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SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject\u2010level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation\u2010based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large\u2010population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation\u2010based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS\u2010CoV\u20102 dynamics, and apply our method to national surveillance counts from the 2013\u20132015 West Africa Ebola\u00a0outbreak.<\/jats:p>","DOI":"10.1111\/biom.13538","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T06:05:37Z","timestamp":1628575537000},"page":"1530-1541","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts"],"prefix":"10.1093","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6361-702X","authenticated-orcid":false,"given":"Jonathan","family":"Fintzi","sequence":"first","affiliation":[{"name":"Biostatistics Research Branch National Institute of Allergy and Infectious Diseases Rockville Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon","family":"Wakefield","sequence":"additional","affiliation":[{"name":"Departments of Biostatistics and Statistics University of Washington Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1917-9288","authenticated-orcid":false,"given":"Vladimir N.","family":"Minin","sequence":"additional","affiliation":[{"name":"Department of Statistics University of California Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.idm.2017.03.001"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Anderson S. 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