Nonhuman primate (NHP) malaria threatens Southeast Asia’s 2030 elimination goals as human infections rise across the region. Identifying the correct vectors is important for controlling the disease. This forum advocates for integrating dissection with polymerase chain reaction to ensure precise identification of the vectors driving NHP malaria infections in humans.

Thailand has achieved significant progress in malaria elimination, with a reduction in annual parasite incidence from 0.53 to 0.22 per thousand in 2014 and 2024, respectively. Given the high diversity of Anopheles mosquito species, elimination efforts must be precisely targeted, taking into account the varied behaviors and vectorial capacities of different vector species. This study aims to systematically review and update the distribution, identification, bionomics, behavior, and a meta-analysis of nonhuman parasite infectivity among mosquitoes. A comprehensive literature search was conducted in PubMed, Scopus, EBSCOhost, and Google Scholar (2013–2025) to identify studies on Anopheles species diversity, distribution, and zoonotic malaria infection in mosquitoes. The meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was carried out using the metafor package in R. A total of 92 relevant papers were included from 811 accessed articles. Of these, most documented geographical distribution, followed by mosquito behaviors, molecular identification, and mosquito infectivity. The pooled mosquito infection prevalence for the present meta-analysis was 0.01 (95

Plasmodium knowlesi, a non-human primate (NHP) malaria parasite, has become a major public health concern in Malaysia and is now the leading cause of human malaria infections in the country. The transmission of P. knowlesi involves a complex cycle among humans, non-human primates and vectors. Numerous studies have focused on these hosts individually, but comprehensive research that integrates field data from all three hosts is lacking. This study aims to integrate multi-pronged surveillance data from macaques, vectors and human blood samples to better understand the epidemiology of P. knowlesi malaria in Peninsular Malaysia. Field sampling data (both previously published and unpublished) collected from humans, macaques and mosquito vectors by this research group in Peninsular Malaysia between 2019 and 2022 were integrated. The data collected for each host type within the same site and month were aggregated as a single sampling event. Partial correlations of outcomes between different host sampling sites were analysed by controlling for inter-host sampling site proximity and temporal difference. Spatiotemporal correlations were analysed between the sampling outcomes and historical human P. knowlesi malaria cases reported within defined distances (up to 20 km) from the sampling sites across different time lead windows (range from −12 to 12 months). Partial correlation analysis, controlled for inter-host sampling-site spatial proximity and temporal difference, showed a statistically significant positive partial correlation between the proportion of field-sampled human P. knowlesi-positive cases and the average number of Anopheles Leucosphyrus-group mosquitoes sampled per night within a 10-km proximity constraint (rs = 0.228, P = 0.042). A consistently statistically significant positive correlation was found between the proportion of P. knowlesi-positive macaques and the number of historical human P. knowlesi cases reported in defined spatial proximity to macaque sampling sites, particularly within spatial radii of 6 km and beyond, across both backward and forward time leads. Other NHP malaria parasites, P. cynomolgi, P. inui, P. coatneyi and P. fieldi, exhibited heterogeneous patterns in macaques and vectors, particularly in terms of geographical distribution and mixed-species infection. The proportions of macaque samples positive for P. knowlesi, P. inui and P. coatneyi were statistically higher in the peridomestic–agriculture area as compared with the urban area. A key finding from this study is that the proportion of P. knowlesi infection in macaques may serve as a useful proxy for persistent transmission in an area, potentially indicating increased risk of human infection in nearby communities. This highlights the value of wildlife surveillance in predicting and managing zoonotic malaria risk. Integrating insights from epidemiology, ecology, veterinary science and public health is essential for effectively controlling zoonotic diseases such as P. knowlesi malaria and reducing their impact on both human and animal populations.

