Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.
INTRODUCTION:In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhib...
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| Format: | Article |
| Jezik: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Serija: | PLoS ONE |
| Online dostop: | http://europepmc.org/articles/PMC5308819?pdf=render |
| _version_ | 1828429910344990720 |
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| author | Alinune N Kabaghe Michael G Chipeta Robert S McCann Kamija S Phiri Michèle van Vugt Willem Takken Peter Diggle Anja D Terlouw |
| author_facet | Alinune N Kabaghe Michael G Chipeta Robert S McCann Kamija S Phiri Michèle van Vugt Willem Takken Peter Diggle Anja D Terlouw |
| author_sort | Alinune N Kabaghe |
| collection | DOAJ |
| description | INTRODUCTION:In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. METHODS:We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. FINDINGS:We conducted five rounds of sampling, and tested 876 children aged 6-59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots-where predicted malaria prevalence was 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 prediction 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. |
| first_indexed | 2024-12-10T17:40:03Z |
| format | Article |
| id | doaj.art-0c9f23f41a724af28c00d24d2132c18c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T17:40:03Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0c9f23f41a724af28c00d24d2132c18c2022-12-22T01:39:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01122e017226610.1371/journal.pone.0172266Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.Alinune N KabagheMichael G ChipetaRobert S McCannKamija S PhiriMichèle van VugtWillem TakkenPeter DiggleAnja D TerlouwINTRODUCTION:In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. METHODS:We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. FINDINGS:We conducted five rounds of sampling, and tested 876 children aged 6-59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots-where predicted malaria prevalence was 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 prediction 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.http://europepmc.org/articles/PMC5308819?pdf=render |
| spellingShingle | Alinune N Kabaghe Michael G Chipeta Robert S McCann Kamija S Phiri Michèle van Vugt Willem Takken Peter Diggle Anja D Terlouw Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. PLoS ONE |
| title | Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. |
| title_full | Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. |
| title_fullStr | Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. |
| title_full_unstemmed | Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. |
| title_short | Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi. |
| title_sort | adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross sectional surveys in rural malawi |
| url | http://europepmc.org/articles/PMC5308819?pdf=render |
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