{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:13:55Z","timestamp":1777428835979,"version":"3.51.4"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI074345"],"award-info":[{"award-number":["R01 AI074345"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1165144"],"award-info":[{"award-number":["OPP1165144"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.<\/jats:p>","DOI":"10.1093\/biomtc\/ujad007","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T01:54:25Z","timestamp":1706493265000},"source":"Crossref","is-referenced-by-count":3,"title":["Adaptive sequential surveillance with network and temporal dependence"],"prefix":"10.1093","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7404-8088","authenticated-orcid":false,"given":"Ivana","family":"Malenica","sequence":"first","affiliation":[{"name":"Department of Statistics, Harvard University , Cambridge, MA 02138 , United States"},{"name":"Division of Biostatistics , Berkeley, CA 94704 , United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy R","family":"Coyle","sequence":"additional","affiliation":[{"name":"Preva Group , Seattle, WA, 98104 , United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark J","family":"van\u00a0der\u00a0Laan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics , Berkeley, CA 94704 , United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4941-2041","authenticated-orcid":false,"given":"Maya L","family":"Petersen","sequence":"additional","affiliation":[{"name":"Division of Biostatistics , Berkeley, CA 94704 , United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"2024012901535459700_bib1","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1093\/cid\/ciz1096","article-title":"Machine learning to identify persons at high-risk of human immunodeficiency virus acquisition in rural Kenya and Uganda","volume":"71","author":"Balzer","year":"2019","journal-title":"Clinical Infectious Diseases"},{"key":"2024012901535459700_bib2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1002\/sim.7320","article-title":"Online cross-validation-based ensemble learning","volume":"37","author":"Benkeser","year":"2018","journal-title":"Statistics in Medicine"},{"key":"2024012901535459700_bib3","first-page":"610","article-title":"Resource constrained deep reinforcement learning","volume-title":"Proceedings of the International Conference on Automated Planning and Scheduling","author":"Bhatia","year":"2021"},{"key":"2024012901535459700_bib4","article-title":"Sequential causal inference in a single world of connected units","author":"Bibaut","year":"2021"},{"key":"2024012901535459700_bib5","article-title":"COVID-19: strategies for allocation of test kits","author":"Biswas","year":"2020"},{"key":"2024012901535459700_bib6","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1214\/16-AOS1534","article-title":"Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward","volume":"45","author":"Chambaz","year":"2017","journal-title":"The Annals of Statistics"},{"key":"2024012901535459700_bib7","doi-asserted-by":"crossref","first-page":"w20445","DOI":"10.4414\/smw.2020.20445","article-title":"Optimal allocation of limited test resources for the quantification of COVID-19 infections","volume":"150","author":"Chatzimanolakis","year":"2020","journal-title":"Swiss Medical Weekly"},{"key":"2024012901535459700_bib8","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1111\/j.1541-0420.2011.01685.x","article-title":"Population intervention causal effects based on stochastic interventions","volume":"68","author":"Diaz","year":"2012","journal-title":"Biometrics"},{"key":"2024012901535459700_bib9","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1002\/sim.9238","article-title":"Optimal test allocation strategy during the COVID-19 pandemic and beyond","volume":"41","author":"Du","year":"2022","journal-title":"Statistics in Medicine"},{"key":"2024012901535459700_bib10","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.stamet.2005.02.003","article-title":"Asymptotics of cross-validated risk estimation in estimator selection and performance assessment","volume":"2","author":"Dudoit","year":"2005","journal-title":"Statistical Methodology"},{"key":"2024012901535459700_bib11","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1093\/biomet\/asy043","article-title":"Constructing dynamic treatment regimes over indefinite time horizons","volume":"105","author":"Ertefaie","year":"2018","journal-title":"Biometrika"},{"key":"2024012901535459700_bib12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jeconom.2021.09.009","article-title":"Who should get vaccinated? 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