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Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border

BACKGROUND: Human mobility is a driver for the reemergence or resurgence of malaria and has been identified as a source of cross-border transmission. However, movement patterns are difficult to measure in rural areas where malaria risk is high. In countries with malaria elimination goals, it is esse...

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Autores principales: Hast, Marisa, Mharakurwa, Sungano, Shields, Timothy M., Lubinda, Jailos, Searle, Kelly, Gwanzura, Lovemore, Munyati, Shungu, Moss, William J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756631/
https://www.ncbi.nlm.nih.gov/pubmed/36522643
http://dx.doi.org/10.1186/s12879-022-07903-4
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author Hast, Marisa
Mharakurwa, Sungano
Shields, Timothy M.
Lubinda, Jailos
Searle, Kelly
Gwanzura, Lovemore
Munyati, Shungu
Moss, William J.
author_facet Hast, Marisa
Mharakurwa, Sungano
Shields, Timothy M.
Lubinda, Jailos
Searle, Kelly
Gwanzura, Lovemore
Munyati, Shungu
Moss, William J.
author_sort Hast, Marisa
collection PubMed
description BACKGROUND: Human mobility is a driver for the reemergence or resurgence of malaria and has been identified as a source of cross-border transmission. However, movement patterns are difficult to measure in rural areas where malaria risk is high. In countries with malaria elimination goals, it is essential to determine the role of mobility on malaria transmission to implement appropriate interventions. METHODS: A study was conducted in Mutasa District, Zimbabwe, to investigate human movement patterns in an area of persistent transmission along the Mozambique border. Over 1 year, a convenience sample of 20 participants/month was recruited from active malaria surveillance cohorts to carry an IgotU(®) GT-600 global positioning system (GPS) data logger during all daily activities. Consenting participants were tested for malaria at data logger distribution using rapid antigen diagnostic tests and completed a survey questionnaire. GPS data were analyzed using a trajectory analysis tool, and participant movement patterns were characterized throughout the study area and across the border into Mozambique using movement intensity maps, activity space plots, and statistical analyses. RESULTS: From June 2016–May 2017, 184 participants provided movement tracks encompassing > 350,000 data points and nearly 8000 person-days. Malaria prevalence at logger distribution was 3.7%. Participants traveled a median of 2.8 km/day and spent a median of 4.6 h/day away from home. Movement was widespread within and outside the study area, with participants traveling up to 500 km from their homes. Indices of mobility were higher in the dry season than the rainy season (median km traveled/day = 3.5 vs. 2.2, P = 0.03), among male compared to female participants (median km traveled/day = 3.8 vs. 2.0, P = 0.0008), and among adults compared to adolescents (median total km traveled = 104.6 vs. 59.5, P = 0.05). Half of participants traveled outside the study area, and 30% traveled into Mozambique, including 15 who stayed in Mozambique overnight. CONCLUSIONS: Study participants in Mutasa District, Zimbabwe, were highly mobile throughout the year. Many participants traveled long distances from home, including overnight trips into Mozambique, with clear implications for malaria control. Interventions targeted at mobile populations and cross-border transmission may be effective in preventing malaria introductions in this region. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07903-4.
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spelling pubmed-97566312022-12-17 Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border Hast, Marisa Mharakurwa, Sungano Shields, Timothy M. Lubinda, Jailos Searle, Kelly Gwanzura, Lovemore Munyati, Shungu Moss, William J. BMC Infect Dis Research BACKGROUND: Human mobility is a driver for the reemergence or resurgence of malaria and has been identified as a source of cross-border transmission. However, movement patterns are difficult to measure in rural areas where malaria risk is high. In countries with malaria elimination goals, it is essential to determine the role of mobility on malaria transmission to implement appropriate interventions. METHODS: A study was conducted in Mutasa District, Zimbabwe, to investigate human movement patterns in an area of persistent transmission along the Mozambique border. Over 1 year, a convenience sample of 20 participants/month was recruited from active malaria surveillance cohorts to carry an IgotU(®) GT-600 global positioning system (GPS) data logger during all daily activities. Consenting participants were tested for malaria at data logger distribution using rapid antigen diagnostic tests and completed a survey questionnaire. GPS data were analyzed using a trajectory analysis tool, and participant movement patterns were characterized throughout the study area and across the border into Mozambique using movement intensity maps, activity space plots, and statistical analyses. RESULTS: From June 2016–May 2017, 184 participants provided movement tracks encompassing > 350,000 data points and nearly 8000 person-days. Malaria prevalence at logger distribution was 3.7%. Participants traveled a median of 2.8 km/day and spent a median of 4.6 h/day away from home. Movement was widespread within and outside the study area, with participants traveling up to 500 km from their homes. Indices of mobility were higher in the dry season than the rainy season (median km traveled/day = 3.5 vs. 2.2, P = 0.03), among male compared to female participants (median km traveled/day = 3.8 vs. 2.0, P = 0.0008), and among adults compared to adolescents (median total km traveled = 104.6 vs. 59.5, P = 0.05). Half of participants traveled outside the study area, and 30% traveled into Mozambique, including 15 who stayed in Mozambique overnight. CONCLUSIONS: Study participants in Mutasa District, Zimbabwe, were highly mobile throughout the year. Many participants traveled long distances from home, including overnight trips into Mozambique, with clear implications for malaria control. Interventions targeted at mobile populations and cross-border transmission may be effective in preventing malaria introductions in this region. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07903-4. BioMed Central 2022-12-15 /pmc/articles/PMC9756631/ /pubmed/36522643 http://dx.doi.org/10.1186/s12879-022-07903-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hast, Marisa
Mharakurwa, Sungano
Shields, Timothy M.
Lubinda, Jailos
Searle, Kelly
Gwanzura, Lovemore
Munyati, Shungu
Moss, William J.
Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title_full Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title_fullStr Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title_full_unstemmed Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title_short Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border
title_sort characterizing human movement patterns using gps data loggers in an area of persistent malaria in zimbabwe along the mozambique border
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756631/
https://www.ncbi.nlm.nih.gov/pubmed/36522643
http://dx.doi.org/10.1186/s12879-022-07903-4
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