Risk factor of plasmodium knowlesi infection in Sabah Borneo Malaysia, 2020: A population-based case-control study

Chin, Abraham Zefong; Avoi, Richard; Atil, Azman; Lukman, Khamisah Awang; Rahim, Syed Sharizman Syed Abdul; Ibrahim, Mohd Yusof; Ahmed, Kawsar; Jeffree, Mohammad Saffree

doi:10.1371/journal.pone.0257104

Cited by 14 publications

(12 citation statements)

References 49 publications

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“…For each pixel grid, the highest value of either of the overlaid value was selected to represent the value of the output map. A priority zone map highlighting important areas for intervention targeting agricultural and logging workers is important as this group of populations is considered at-risk and regularly exposed to potentially infective mosquitoes ( Grigg et al, 2017 ; Chin et al, 2021 ). It was noted that 92% of tree cover loss in the year 2010–2019 was driven by deforestation ( Global Forest Watch, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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

Phang

Hamid²,

Jelip³

et al. 2023

Front. Microbiol.

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The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUCROC values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75–345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.

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Section: Methodsmentioning

confidence: 99%

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

Phang

Hamid²,

Jelip³

et al. 2023

Front. Microbiol.

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“…Although BLAST searches were suggestive, phylogenetic analyses of the sequencing data were not definitive for species confirmation. It is, therefore, possible that a proportion of these individuals had submicroscopic P. knowlesi infections that were asymptomatic or not the cause of fever, but the corollary to this is that there should be other people in the same areas with clinically evident knowlesi malaria requiring treatment in local healthcare facilities 27,39 with some progressing to severe disease 6,[17][18][19][20] . Even if P. knowlesi cases were misidentified by microscopy as one of the human malarias 19,28,29 , historical data showing low number of total malaria cases with relatively few hospitalisations in Kapuas Hulu District 12 would suggest that the burden of knowlesi malaria in the community, if present at all, is small.…”

Section: Discussionmentioning

confidence: 99%

“…Although there is a significant burden of knowlesi malaria in both Sabah and Sarawak, including severe and fatal cases 6,[17][18][19][20] , data relating to the prevalence and clinical sequelae of this infection in humans in Indonesian Borneo are sparse. Only 12 confirmed cases have been reported to date [21][22][23][24][25] and all of these are from South and Central Kalimantan rather than the provinces of West and North Kalimantan bordering Malaysian Borneo.…”

mentioning

confidence: 99%

A survey of simian Plasmodium infections in humans in West Kalimantan, Indonesia

Sugiarto

Natalia

Mohamad

et al. 2022

Sci Rep

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The simian parasite Plasmodium knowlesi is the predominant species causing human malaria infection, including hospitalisations for severe disease and death, in Malaysian Borneo. By contrast, there have been only a few case reports of knowlesi malaria from Indonesian Borneo. This situation seems paradoxical since both regions share the same natural macaque hosts and Anopheles mosquito vectors, and therefore have a similar epidemiologically estimated risk of infection. To determine whether there is a true cross-border disparity in P. knowlesi prevalence, we conducted a community-based malaria screening study using PCR in Kapuas Hulu District, West Kalimantan. Blood samples were taken between April and September 2019 from 1000 people aged 6 months to 85 years attending health care facilities at 27 study sites within or close to jungle areas. There were 16 Plasmodium positive samples by PCR, five human malarias (two Plasmodium vivax, two Plasmodium ovale and one Plasmodium malariae) and 11 in which no species could be definitively identified. These data suggest that, if present, simian malarias including P. knowlesi are rare in the Kapuas Hulu District of West Kalimantan, Indonesian Borneo compared to geographically adjacent areas of Malaysian Borneo. The reason for this discrepancy, if confirmed in other epidemiologically similar regions of Indonesian Borneo, warrants further studies targeting possible cross-border differences in human activities in forested areas, together with more detailed surveys to complement the limited data relating to monkey hosts and Anopheles mosquito vectors in Indonesian Borneo.

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“…A population-based case-controlled study in Sabah, Malaysia previously showed that males were 2.71 times more prone to acquiring P. knowlesi malaria compared to females. Other risk factors include staying overnight in the forest, not using mosquito repellent, and a history of malaria infection [ 12 ]. Besides that, P. knowlesi transmission is driven by natural and human-induced environmental variations, as seen in Sabah, where the shrinking primary forest coverage has been linked to an increase in infections [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Inter-Population Genetic Diversity and Clustering of Merozoite Surface Protein-1 (pkmsp-1) of Plasmodium knowlesi Isolates from Malaysia and Thailand

et al. 2023

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The genetic diversity of pkmsp-1 of Malaysian Plasmodium knowlesi isolates was studied recently. However, the study only included three relatively older strains from Peninsular Malaysia and focused mainly on the conserved blocks of this gene. In this study, the full-length pkmsp-1 sequence of recent P. knowlesi isolates from Peninsular Malaysia was characterized, along with Malaysian Borneo and Thailand pkmsp-1 sequences that were retrieved from GenBank. Genomic DNA of P. knowlesi was extracted from human blood specimens and the pkmsp-1 gene was PCR-amplified, cloned, and sequenced. The sequences were analysed for genetic diversity, departure from neutrality, and geographical clustering. The pkmsp-1 gene was found to be under purifying/negative selection and grouped into three clusters via a neighbour-joining tree and neighbour net inferences. Of the four polymorphic blocks in pkmsp-1, block IV, was most polymorphic, with the highest insertion–deletion (indel) sites. Two allelic families were identified in block IV, thereby highlighting the importance of this block as a promising genotyping marker for the multiplicity of infection study of P. knowlesi malaria. A single locus marker may provide an alternate, simpler method to type P. knowlesi in a population.

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Risk factor of plasmodium knowlesi infection in Sabah Borneo Malaysia, 2020: A population-based case-control study

Cited by 14 publications

References 49 publications

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

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

A survey of simian Plasmodium infections in humans in West Kalimantan, Indonesia

Inter-Population Genetic Diversity and Clustering of Merozoite Surface Protein-1 (pkmsp-1) of Plasmodium knowlesi Isolates from Malaysia and Thailand

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