Aedes aegypti mosquitoes have been incriminated as the vectors of several medically important pathogens. An important vector control strategy against Ae. aegypti is the release of Ae. aegypti infected with Wolbachia bacteria. The bacteria can either suppress the population of mosquitoes, or replace the mosquito population with Wolbachia-infected mosquitoes, which are believed to hamper the transmission of several pathogens. However, the fitness cost of Wolbachia infection on the different stages of mosquito under different temperature settings remains to be evaluated thoroughly. Here, the effect of cool (20 degrees C), standard ambient (25 degrees C), warm (30 degrees C), and hot (35 degrees C) water temperatures on the immatures of wAlbB-infected Ae. aegypti (WIA) and wAlbB-uninfected Ae. aegypti (WUA) were evaluated, to determine the feasibility and sustainability of the Wolbachia-based vector control method. The egg-hatching, pupation, and adult emergence of WIA and WUA were significantly affected by temperatures, with WIA demonstrating significantly lower thermotolerance than WUA. The lower success of metamorphosis by WIA than WUA at 30 degrees C raises concern about the feasibility of Ae. aegypti population replacement by WIA in the tropics, especially during the heatwave episodes experienced by many areas in 2024. The Wolbachia-based vector control strategy may be more suitable for areas with lower ambient temperature.

Lymphatic filariasis is a neglected tropical disease of public health concern targeted for elimination globally. Malaysia is endemic to filariasis caused mainly by the filarial parasite Brugia malayi, with decades of continues elimination efforts. Despite recorded success, the disease is yet to be eliminated. Recently, reinfection in regions following mass drug administration programs and resurgence in some parts of the country raises concern as the country geared towards the 2030 filariasis elimination target. This study aims to provide pool prevalence estimates of the disease in animals and humans in Malaysia using a proportionate meta-analysis. Recent epidemiolocal data, potential filaria hotspots and the role of human induced environmental degradation on zoonotic filariasis transmission are also discussed. A Generalized Linear Mixed Model (GLMM) was used for the proportionate meta-analysis of prevalence data from 12 included studies. The result reveals overall human zoonotic filariasis estimated pool prevalence of 3% [95% CI: 0.01-0.09] and 5% [95% CI = 0.01-0.17] among animals in Malaysia, with a significant between study heterogeneity (I² = 97%; I² = 94%, p < 0.001, respectively). A subgroup meta-analysis of animal prevalence reveals high common effect estimated prevalence among monkeys 50% [95% CI = 0.43-0.58] with a random effect of 9% [0.00-0.94], with no observed between study heterogeneity (I² = 0%, p = 1). This study provides insight into zoonotic brugian filariasis that can be useful for the development of effective and sustainable lymphatic filariasis elimination program in Malaysia and other filarial endemic regions.

The increasing burden of non-human primates (NHP) malaria, driven primarily by Plasmodium knowlesi, poses a growing public health threat in many countries across Southeast Asia. Compounding this challenge, the emergence of other NHP Plasmodium species infecting humans, including P. cynomolgi, P. inui, and P. fieldi, introduces additional complexity to malaria elimination efforts, particularly in countries like Malaysia, Thailand, and Vietnam. A complex interplay among human populations, vector dynamics, and environmental factors influences the transmission and prevalence of this disease. This narrative review delves into the current vectors of NHP malaria in Southeast Asia, highlighting the crucial role of environmental determinants in shaping the bionomics of Anopheles mosquito vectors. Key environmental factors, such as temperature fluctuations, relative humidity, elevation, precipitation patterns, seasonality, and land use, play a pivotal role in shaping vector abundance and survival, ultimately influencing the transmission intensity of zoonotic malaria. Adopting a One Health approach, which recognizes the interconnections between human, animal, and environmental health, is crucial for unravelling these complex dynamics. Advancing this integrated framework will require continued research and understanding of vector ecology across diverse environmental settings and geographical regions. This review provides comprehensive information on vector bionomics in relation to the changing environmental factors, besides highlighting the importance of a multidisciplinary strategy that integrates vector surveillance, sustainable land management, and targeted public health interventions to inform effective, evidence-based malaria control efforts in Southeast Asia.

