Multi-pronged surveillance to understand the spatiotemporal correlations among macaques, vectors and humans in Plasmodium knowlesi malaria transmission Article Swipe
Wei Kit Phang
,
Nantha Kumar Jeyaprakasam
,
Sandthya Pramasivan
,
Zailiza Suli
,
Jenn Zhueng Tam
,
Mohd Hafizi bin Abdul Hamid
,
Mohd Lutfi Abdullah
,
Anis Adlina Binti Isman Rohimly
,
Norsharina Binti Ashrat
,
Ting‐Wu Chuang
,
Wang Nguitragool
,
Indra Vythilingam
,
Yee Ling Lau
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13071-025-07082-6
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13071-025-07082-6
Background 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. Methods Field sampling data (both previously published and unpublished) collected from humans, macaques and mosquito vectors by this research group in Peninsular Malaysia between 2019 and 2022 were integrated.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). Results 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. Conclusions 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. Graphical abstract
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13071-025-07082-6
- https://parasitesandvectors.biomedcentral.com/counter/pdf/10.1186/s13071-025-07082-6
- OA Status
- gold
- References
- 42
- OpenAlex ID
- https://openalex.org/W4415658726
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415658726Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13071-025-07082-6Digital Object Identifier
- Title
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Multi-pronged surveillance to understand the spatiotemporal correlations among macaques, vectors and humans in Plasmodium knowlesi malaria transmissionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-29Full publication date if available
- Authors
-
Wei Kit Phang, Nantha Kumar Jeyaprakasam, Sandthya Pramasivan, Zailiza Suli, Jenn Zhueng Tam, Mohd Hafizi bin Abdul Hamid, Mohd Lutfi Abdullah, Anis Adlina Binti Isman Rohimly, Norsharina Binti Ashrat, Ting‐Wu Chuang, Wang Nguitragool, Indra Vythilingam, Yee Ling LauList of authors in order
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https://doi.org/10.1186/s13071-025-07082-6Publisher landing page
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https://parasitesandvectors.biomedcentral.com/counter/pdf/10.1186/s13071-025-07082-6Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://parasitesandvectors.biomedcentral.com/counter/pdf/10.1186/s13071-025-07082-6Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.sampling | 98, 143, 152, 160, 172, 191, 289 |
| abstract_inverted_index.temporal | 164, 215 |
| abstract_inverted_index.vectors, | 329 |
| abstract_inverted_index.vectors. | 47 |
| abstract_inverted_index.wildlife | 411 |
| abstract_inverted_index.zoonotic | 417, 435 |
| abstract_inverted_index.-positive | 232, 271 |
| abstract_inverted_index.Anopheles | 239 |
| abstract_inverted_index.Graphical | 452 |
| abstract_inverted_index.Malaysia. | 95 |
| abstract_inverted_index.analysis, | 207 |
| abstract_inverted_index.collected | 105, 127 |
| abstract_inverted_index.cynomolgi | 312 |
| abstract_inverted_index.different | 150, 194 |
| abstract_inverted_index.distances | 184 |
| abstract_inverted_index.essential | 431 |
| abstract_inverted_index.exhibited | 323 |
| abstract_inverted_index.increased | 398 |
| abstract_inverted_index.infection | 381, 402 |
| abstract_inverted_index.integrate | 73 |
| abstract_inverted_index.macaques, | 78 |
| abstract_inverted_index.non-human | 6, 44 |
| abstract_inverted_index.parasite, | 10 |
| abstract_inverted_index.proximity | 162, 213, 248, 286 |
| abstract_inverted_index.published | 102 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Peninsular | 94, 117 |
| abstract_inverted_index.Plasmodium | 2 |
| abstract_inverted_index.aggregated | 139 |
| abstract_inverted_index.constraint | 249 |
| abstract_inverted_index.controlled | 208 |
| abstract_inverted_index.highlights | 407 |
| abstract_inverted_index.historical | 175, 277 |
| abstract_inverted_index.indicating | 397 |
| abstract_inverted_index.infection. | 338 |
| abstract_inverted_index.infections | 29 |
| abstract_inverted_index.integrates | 60 |
| abstract_inverted_index.inter-host | 159, 210 |
| abstract_inverted_index.mosquitoes | 241 |
| abstract_inverted_index.parasites, | 310 |
| abstract_inverted_index.persistent | 391 |
| abstract_inverted_index.predicting | 414 |
| abstract_inverted_index.previously | 101 |
| abstract_inverted_index.proportion | 226, 267, 377 |
| abstract_inverted_index.understand | 86 |
| abstract_inverted_index.veterinary | 425 |
| abstract_inverted_index.Conclusions | 367 |
| abstract_inverted_index.Integrating | 420 |
| abstract_inverted_index.controlling | 157, 434 |
| abstract_inverted_index.correlation | 206, 223, 262 |
| abstract_inverted_index.difference, | 216 |
| abstract_inverted_index.difference. | 165 |
| abstract_inverted_index.effectively | 433 |
| abstract_inverted_index.integrated. | 124 |
| abstract_inverted_index.potentially | 396 |
| abstract_inverted_index.proportions | 340 |
| abstract_inverted_index.significant | 220, 260 |
| abstract_inverted_index.communities. | 405 |
| abstract_inverted_index.consistently | 258 |
| abstract_inverted_index.correlations | 146, 167 |
| abstract_inverted_index.distribution | 335 |
| abstract_inverted_index.epidemiology | 88 |
| abstract_inverted_index.geographical | 334 |
| abstract_inverted_index.particularly | 291, 330 |
| abstract_inverted_index.populations. | 451 |
| abstract_inverted_index.surveillance | 75, 412 |
| abstract_inverted_index.transmission | 34, 392 |
| abstract_inverted_index.unpublished) | 104 |
| abstract_inverted_index.comprehensive | 57 |
| abstract_inverted_index.epidemiology, | 423 |
| abstract_inverted_index.field-sampled | 228 |
| abstract_inverted_index.heterogeneous | 324 |
| abstract_inverted_index.individually, | 55 |
| abstract_inverted_index.mixed-species | 337 |
| abstract_inverted_index.multi-pronged | 74 |
| abstract_inverted_index.sampling-site | 211 |
| abstract_inverted_index.statistically | 219, 259, 355 |
| abstract_inverted_index.Spatiotemporal | 166 |
| abstract_inverted_index.Leucosphyrus-group | 240 |
| abstract_inverted_index.peridomestic–agriculture | 359 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5048607201 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I47519274 |
| citation_normalized_percentile |