Abstract
Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.
Highlights
The Loreto Department of Peru, in the Amazon region, is the most important malaria-endemic area of the country where more than 95% of country-wide cases are transmitted
This study provides a field-deployable approach to obtaining objective GPS data to characterize the role of human movement in the epidemiology of malaria in river networks in the Peruvian Amazon
Human movement has previously been indirectly pointed as important for malaria risk and exposure based on epidemiologic, molecular and vector biology studies [3, 4, 8, 9], and portrayed using new geo-referencing approached tailored to the lack of network accessibility in these settings [19, 20], this study provides the basis to obtain fine-scale resolution to obtain evidence regarding the influence of human movement in malaria transmission
Summary
By applying movement ecology approaches, these data can be combined with spatial and environmental data to relate the probability of an individual using a particular space with characteristics of that location [24]; these approaches have been applied to examine fine-scale movement into malaria vector habitats [25] They have not been applied within a riverine setting, and the costs of utilizing those devices at a population level (∼100 USD per GPS tracker) remain prohibitively expensive for public health programs in this region. As a consequence of this knowledge gap, the MoH remains focused on control activities in high-risk communities as single entities, instead of encompassing highly connected landscape units (i.e., communities within watersheds) This study addresses this gap and aimed to develop a new device to monitor and describe human movement patterns, as a step to provide evidence of the role of human population movement on malaria epidemiology in rural villages in the Peruvian Amazon river networks. To identify potential risk factors for infection associated with mobility, novel movement ecology analytical approaches were applied to describe heterogeneities in movement patterns by sociodemographic characteristics and infection status of villagers
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