The reported cases of Plasmodium cynomolgi in Southeast Asia pose a significant public health concern. Sporadic reports of human Plasmodium cynomolgi infections have increased in the past few years, raising attention regarding its potential impact on human populations. Further compounding this issue are the morphological similarities between P. cynomolgi and the human malaria parasite Plasmodium vivax, which may lead to misdiagnosis and underreporting of P. cynomolgi infections. Both in vitro and in vivo studies have shown that P. cynomolgi can effectively invade human reticulocytes using mechanisms like those employed by P. vivax, underscoring its capacity to infect human hosts if given the opportunity. These studies collectively highlight the parasite's potential to establish infections in humans and emphasize the need for molecular diagnostic tools to accurately detect P. cynomolgi. Additionally, challenges in accurate diagnosis and surveillance systems may underestimate the true extent of their impact, making it imperative for healthcare authorities to bolster monitoring efforts and deploy targeted interventions. Strengthening surveillance, improving diagnostic capabilities, and developing targeted vector control strategies are crucial to mitigating the risk of P. cynomolgi becoming a major zoonotic disease like its counterpart, Plasmodium knowlesi. Thus, this review aims to highlight the current understanding of P. cynomolgi infections in human, vector, and macaque hosts based on collated data from previous studies while underscoring the urgent need for enhanced surveillance, accurate diagnostic tools, and effective vector control strategies to mitigate its potential as a significant zoonotic threat in Southeast Asia.

Studies have suggested animals as possible reservoir hosts for flaviviruses transmitted by Aedes mosquitoes; however, there is limited evidence for the dengue virus in Malaysia. One of the possible ways to determine the zoonotic potential for any pathogen transmission is through blood meal analysis which can provide valuable insights into the feeding preferences of the mosquitoes. Unfortunately, limited information is available on the feeding preferences of Aedes mosquitoes in Malaysia. Thus, this study aimed to identify the blood-feeding preferences of Aedes aegypti and Aedes albopictus from different ecotypes in Selangor, Malaysia. The field mosquitoes were collected using a modified backpack aspirator and CDC light trap. The collected mosquitoes were initially classified based on degrees of blood digestion according to the Sella scale before extracting the DNA. The presence of vertebrate DNA was detected using nested PCR, and samples positive for vertebrate DNA were further subjected to species-specific PCR targeting the common animals found at the study locations. In general, 51 of 187 field caught Aedes mosquitoes were positive for the presence of vertebrate DNA in their blood meal. The most frequent blood meal source was human (38.2%), followed by monkey (12.7%), bovine (10.9%), chicken (7.3%) and dog (3.6%). The human blood index (HBI) of Ae. albopictus collected across the four different ecotypes revealed that, Ae. albopictus collected near human dwellings showed 100% anthropophilic tendency. Interestingly, there were two Aedes mosquitoes, Ae. aegypti (n=1) and Ae. albopictus (n=1) positive for both human and monkey blood. Since sylvatic dengue continues to flourish in Southeast Asia, this finding from blood meal analysis shows the potential for zoonotic transmission by Aedes mosquitoes in these locations. However, further research must be carried out to understand the role of animals as potential reservoir hosts for the dengue virus, especially through the detection of the virus in the blood meal.

BackgroundIn 2008-2010, Malaysia experienced a nationwide chikungunya virus (CHIKV) outbreak caused by the Indian Ocean lineage E1-226V (valine) variant, adapted to Aedes albopictus. In 2017-2022, transition to an E1-226A (alanine) variant occurred. Ae. albopictus prevails in rural areas, where most cases occurred during the E1-226V outbreak, while Ae. aegypti dominates urban areas. The shift in circulating CHIKV variants from E1-226V to E1-226A (2009-2022) was hypothesized to result in a transition from rural to urban CHIKV distribution, driven by differences in Ae. aegypti vector competence for the two variants. This study aimed to: (1) map the spatiotemporal spread of CHIKV cases in Malaysia between 2009-2022; and (2) compare replication of E1-226A and E1-226V variants in the midguts and head/thoraxes of Ae. aegypti.Methodology/principal findingsSpatiotemporal analysis of national notified CHIKV case addresses was performed. Between 2009-2022, 12,446 CHIKV cases were reported, with peaks in 2009 and 2020, and a significant shift from predominantly rural cases in 2009-2011 (85.1% rural), to urban areas in 2017-2022 (86.1% urban; p<0.0001). Two Ae. aegypti strains, field-collected MC1 and laboratory Kuala Lumpur (KL) strains, were fed infectious blood containing constructed CHIKV clones, pCMV-p2020A (E1-226A) and pCMV-p2020V (E1-226V) to measure CHIKV replication by real-time PCR and/or virus titration. The pCMV-p2020A clone replicated better in Ae. aegypti cell line Aag2 and showed higher replication, infection and dissemination efficiency in both Ae. aegypti strains, compared to pCMV-p2020V.Conclusions/significanceThis study revealed that a change in circulating CHIKV variants can be associated with changes in vector competence and outbreak epidemiology. Continued genomic surveillance of arboviruses is important.

