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A Bayesian model combining epidemiological, human mobility, and parasite genetic data was developed to estimate malaria importation and geographic origins of\n                    <jats:italic>Plasmodium falciparum<\/jats:italic>\n                    cases. Using microhaplotype-based genetic relatedness from 1605 samples across nine Mozambican provinces in 2022, the study focused on two low-transmission districts in the south: Magude and Matutuine. Parasites from southern Mozambique showed lower genetic relatedness to those from northern\/central regions (0.021) than the national average (0.034, p&lt;0.001), indicating limited connectivity. Overall, 42% (88\/207) of infections in these districts were classified as imported, mainly originating from Inhambane province (63% [55\/88]). Imported cases showed higher parasite complexity than local ones (odds ratios [OR] = 1.3). Importation rates differed markedly between districts \u2013 Matutuine (48.60%, 87\/179) was far more affected than Magude (10.71%, 3\/28) \u2013 highlighting the need for localised rather than uniform elimination strategies. In Matutuine, importation appears to be actively sustaining transmission, suggesting that reducing malaria burden in source regions (particularly Inhambane) and targeting travellers from central and northern Mozambique would have the greatest elimination impact.\n                  <\/jats:p>","DOI":"10.7554\/elife.107136","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:30:28Z","timestamp":1753889428000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.107136","source":"Crossref","is-referenced-by-count":1,"title":["Estimating probabilities of malaria importation in southern Mozambique through modelling P. falciparum genomics and mobility 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