Kostandova Natalya, Prosperi Christine, Mutembo Simon, Nakazwe Chola, Namukoko Harriet, Nachinga Bertha, Chongwe Gershom, Chilumba Innocent, Kabalo Elliot N, Makungo Kabondo, Matakala Kalumbu H, Musukwa Gloria, Hamahuwa Mutinta, Mufwambi Webster, Matoba Japhet, Mutale Irene, Simulundu Edgar, Ndubani Phillimon, Hasan Alvira Z, Truelove Shaun A, Winter Amy K, Carcelen Andrea C, Lau Bryan, Moss William J, Wesolowski Amy
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Am J Epidemiol. 2025 Jun 3;194(6):1584-1594. doi: 10.1093/aje/kwae304.
Models of measles transmission can be used to identify areas of high risk to tailor immunization strategies. Estimates of spatial connectivity can be derived from data such as mobile phone records, but it is not clear how this maps to the movement of children who are more likely to be infected. Using travel surveys across 2 districts in Zambia and national mobile phone data, we compared estimates of out-of-district travel for the population captured in the mobile phone data and child-specific travel from travel surveys. We then evaluated the impact of unadjusted and adjusted connectivity measures on simulated measles virus introduction events across Zambia. The number of trips made by children from the travel survey was 3 to 5 times lower than the general population estimates from mobile phone data. This decreased the percentage of districts with measles virus introduction events from 78% when using unadjusted data to 51% to 64% following adjustment. Failure to account for age-specific heterogeneities in travel estimated from mobile phone data resulted in overestimating subnational areas at high risk of introduction events, which could divert mitigation efforts to districts that are at lower risk.
麻疹传播模型可用于识别高风险区域，以制定免疫策略。空间连通性估计可从手机记录等数据中得出，但尚不清楚这如何映射到更易感染儿童的流动情况。利用赞比亚两个地区的出行调查和全国手机数据，我们比较了手机数据中记录的人群的区外出行估计与出行调查中儿童特定出行情况。然后，我们评估了未经调整和调整后的连通性措施对赞比亚模拟麻疹病毒引入事件的影响。出行调查中儿童的出行次数比手机数据中一般人群的估计低3至5倍。这使得发生麻疹病毒引入事件的地区比例从未经调整数据时的78%降至调整后的51%至64%。未考虑从手机数据估计的特定年龄出行异质性会导致高估存在引入事件高风险的次国家区域，这可能会将缓解措施转向风险较低的地区。