Despite the persistency of World Health Organization to eliminate malaria since 1987, malaria disease continues to pose a significant threat to global health. As the severity of malaria persists over the years, there is a critical need for an automated diagnosis system for more efficient diagnosis and effective treatment administration. To mitigate the increase in mosquito-borne diseases, there has been a heightened interest in the application of Artificial intelligence (AI), specifically deep learning. With the assistance of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework, an extensive review of current state of automated malaria diagnosis systems utilizing machine learning and deep learning approaches was performed across eight scientific databases, with 50 articles shortlisted from the years 2015-2023. Besides, identifying the research gaps, we synthesise the existing literature, analyse the outcomes, and explore the critical parameters that influence model performance. From the review, the prevailing models primarily focus on binary classification while disregarding cross-dataset validations and multi-stage classification. This gap challenges the delivering effective treatments, especially considering potential drug resistance. Established protocols and classification models are needed to anticipate the specific malaria species. The keywords in automated malaria diagnosis that we identified include machine learning, deep learning, transfer learning, and convolutional neural networks. Through examinations of the constraints in current methodologies, we provide valuable suggestions that could propel the field of automated malaria diagnosis. This systematic review provides a comprehensive overview, critical insights, and a roadmap for future research endeavours in this vital domain of healthcare.

Malaria continues to be a global public health problem although it has been eliminated from many countries. Sri Lanka and China are two countries that recently achieved malaria elimination status, and many countries in Southeast Asia are currently in the pipeline for achieving the same goal by 2030. However, Plasmodium knowlesi, a non-human primate malaria parasite continues to pose a threat to public health in this region, infecting many humans in all countries in Southeast Asia except for Timor-Leste. Besides, other non-human primate malaria parasite such as Plasmodium cynomolgi and Plasmodium inui are infecting humans in the region. The non-human primates, the long-tailed and pig-tailed macaques which harbour these parasites are now increasingly prevalent in farms and forest fringes close by to the villages. Additionally, the Anopheles mosquitoes belonging to the Lecuosphyrus Group are also present in these areas which makes them ideal for transmitting the non-human primate malaria parasites. With changing landscape and deforestation, non-human primate malaria parasites will affect more humans in the coming years with the elimination of human malaria. Perhaps due to loss of immunity, more humans will be infected as currently being demonstrated in Malaysia. Thus, control measures need to be instituted rapidly to achieve the malaria elimination status by 2030. However, the zoonotic origin of the parasite and the changes of the vectors behaviour to early biting seems to be the stumbling block to the malaria elimination efforts in this region. In this review, we discuss the challenges faced in malaria elimination due to deforestation and the serious threat posed by non-human primate malaria parasites.

Insect vectors pose one of the greatest threats to human health. In 2017, recognizing the importance of vector control, the World Health Organization adopted the Global Vector Control Response (GVCR) initiative to provide strategic guidance to countries and strengthen vector control approaches. In this Voices, we ask: what progress has been made, and what barriers must still be overcome, to achieve the goals set out by the GVCR by 2030?

