Document Type
Article
Publication Date
2017
Abstract
In areas approaching malaria elimination, human mobility patterns are important in determining the proportion of malaria cases that are imported or the result of low-level, endemic transmission. A convenience sample of participants enrolled in a longitudinal cohort study in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia, was selected to carry a GPS data logger for one month from October 2013 to August 2014. Density maps and activity space plots were created to evaluate seasonal movement patterns. Time spent outside the household compound during anopheline biting times, and time spent in malaria high- and lowrisk areas, were calculated. There was evidence of seasonal movement patterns, with increased long-distance movement during the dry season. A median of 10.6% (interquartile range (IQR): 5.8-23.8) of time was spent away from the household, which decreased during anopheline biting times to 5.6% (IQR:1.7-14.9). The per cent of time spent in malaria high-risk areas for participants residing in high-risk areas ranged from 83.2% to 100%, but ranged from only 0.0% to 36.7% for participants residing in low-risk areas. Interventions targeted at the household may be more effective because of restricted movement during the rainy season, with limited movement between high- and low-risk areas.
Recommended Citation
Searle, K. M.; Lubinda, J.; Hamapumbu, H.; Shields, T. M.; Curriero, F. C.; Smith, D. L.; Thuma, Philip; and Moss, W. J., "Characterizing and Quantifying Human Movement Patterns Using GPS Data Loggers in an Area Approaching Malaria Elimination in Rural Southern Zambia" (2017). Biology Educator Scholarship. 129.
https://mosaic.messiah.edu/bio_ed/129
Comments
Searle, K., et al. (2017). Characterizing and Quantifying Human Movement Patterns Using GPS Data Loggers in an Area Approaching Malaria Elimination in Rural Southern Zambia. Royal Society Open Science 4(5)1-12.
© 2017 Royal Society. Published under Creative Commons Attribution License. Original published version available at https://doi.org/10.1098/rsos.170046.
Searle, K. M., Lubinda, J., Hamapumbu, H., Shields, T. M., Curriero, F. C., Smith, D. L., Thuma, P. E., & Moss, W. J. (2017). Characterizing and quantifying human movement patterns using GPS data loggers in an area approaching malaria elimination in rural southern Zambia. Royal Society Open Science, 4(5), 170046. https://doi.org/10.1098/rsos.170046