Developing a Predictive Model for Plasmodium knowlesi–Susceptible Areas in Malaysia Using Geospatial Data and Artificial Neural Networks

Abstract

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

“…Molecular epidemiological studies have found that the geographical separation could have also driven the allopatric divergence of P. knowlesi into distinct subpopulations ( Divis et al, 2017 ). Studies in Sabah, a state in Malaysian Borneo, have demonstrated the association between environmental factors and knowlesi malaria risk ( Brock et al, 2019 ; Fornace et al, 2019 ; Sato et al, 2019 ; Hod et al, 2022 ). However, environmental influences on knowlesi malaria in Peninsular Malaysia are not widely studied.…”

A multi-criteria framework for disease surveillance site selection: case study for Plasmodium knowlesi malaria in Indonesia

“…There are several existing spatial distribution models of P. knowlesi malaria risk [ 9 , 36 , 37 ]. While other models predict P. knowlesi malaria risk on a district or state level, a key distinction of the model described by Shearer et al [ 9 ] is a map with a resolution of approximately 5 × 5 km 2 of relative predicted risk throughout the Southeast Asia region [ 9 ] (electronic supplementary material, §S2).…”

First documented case of Plasmodium knowlesi infection in Denmark in a traveller returning from Malaysian Borneo

“…The simian parasite Plasmodium knowlesi with a natural reservoir in long-tailed macaques, pig-tailed macaques and banded leaf monkeysis considered the fth Plasmodium species causing malaria in humans (1,2). P. knowlesi cases have been reported from almost every country in Southeast Asia, but the Malaysian states of Sarawak and Sabah have the highest incidence in the region (3), where it is an emerging challenge in efforts to eliminate malaria (4). No indigenous cases of Plasmodium falciparum, Plasmodium vivax or Plasmodium malariae have been recorded in Malaysia since 2017, and in 2020, the country reached the World Health Organization Global Technical Strategy for Malaria 2016-2030 goal by interrupting local malaria transmission (5).…”