Abstract Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

The gold standard for diagnosing malaria remains microscopic examination; however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ $0.5=96$ %) than YOLOv4 (mAP@ $0.5=89$ %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ $0.5=90$ %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4’s localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.

Wing measurement is an important parameter in many entomological studies. However, the methods of measuring wings vary with studies, and a gold standard method was not available for this procedure. This in turn limits researchers from confidently comparing their research findings with published data collected by other means of measurement. This study investigated the interchangeability of three commonly available methods for wing measurement, namely the calliper method, stereomicroscope-assisted photography method, and digital microscope-assisted photography method, using the laboratory colony of Aedes aegypti. It was found that the calliper method and the photography-based methods yielded similar results, hence the good interchangeability of these methods. Nevertheless, the digital microscope-assisted photography method yielded more accurate measurements, due to the higher resolution of the captured photos, and minimal technical bias during the data collection, as compared to the calliper-based and stereomicroscope-assisted photography methods. This study served as a reference for researchers to select the most suitable measurement method in future studies.

The complex transmission profiles of vector-borne zoonoses (VZB) and vector-borne infections with animal reservoirs (VBIAR) complicate efforts to break the transmission circuit of these infections. To control and eliminate VZB and VBIAR, insecticide application may not be conducted easily in all circumstances, particularly for infections with sylvatic transmission cycle. As a result, alternative approaches have been considered in the vector management against these infections. In this review, we highlighted differences among the environmental, chemical, and biological control approaches in vector management, from the perspectives of VZB and VBIAR. Concerns and knowledge gaps pertaining to the available control approaches were discussed to better understand the prospects of integrating these vector control approaches to synergistically break the transmission of VZB and VBIAR in humans, in line with the integrated vector management (IVM) developed by the World Health Organization (WHO) since 2004.

Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effectiveness depends on the trained microscopist's skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to determine the most suitable deep-learning object detection architecture and their applicability to detect and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and from four stages of infection with 80/20 train and test data partition. The performance of object detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The best-performing model was also assessed with an independent dataset to verify the models' ability to generalize in different domains. The results show that upon training, the Yolov4 model achieves a precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin blood smear images. Object detectors can complement a deep learning classification model in detecting infected cells since they eliminate the need to train on single-cell images and have been demonstrated to be more feasible for a different target domain.

BACKGROUND:The elimination of malaria in Southeast Asia has become more challenging as a result of rising knowlesi malaria cases. In addition, naturally occurring human infections with other zoonotic simian malaria caused by Plasmodium cynomolgi and Plasmodium inui adds another level of complexity in malaria elimination in this region. Unfortunately, data on vectors which are responsible for transmitting this zoonotic disease is very limited.METHODOLOGY/PRINCIPAL FINDINGS:We conducted longitudinal studies to investigate the entomological parameters of the simian malaria vectors and to examine the genetic diversity and evolutionary pattern of their simian Plasmodium. All the captured Anopheles mosquitoes were dissected to examine for the presence of oocysts, sporozoites and to determine the parous rate. Our study revealed that the Anopheles Leucosphyrus Group mosquitoes are highly potential competent vectors, as evidenced by their high rate of parity, survival and sporozoite infections in these mosquitoes. Thus, these mosquitoes represent a risk of human infection with zoonotic simian malaria in this region. Haplotype analysis on P. cynomolgi and P. inui, found in high prevalence in the Anopheles mosquitoes from this study, had shown close relationship between simian Plasmodium from the Anopheles mosquitoes with its vertebrate hosts. This directly signifies the ongoing transmission between the vector, macaques, and humans. Furthermore, population genetic analysis showed significant negative values which suggest that both Plasmodium species are undergoing population expansion.CONCLUSIONS/SIGNIFICANCE:With constant microevolutionary processes, there are potential for both P. inui and P. cynomolgi to emerge and spread as a major public health problem, following the similar trend of P. knowlesi. Therefore, concerted vector studies in other parts of Southeast Asia are warranted to better comprehend the transmission dynamics of this zoonotic simian malaria which eventually would aid in the implementation of effective control measures in a rapidly changing environment.