Recent Incidence of Human Malaria Caused by Plasmodium knowlesi in the Villages in Kudat Peninsula, Sabah, Malaysia: Mapping of The Infection Risk Using Remote Sensing Data

Shigeharu Sato, and Bumpei Tojo, and Tomonori Hoshi, and Lis Izni Fanirah Minsong, and Omar Kwang Kugan, and Nelbon Giloi, and Kamruddin Ahmed, and Mohammad Saffree Jeffree, and Kazuhiko Moji, and Kiyoshi Kita, (2019) Recent Incidence of Human Malaria Caused by Plasmodium knowlesi in the Villages in Kudat Peninsula, Sabah, Malaysia: Mapping of The Infection Risk Using Remote Sensing Data. International Journal of Environmental Research and Public Health, 16 (2954). pp. 1-10.

[img]
Preview
Text
Recent Incidence of Human Malaria Caused by Plasmodium knowlesi in the Villages in Kudat Peninsula Sabah Malaysia Mapping of The Infection Risk Using Remote Sensing Data.pdf

Download (1MB) | Preview

Abstract

Plasmodium knowlesi (Pk) is a malaria parasite that naturally infects macaque monkeys in Southeast Asia. Pk malaria, the zoonosis transmitted from the infected monkeys to the humans by Anopheles mosquito vectors, is now a serious health problem in Malaysian Borneo. To createa strategic plan to control Pk malaria, it is important to estimate the occurrence of the disease correctly. The rise of Pk malaria has been explained as being due to ecological changes, especially deforestation. In this research, we analysed the time-series satellite images of MODIS (MODerate-resolution Imaging Spectroradiometer) of the Kudat Peninsula in Sabah and created the “Pk risk map” on which the Land-Use and Land-Cover (LULC) information was visualised. The case number of Pk malaria of a village appeared to have a correlation with the quantity of two specific LULC classes, the mosaic landscape of oil palm groves and the nearby land-use patches of dense forest, surrounding the village. Applying a Poisson multivariate regression with a generalized linear mixture model (GLMM), the occurrence of Pk malaria cases was estimated from the population and the quantified LULC distribution on the map. The obtained estimations explained the real case numbers well, when the contribution of another risk factor, possibly the occupation of the villagers, is considered. This implies that the occurrence of the Pk malaria cases of a village can be predictable from the population of the village and the LULC distribution shown around it on the map. The Pk risk map will help to assess the Pk malaria risk distributions quantitatively and to discover the hidden key factors behind the spread of this zoonosis.

Item Type: Article
Uncontrolled Keywords: Plasmodiumknowlesi, infection risk map, geographical analysis, remote sensing, MODIS, EVI phenology, generalised linear mixture model, Bayesian inference
Subjects: R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine
Divisions: FACULTY > Faculty of Medicine and Health Sciences
Depositing User: MDM SITI AZIZAH IDRIS
Date Deposited: 23 Jan 2020 02:27
Last Modified: 18 Apr 2020 13:09
URI: http://eprints.ums.edu.my/id/eprint/24676

Actions (login required)

View Item View Item