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Integrating mobility, travel survey, and malaria case data to understand drivers of malaria importation to Zanzibar, 2022-2023

Muller, J. G.; Dewalt, K.; Goel, V.; Ali, M.; Hassan, W.; Mohamed, B.; Choloi, B. B.; Msolo, D. C.; Zacharia, A.; Nwange, M.; Kane, R.; Bisanzio, D.; Emch, M.; Bjorkman, A.; Reithinger, R.; Shija, S. J.; Ngasala, B. E.; Juliano, J. J.; Lin, J. T.

2025-06-05 infectious diseases
10.1101/2025.06.04.25327997 medRxiv
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BackgroundZanzibar has achieved historic reductions in malaria incidence, but high connectivity to mainland Tanzania and imported cases remain a challenge to "last mile" malaria elimination. MethodsTo understand factors driving malaria importation, we collected travel histories and demographics of malaria cases presenting to 94 health facilities across Zanzibars main island, Unguja, from 2022-2023. We also analyzed population mobility data--self-reported travel at the outbound Dar es Salaam ferry terminal and Meta colocation and movement distribution--to examine movement patterns between Unguja and mainland Tanzania. We integrated these with Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data to explore the seasonality of human movement and travel-associated malaria risk. ResultsAmong 1,172 malaria cases reporting recent travel to mainland Tanzania, travel to Tanga (20%), Dar es Salaam (20%), and Morogoro (15%) were most common. Nearly half of travelers had spent over 14 nights away from home; the majority were visiting family. While mainlanders made up two-thirds of ferry travelers, 28% of travel-associated malaria cases in Unguja reported primary residence on the mainland. Zanzibari residents who reported travel to mainland regions with high/moderate malaria risk during the dry season composed the highest proportion of travel-related malaria cases. Long-distance travel off Unguja decreased during the rainy seasons, and imported cases correlated with rainfall at the mainland source, rather than in Zanzibar. With different biases, colocation and ferry data approximated the proportional makeup of at-risk travel relatively well. ConclusionsMovement flows and seasonal rainfall patterns drive imported malaria in predictable ways that can be harnessed to target high-risk travelers for intervention.

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