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We first select publicly-available sociodemographic, economic, and health-related characteristics of 3140 United States (US) counties at the time of the confirmed early US outbreak of the novel SARS-CoV-2 virus in March 2020 for analysis. Our primary result is the set of US counties that experienced the upper quartile of population-adjusted Omicron variant incidence at certain period (e.g., 100 days) after Omicron variant\u2019s appearance in the US. We show more predictive results by incorporating additional data and features (e.g., human mobility) that can be acquired dynamically after the outbreak, to improve prediction accuracy at the cost of additional waiting time and effort. Towards the goal of decision support, we aim to prospectively evaluate our models\u2019 ability to classify and rank US counties at risk. We measure their classification performance using the area under receiver operating characteristic curve score with 95% confidence intervals. We further calculate the proportion of the top counties by Omicron incidence that our models correctly identify, and compare their score with those of individual county-level features that can serve as a heuristic predictive performance baseline. Our results show that ML algorithms predict county-level Omicron variant incidence with better performance than natural heuristics that decision makers might otherwise use. More generally, historical data from the first wave of a novel pandemic can help predict the incidence of future variants and strengthen state or federal pandemic response interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Data-driven predictive models that capture patterns from early viral variants can support policymaking related to emerging viral variants.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>County-level sociodemographic, health, and economic characteristics are predictive of early COVID-19 outcomes in the United States (US).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Machine learning models trained on early US county-level COVID-19 outcomes are additionally predictive of county-level SARS-CoV-2 Omicron variant outcomes.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>County-level machine learning models can be used as a critical policymaking tool given the inevitability of novel emerging viruses.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10729-025-09728-4","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T07:53:57Z","timestamp":1760342037000},"page":"738-758","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk"],"prefix":"10.1007","volume":"28","author":[{"given":"Kevin B.","family":"Smith","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqian","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6372-6066","authenticated-orcid":false,"given":"Brian T.","family":"Denton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"9728_CR1","doi-asserted-by":"publisher","first-page":"101858","DOI":"10.1016\/j.pmedr.2022.101858","volume":"28","author":"G Avirappattu","year":"2022","unstructured":"Avirappattu G, Pach A III, Locklear CE et al (2022) An optimized machine learning model for identifying socio-economic, demographic and health-related variables associated with low vaccination levels that vary across ZIP codes in California. 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