High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data

Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria r...
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Autor*in:	Ryunosuke Komura [verfasserIn] Masayuki Matsuoka [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	malaria Democratic Republic of the Congo multi-criteria evaluation geographic information system

Übergeordnetes Werk:	In: ISPRS International Journal of Geo-Information - MDPI AG, 2012, 12(2023), 12, p 489
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:12, p 489

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijgi12120489

Katalog-ID:	DOAJ098859161

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allfieldsSound	10.3390/ijgi12120489 doi (DE-627)DOAJ098859161 (DE-599)DOAJd61064eb8cf9442388b85c692727f692 DE-627 ger DE-627 rakwb eng G1-922 Ryunosuke Komura verfasserin aut High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts. malaria Democratic Republic of the Congo multi-criteria evaluation geographic information system Geography (General) Masayuki Matsuoka verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 12(2023), 12, p 489 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:12 year:2023 number:12, p 489 https://doi.org/10.3390/ijgi12120489 kostenfrei https://doaj.org/article/d61064eb8cf9442388b85c692727f692 kostenfrei https://www.mdpi.com/2220-9964/12/12/489 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2023 12, p 489
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authorswithroles_txt_mv	Ryunosuke Komura @@aut@@ Masayuki Matsuoka @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	Ryunosuke Komura
spellingShingle	Ryunosuke Komura misc G1-922 misc malaria misc Democratic Republic of the Congo misc multi-criteria evaluation misc geographic information system misc Geography (General) High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data
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topic_title	G1-922 High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data malaria Democratic Republic of the Congo multi-criteria evaluation geographic information system
topic	misc G1-922 misc malaria misc Democratic Republic of the Congo misc multi-criteria evaluation misc geographic information system misc Geography (General)
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title	High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data
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title_sort	high-temporal-resolution prediction of malaria transmission risk in south kivu, democratic republic of the congo, based on multi-criteria evaluation using geospatial data
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title_auth	High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data
abstract	Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts.
abstractGer	Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts.
abstract_unstemmed	Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts.
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title_short	High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data
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High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data

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