Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi

Alinune N. Kabaghe, Michael G. Chipeta, Robert S. McCann, Kamija S. Phiri, Michèle van Vugt, Willem Takken, Peter Diggle, Anja D. Terlouw, Joshua Yukich
<span title="2017-02-14">2017</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
OPEN ACCESS Citation: Kabaghe AN, Chipeta MG, McCann RS, Phiri KS, van Vugt M, Takken W, et al. (2017) Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated crosssectional surveys in rural Malawi. PLoS ONE 12(2): e0172266. above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets. Conclusions Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence
more &raquo; ... n accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the greatest health impact and is ready for assessment in other diseases. spatial variation to the predicted log-odds of malaria prevalence in children 6-59 months at each of the observed locations in focal area B.
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