RESUMEN
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, however it is not clear how this maps to the movement of children who are more likely to be infected. Using travel surveys across two 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 three to five 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% - 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.
RESUMEN
The COVID-19 pandemic has impacted many facets of human behavior, including human mobility partially driven by the implementation of non-pharmaceutical interventions (NPIs) such as stay at home orders, travel restrictions, and workplace and school closures. Given the importance of human mobility in the transmission of SARS-CoV-2, there have been an increase in analyses of mobility data to understand the COVID-19 pandemic to date. However, despite an abundance of these analyses, few have focused on Sub-Saharan Africa (SSA). Here, we use mobile phone calling data to provide a spatially refined analysis of sub-national human mobility patterns during the COVID-19 pandemic from March 2020-July 2021 in Zambia using transmission and mobility models. Overall, among highly trafficked intra-province routes, mobility decreased up to 52% during the time of the strictest NPIs (March-May 2020) compared to baseline. However, despite dips in mobility during the first wave of COVID-19 cases, mobility returned to baseline levels and did not drop again suggesting COVID-19 cases did not influence mobility in subsequent waves.
RESUMEN
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.