TY - JOUR
T1 - The demographics of human and malaria movement and migration patterns in East Africa
AU - Pindolia, Deepa K.
AU - Garcia, Andres J.
AU - Huang, Zhuojie
AU - Smith, David L.
AU - Alegana, Victor A.
AU - Noor, Abdisalan M.
AU - Snow, Robert W.
AU - Tatem, Andrew J.
N1 - Funding Information:
AJT & DLS acknowledge funding support from the Emerging Pathogens Institute, University of Florida, the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and are also supported by grants from NIH/NIAID (U19AI089674) and the Bill and Melinda Gates Foundation (#49446 and #1032350). DLS acknowledges funding support from Bloomberg Family Foundation. RWS is supported by the Wellcome Trust as Principal Research Fellow (#079080). AMN is supported by a Wellcome Trust Intermediate Research Fellowship (##095127). Both RWS and AMN are also supported by Wellcome Trust Major Overseas Programme grant to the KEMRI/Wellcome Trust Research Programme (#092654). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This paper forms part of the output of the AfriPop population mapping project (www.afripop.org) and the human mobility mapping project (www.thummp.org).
PY - 2013
Y1 - 2013
N2 - Introduction. The 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. Methods. National 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. Results: Patterns 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. Conclusion: Census 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.
AB - Introduction. The 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. Methods. National 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. Results: Patterns 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. Conclusion: Census 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.
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U2 - 10.1186/1475-2875-12-397
DO - 10.1186/1475-2875-12-397
M3 - Article
C2 - 24191976
AN - SCOPUS:84887054069
VL - 12
JO - Malaria Journal
JF - Malaria Journal
SN - 1475-2875
IS - 1
M1 - 397
ER -