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Open AccessArticle

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

1
Office of Infectious Disease, Washington State Department of Health, Olympia, WA 98195, USA
2
Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
3
Department of Global Health, University of Washington, Seattle, WA 98195, USA
4
Department of Allergy and Infectious Disease, University of Washington, Seattle, WA 98195, USA
*
Author to whom correspondence should be addressed.
Viruses 2020, 12(2), 142; https://doi.org/10.3390/v12020142
Received: 31 December 2019 / Revised: 21 January 2020 / Accepted: 22 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue HIV Molecular Epidemiology for Prevention)
Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1–1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5–3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria. View Full-Text
Keywords: human immunodeficiency virus (HIV); molecular epidemiology; cluster detection; sequence analysis; public health response; disease surveillance human immunodeficiency virus (HIV); molecular epidemiology; cluster detection; sequence analysis; public health response; disease surveillance
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MDPI and ACS Style

Erly, S.J.; Herbeck, J.T.; Kerani, R.P.; Reuer, J.R. Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State. Viruses 2020, 12, 142.

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