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      No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data

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          Abstract

          Reported incidence of the zoonotic malaria Plasmodium knowlesi has markedly increased across Southeast Asia and threatens malaria elimination. Nonzoonotic transmission of P. knowlesi has been experimentally demonstrated, but it remains unknown whether nonzoonotic transmission is contributing to increases in P. knowlesi cases. Here, we adapt model-based inference methods to estimate R C , individual case reproductive numbers, for P. knowlesi, P. falciparum and P. vivax human cases in Malaysia from 2012–2020 (n = 32,635). Best fitting models for P. knowlesi showed subcritical transmission ( R C  < 1) consistent with a large reservoir of unobserved infection sources, indicating P. knowlesi remains a primarily zoonotic infection. In contrast, sustained transmission ( R C  > 1) was estimated historically for P. falciparum and P. vivax, with declines in R C estimates observed over time consistent with local elimination. Together, this suggests sustained nonzoonotic P. knowlesi transmission is highly unlikely and that new approaches are urgently needed to control spillover risks.

          Abstract

          Plasmodium knowlesi is a zoonotic malaria parasite that can infect humans, but whether human-mosquito-human transmission occurs is not known. Here, the authors use data from Malaysia and show, through mathematical modelling, that sustained non-zoonotic transmission is unlikely to be occurring in this setting.

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          Most cited references39

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          Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

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            Pathways to zoonotic spillover

            Zoonotic diseases present a substantial global health burden. In this Opinion article, Plowrightet al. present an integrative conceptual and quantitative model that reveals that all zoonotic pathogens must overcome a hierarchical series of barriers to cause spillover infections in humans. Supplementary information The online version of this article (doi:10.1038/nrmicro.2017.45) contains supplementary material, which is available to authorized users.
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              Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures

              Abstract Severe acute respiratory syndrome (SARS) has been the first severe contagious disease to emerge in the 21st century. The available epidemic curves for SARS show marked differences between the affected regions with respect to the total number of cases and epidemic duration, even for those regions in which outbreaks started almost simultaneously and similar control measures were implemented at the same time. The authors developed a likelihood-based estimation procedure that infers the temporal pattern of effective reproduction numbers from an observed epidemic curve. Precise estimates for the effective reproduction numbers were obtained by applying this estimation procedure to available data for SARS outbreaks that occurred in Hong Kong, Vietnam, Singapore, and Canada in 2003. The effective reproduction numbers revealed that epidemics in the various affected regions were characterized by markedly similar disease transmission potentials and similar levels of effectiveness of control measures. In controlling SARS outbreaks, timely alerts have been essential: Delaying the institution of control measures by 1 week would have nearly tripled the epidemic size and would have increased the expected epidemic duration by 4 weeks.
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                Author and article information

                Contributors
                Kimberly.Fornace@lshtm.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 June 2023
                1 June 2023
                2023
                : 14
                : 2945
                Affiliations
                [1 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, School of Biodiversity, One Health and Veterinary Medicine, , University of Glasgow, ; Glasgow, UK
                [2 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Saw Swee Hock School of Public Health, , National University of, ; Singapore, Singapore
                [3 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Faculty of Infectious and Tropical Diseases, , London School of Hygiene and Tropical Medicine, ; London, UK
                [4 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, MRC Centre for Global Infectious Disease Analysis, , Imperial College London, ; London, UK
                [5 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, University of California, San Francisco, ; San Francisco, USA
                [6 ]GRID grid.265727.3, ISNI 0000 0001 0417 0814, Faculty of Medicine and Health Sciences, , Universiti Malaysia Sabah, ; Kota Kinabalu, Malaysia
                [7 ]GRID grid.415759.b, ISNI 0000 0001 0690 5255, Vector-borne Disease Control Division, , Ministry of Health Malaysia, ; Putrajaya, Malaysia
                [8 ]GRID grid.3575.4, ISNI 0000000121633745, Global Malaria Programme, , World Health Organization, ; Geneva, Switzerland
                [9 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Section of Epidemiology, , University of Copenhagen, ; Copenhagen, Denmark
                Author information
                http://orcid.org/0000-0002-5484-241X
                http://orcid.org/0000-0002-5593-0470
                http://orcid.org/0000-0002-0373-4451
                http://orcid.org/0000-0002-1869-3701
                http://orcid.org/0000-0002-0007-4910
                http://orcid.org/0000-0003-4863-075X
                Article
                38476
                10.1038/s41467-023-38476-8
                10235043
                37263994
                0c4699a5-cd3c-40e4-b58e-a594eb70f4af
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 November 2022
                : 2 May 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004423, World Health Organization (WHO);
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 221963/Z/20/Z
                Award ID: 220900/Z/20/Z
                Award ID: 220900/Z/20/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000288, Royal Society;
                Award ID: 221963/Z/20/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MR/R015600/1
                Award ID: MR/R015600/1
                Award ID: MR/R015600/1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100009708, Novo Nordisk Fonden (Novo Nordisk Foundation);
                Award ID: NNF20OC0059309
                Award Recipient :
                Funded by: Eric and Wendy Schmidt Fund for Strategic Innovation via the Schmidt Polymath Award (G-22-63345)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                computational models,malaria,epidemiology
                Uncategorized
                computational models, malaria, epidemiology

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