Predicting and Validating Malaria Vulnerability in Rural Kwara State, Nigeria: A Geospatial - Clinical Laboratory Approach

Olalubi, Oluwasogo A.; Salako, Gabriel; Sawyerr, Henry Olawale; Ajao, A. M.; Adetunde, Oluwasegun T.; Tambo, Ernest

doi:10.2139/ssrn.3213663

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“…It has been discovered that mosquitoes can lay their eggs in rivers, puddles, and dammed water bodies. Because water is required for the oviposition and breeding stages of mosquito larvae, mosquito density is higher during the rainy season than during the dry season, resulting in seasonal malaria epidemiology (Olalubi et al, 2020). Anopheles larvae breed in rivers, ditches, and habitat within a radius of 0.5−2 km of the homes of malariapositive sufferers ( Gómez-Barroso et al, 2017;Kifle et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Malaria Surveillance Mapping in Yogyakarta Special Region, Indonesia

Nuraisyah

Nurlaily²,

Ruliyandari³

et al. 2022

J Epidemiol Public Healt

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Background: Kulon Progo is one of contributing malaria cases in Indonesia and eliminating incidence malaria still unsolved problem in Indonesia. This study aims to analyze the relationship between mosquito breeding sites, the distribution of malaria cases through Arc-GIS specifically for buffering and spatial analysis in Kulon Progo Regency from 2015 to 2021. Subjects dan Method: This descriptive research retrospective approach was conducted from secondary data on malaria cases in Kulon Progo between 2015 and 2021. The variable research in this study are positive malaria cases diagnosed using the traditional method of thick blood and thin smear. The sampling technique in this study used total sampling, in totally 265 cases were included. The Data on malaria cases in Kulon Progo Health Office were used as instruments to develop the spatial map and questionnaires served as a confirmation sheet for demographic characteristic. GPS (Global Positioning System) 10.3 used to determine the coordinates of malaria cases. Data on malaria cases are presented in a six-year time series. Area classification using Arc-GIS 10.1 software with buffer analysis and visualization data was utilized to determine the distribution pattern of malaria. Results: Incidence declined sharply 23.9 to 0.4 cases per 100,000 in 2015 to 2021. The purely cluster of malaria cases trend were in the watershed area at a distance of <250 meters in Kokap Sub-district. Malaria cases were mostly found in rice fields with a distance of <250 meters in Samigaluh Sub-district. All malaria cases were in the garden areas of <250meters in Nanggulang and the forest area of >250 meters in the Kalibawang Sub-district. Conclusion: Probability of malaria transmission are rivers, rice fields and gardens. It is necessary to hold training on the use of the Arc-GIS application for surveillance officers.

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Section: Discussionmentioning

confidence: 99%