The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia

Published on Dec 1, 2019in International Journal of Health Geographics2.86
· DOI :10.1186/s12942-019-0183-y
Marisa Hast2
Estimated H-index: 2
(Johns Hopkins University),
Kelly M. Searle4
Estimated H-index: 4
(UMN: University of Minnesota)
+ 8 AuthorsWilliam J. Moss36
Estimated H-index: 36
(Johns Hopkins University)
Human movement is a driver of malaria transmission and has implications for sustainable malaria control. However, little research has been done on the impact of fine-scale movement on malaria transmission and control in high-transmission settings. As interest in targeted malaria control increases, evaluations are needed to determine the appropriateness of these strategies in the context of human mobility across a variety of transmission settings. A human mobility study was conducted in Nchelenge District, a high-transmission setting in northern Zambia. Over 1 year, 84 participants were recruited from active malaria surveillance cohorts to wear a global positioning system data logger for 1 month during all daily activity. Participants completed a survey questionnaire and underwent malaria testing and treatment at the time of logger distribution and at collection 1 month later. Incident malaria infections were identified using polymerase chain reaction. Participant movement was characterized throughout the study area and across areas targeted for an indoor residual spraying (IRS) intervention. Participant movement patterns were compared using movement intensity maps, activity space plots, and statistical analyses. Malaria risk was characterized across participants using spatial risk maps and time spent away from home during peak vector biting hours. Movement data were collected from 82 participants, and 63 completed a second study visit. Participants exhibited diverse mobility patterns across the study area, including movement into and out of areas targeted for IRS, potentially mitigating the impact of IRS on parasite prevalence. Movement patterns did not differ significantly by season or age, but male participants traveled longer distances and spent more time away from home. Monthly malaria incidence was 22%, and malaria risk was characterized as high across participants. Participants with incident parasitemia traveled a shorter distance and spent more time away from home during peak biting hours; however, these relationships were not statistically significant, and malaria risk score did not differ by incident parasitemia. Individual movement patterns in Nchelenge District, Zambia have implications for malaria control, particularly the effectiveness of targeted IRS strategies. Large and fine-scale population mobility patterns should be considered when planning intervention strategies across transmission settings.
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#1Tara A. Brant (LSTM: Liverpool School of Tropical Medicine)H-Index: 1
#2Patricia N. Okorie (UI: University of Ibadan)H-Index: 7
Last.Louise A. Kelly-Hope (LSTM: Liverpool School of Tropical Medicine)H-Index: 23
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#1Jennifer Claire Stevenson (Johns Hopkins University)H-Index: 14
#2Jessie Pinchoff (Johns Hopkins University)H-Index: 8
Last.Douglas E. Norris (Johns Hopkins University)H-Index: 32
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#1Smita Das (Johns Hopkins University)H-Index: 3
#2Mbanga Muleba (Emory University)H-Index: 1
Last.Douglas E. Norris (Johns Hopkins University)H-Index: 32
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#1William J. Moss (UCSF: University of California, San Francisco)H-Index: 36
#2Grant Dorsey (UCSF: University of California, San Francisco)H-Index: 56
Last.James W. Kazura (Case Western Reserve University)H-Index: 50
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