{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T05:15:21Z","timestamp":1793078121113,"version":"4.5.0"},"reference-count":42,"publisher":"National Academy of Sciences","issue":"34","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000060","name":"HHS | NIH | National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["1R01AI160780"],"award-info":[{"award-number":["1R01AI160780"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,8,25]]},"abstract":"<jats:p>Models of infectious disease dynamics should align the spatial scale of mobility data to the scale of travel relevant to inferring disease introduction events and subsequent local transmission. Despite this, the biases of spatially aggregating mobility data on model inferences are rarely explored. Here, we examine the sensitivity of infectious disease modeling results to different spatial scales of human mobility by integrating multiscale mobility data from Sri Lanka into Susceptible, Exposed, Infected, Recovered (SEIR) metapopulation models. Aggregated mobility data were obtained from mobile phone records at three increasingly coarser spatial scales to simulate epidemic spread of an emerging acute respiratory infection. We found that travel was not evenly distributed among nested spatial scales when data were disaggregated, with subunits close to borders exhibiting higher levels of travel relative to the unit. In simulations of disease transmission, these different scales of mobility aggregation had potential to yield wide variations in the estimated spatial invasion timing but not in final epidemic size. Modeled differences in spatial invasion time depended on disease transmission intensity and exogenous factors like the initial seeding location. Our results carry implications for infectious disease modeling best practices and public health response, particularly the policy decisions made from model inferences that were or were not informed by the relevant spatial scale of mobility data such as intervention timing, risk communication, and resource allocation.<\/jats:p>","DOI":"10.1073\/pnas.2533465123","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T17:59:27Z","timestamp":1787075967000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing the impact of incorporating spatially aggregated human mobility data into infectious disease models"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2845-5427","authenticated-orcid":false,"given":"Ronan","family":"Corgel","sequence":"first","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health","place":["Baltimore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyra H.","family":"Grantz","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health","place":["Baltimore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1083-3850","authenticated-orcid":false,"given":"Lauren","family":"Gardner","sequence":"additional","affiliation":[{"name":"Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering","place":["Baltimore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9437-1907","authenticated-orcid":false,"given":"Derek A. 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