurately predicted incidence. Conclusions These modeling approaches can complement traditional surveillance, especially when diagnostic testing practices change over time. The models can be tailored to different epidemiological settings and used in real time to guide public health interventions. Funding This work was supported by funding from Genome BC, Michael Smith Foundation for Health Research and British Columbia Centre for Disease Control Foundation to C.A.H. This work was also funded by the Public Health Agency of Canada COVID-19 Immunity Task Force COVID-19 Hot Spots Competition Grant (2021-HQ-000120) to M.G.R.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.03.06.23286837
- https://www.medrxiv.org/content/medrxiv/early/2023/03/06/2023.03.06.23286837.full.pdf
- OA Status
- green
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323363937
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323363937Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.03.06.23286837Digital Object Identifier
- Title
-
Prediction of SARS-CoV-2 transmission dynamics based on population-level cycle threshold values: A Machine Learning and mechanistic modeling studyWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-06Full publication date if available
- Authors
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Afraz A. Khan, Hind Sbihi, Michael A. Irvine, Agatha N. Jassem, Yayuk Joffres, Braeden Klaver, Naveed Z. Janjua, Aamir Bharmal, Carmen Ng, Amanda Wilmer, John Kenneth Galbraith, Marc G. Romney, Bonnie Henry, Linda Hoang, Mel Krajden, Catherine A. HoganList of authors in order
- Landing page
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https://doi.org/10.1101/2023.03.06.23286837Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2023/03/06/2023.03.06.23286837.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2023/03/06/2023.03.06.23286837.full.pdfDirect OA link when available
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Epidemiology, Population, Incidence (geometry), Public health, Basic reproduction number, Demography, Medicine, Transmission (telecommunications), Statistics, Environmental health, Computer science, Mathematics, Internal medicine, Pathology, Sociology, Geometry, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5015826695 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 16 |
| corresponding_institution_ids | https://openalex.org/I141945490, https://openalex.org/I2802940977 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
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| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.03282971 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |