Spatial distribution of Plasmodium knowlesi cases and their vectors in Johor, Malaysia: in light of human malaria elimination

Abstract

Background Plasmodium knowlesi, a simian malaria parasite infection, increases as Plasmodium falciparum and Plasmodium vivax infections decrease in Johor, Malaysia. Therefore, this study aimed to identify the distribution of vectors involved in knowlesi malaria transmission in Johor. This finding is vital in estimating hotspot areas for targeted control strategies. Methods Anopheles mosquitoes were collected from the location where P. knowlesi cases were reported. Cases of knowlesi malaria from 2011 to 2019 in Johor were analyzed. Internal transcribed spacers 2 (ITS2) and cytochrome c oxidase subunit I (COI) genes were used to identify the Leucosphyrus Group of Anopheles mosquitoes. In addition, spatial analysis was carried out on the knowlesi cases and vectors in Johor. Results One hundred and eighty-nine cases of P. knowlesi were reported in Johor over 10 years. Young adults between the ages of 20–39 years comprised 65% of the cases. Most infected individuals were involved in agriculture and army-related occupations (22% and 32%, respectively). Four hundred and eighteen Leucosphyrus Group Anopheles mosquitoes were captured during the study. Anopheles introlatus was the predominant species, followed by Anopheles latens. Spatial analysis by Kriging interpolation found that hotspot regions of P. knowlesi overlapped or were close to the areas where An. introlatus and An. latens were found. A significantly high number of vectors and P. knowlesi cases were found near the road within 0–5 km. Conclusions This study describes the distribution of P. knowlesi cases and Anopheles species in malaria-endemic transmission areas in Johor. Geospatial analysis is a valuable tool for studying the relationship between vectors and P. knowlesi cases. This study further supports that the Leucosphyrus Group of mosquitoes might be involved in transmitting knowlesi malaria cases in Johor. These findings may provide initial evidence to prioritize diseases and vector surveillance.

Multiplex PCR Assay for the Identification of Four Species of the Anopheles Leucosphyrus Sub-Group in Malaysia

“…For accurate species identification, molecular markers, particularly the rRNA ITS2 gene, are used to distinguish between sibling species in several Asian Anopheles complexes such as An. minimus group [46], An. hycarnus group [47], An.…”

“…latens clade I and An. latens clade II [46] used in the study were collected from different states in Malaysia, namely Johor, Kelantan, Pahang, Selangor, Sabah, and Sarawak, where most simian malaria infections were reported [47] (Figure 1) (Table S1). Mosquitoes were collected using bare-leg capture (BLC) [48] as well as human baited trap, CDC light trap, and mosquito magnet from 18:00 to 23:30 as described in the work of [49].…”

“…ITS2 DNA sequences were used to design Anopheles species-specific primers in this study [44][45][46][47][48][49][51][52][53][54][55][56][57]. Single-round multiplex PCR was designed based on species-specific variations in the sequences of the ITS2, a ribosomal DNA gene (rDNA) commonly used to distinguish cryptic Anopheles species, particularly those belonging to Asian complexes and groups [44,45,[51][52][53][54][55][56][57].…”

Perspectives of vector management in the control and elimination of vector-borne zoonoses

“…The clearance of one vector by the bacteria may allow other vectors to thrive, rendering the disease control futile. Knowlesi malaria is an example of VBZ with multiple vectors, and the list of knowlesi malaria vectors is expanding for the moment ( Ang et al, 2020 ; Jeyaprakasam et al, 2020 ; Pramasivan et al, 2021 ; Vythilingam et al, 2021 ). Apart from this, the actual efficacy of Wolbachia approach to reduce disease transmission has been questioned.…”

Social divisions and risk perception can drive divergent epidemic dynamics and large second and third waves

“…Populations may be subdivided based on an array of factors (e.g., race, ethnicity, age, and geography), with marked differences in pathogen exposure and infection severity (17,(19)(20)(21)(22)(23). Risk of pathogen introduction may vary between groups: high income groups may encounter pathogens endemic to other regions through international travel, low income groups may have heightened likelihood of exposure connected to poor housing quality and insufficient occupational protections, and certain regions and occupations experience greater risks of exposure to zoonotic illnesses (19,(24)(25)(26)(27). Once a pathogen is introduced, it may spread at different rates within groups based on factors like housing density and access to healthcare (20,24,28).…”