Abstract
IntroductionThe quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations.MethodsNational population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined.ResultsPatterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20–30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10–20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use.ConclusionCensus and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.
Highlights
The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities
Directly estimating malaria movement has primarily been based on travel history data from selected population groups or geographic areas, with travel studied as a possible risk factor for infection [23,24]
Novel study methods based on mobile phone usage data combined with P. falciparum malaria transmission maps, for example, have begun to tackle Human population movement (HPM) and malaria movement dynamics at other scales [19], such as in Zanzibar island and at a national level in Kenya [9,33]
Summary
The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. A variety of data types, statistical analyses and mathematical models have been used to quantify HPM patterns [13,14] and specific HPM patterns relevant for malaria dynamics [15,16,17,18,19,20] at different spatial scales National surveillance data, such as hospital patient records, that provide individual-level travel history and demographic data have been used to directly quantify features of imported malaria cases [21]. Surveillance data is likely to miss asymptomatic parasite carriers and non-health seeking cases [22], and comprehensive and reliable surveillance systems to detect imported cases are generally under developed in low-income countries In these settings, directly estimating malaria movement has primarily been based on travel history data from selected population groups or geographic areas, with travel studied as a possible risk factor for infection [23,24]. Detailed comparisons of high-risk demographic HPM groups have not been undertaken at a national or regional level, despite their importance in understanding malaria movement and refining quantitative evidence for guiding policy decisions
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