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Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia
BACKGROUND: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning sys...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493314/ https://www.ncbi.nlm.nih.gov/pubmed/22583705 http://dx.doi.org/10.1186/1475-2875-11-165 |
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author | Midekisa, Alemayehu Senay, Gabriel Henebry, Geoffrey M Semuniguse, Paulos Wimberly, Michael C |
author_facet | Midekisa, Alemayehu Senay, Gabriel Henebry, Geoffrey M Semuniguse, Paulos Wimberly, Michael C |
author_sort | Midekisa, Alemayehu |
collection | PubMed |
description | BACKGROUND: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. METHODS: In this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. RESULTS: Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. CONCLUSIONS: Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions. |
format | Online Article Text |
id | pubmed-3493314 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34933142012-11-09 Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia Midekisa, Alemayehu Senay, Gabriel Henebry, Geoffrey M Semuniguse, Paulos Wimberly, Michael C Malar J Research BACKGROUND: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. METHODS: In this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. RESULTS: Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. CONCLUSIONS: Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions. BioMed Central 2012-05-14 /pmc/articles/PMC3493314/ /pubmed/22583705 http://dx.doi.org/10.1186/1475-2875-11-165 Text en Copyright ©2012 Midekisa et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Midekisa, Alemayehu Senay, Gabriel Henebry, Geoffrey M Semuniguse, Paulos Wimberly, Michael C Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title | Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title_full | Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title_fullStr | Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title_full_unstemmed | Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title_short | Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia |
title_sort | remote sensing-based time series models for malaria early warning in the highlands of ethiopia |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493314/ https://www.ncbi.nlm.nih.gov/pubmed/22583705 http://dx.doi.org/10.1186/1475-2875-11-165 |
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