Towards malaria risk prediction in Afghanistan using remote sensing
Abstract Background Malaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across...
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ftdoajarticles:oai:doaj.org/article:f59e536db6544659a5d5af088d1e566a 2023-05-15T15:16:08+02:00 Towards malaria risk prediction in Afghanistan using remote sensing Soebiyanto Radina P Adimi Farida Safi Najibullah Kiang Richard 2010-05-01T00:00:00Z https://doi.org/10.1186/1475-2875-9-125 https://doaj.org/article/f59e536db6544659a5d5af088d1e566a EN eng BMC http://www.malariajournal.com/content/9/1/125 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-9-125 1475-2875 https://doaj.org/article/f59e536db6544659a5d5af088d1e566a Malaria Journal, Vol 9, Iss 1, p 125 (2010) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2010 ftdoajarticles https://doi.org/10.1186/1475-2875-9-125 2022-12-31T01:28:35Z Abstract Background Malaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme. Methods Provincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation. Results Vegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R 2 of 0.845. Although the R 2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases. Conclusions The provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 9 1 125 |
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Directory of Open Access Journals: DOAJ Articles |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Soebiyanto Radina P Adimi Farida Safi Najibullah Kiang Richard Towards malaria risk prediction in Afghanistan using remote sensing |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Malaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme. Methods Provincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation. Results Vegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R 2 of 0.845. Although the R 2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases. Conclusions The provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a ... |
format |
Article in Journal/Newspaper |
author |
Soebiyanto Radina P Adimi Farida Safi Najibullah Kiang Richard |
author_facet |
Soebiyanto Radina P Adimi Farida Safi Najibullah Kiang Richard |
author_sort |
Soebiyanto Radina P |
title |
Towards malaria risk prediction in Afghanistan using remote sensing |
title_short |
Towards malaria risk prediction in Afghanistan using remote sensing |
title_full |
Towards malaria risk prediction in Afghanistan using remote sensing |
title_fullStr |
Towards malaria risk prediction in Afghanistan using remote sensing |
title_full_unstemmed |
Towards malaria risk prediction in Afghanistan using remote sensing |
title_sort |
towards malaria risk prediction in afghanistan using remote sensing |
publisher |
BMC |
publishDate |
2010 |
url |
https://doi.org/10.1186/1475-2875-9-125 https://doaj.org/article/f59e536db6544659a5d5af088d1e566a |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 9, Iss 1, p 125 (2010) |
op_relation |
http://www.malariajournal.com/content/9/1/125 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-9-125 1475-2875 https://doaj.org/article/f59e536db6544659a5d5af088d1e566a |
op_doi |
https://doi.org/10.1186/1475-2875-9-125 |
container_title |
Malaria Journal |
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9 |
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1 |
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125 |
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1766346439679541248 |