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Punyapornwithaya V, Salvador R, Modethed W, Arjkumpa O, Jarassaeng C, Limon G, Gubbins S. Estimating the Transmission Kernel for Lumpy Skin Disease Virus from Data on Outbreaks in Thailand in 2021. Viruses 2023; 15:2196. [PMID: 38005874 PMCID: PMC10675364 DOI: 10.3390/v15112196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/26/2023] Open
Abstract
Nationwide outbreaks of lumpy skin disease (LSD) were observed in Thailand in 2021. A better understanding of its disease transmission is crucial. This study utilized a kernel-based approach to characterize the transmission of LSD between cattle herds. Outbreak data from the Khon Kaen and Lamphun provinces in Thailand were used to estimate transmission kernels for each province. The results showed that the majority of herd-to-herd transmission occurs over short distances. For Khon Kaen, the median transmission distance from the donor herd was estimated to be between 0.3 and 0.8 km, while for Lamphun, it ranged from 0.2 to 0.6 km. The results imply the critical role that insects may play as vectors in the transmission of LSD within the two study areas. This is the first study to estimate transmission kernels from data on LSD outbreaks in Thailand. The findings from this study offer valuable insights into the spatial transmission of this disease, which will be useful in developing prevention and control strategies.
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Affiliation(s)
- Veerasak Punyapornwithaya
- Research Center of Veterinary Biosciences and Veterinary Public Health, Chiang Mai University, Chiang Mai 50100, Thailand;
- Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand;
| | - Roderick Salvador
- College of Veterinary Science and Medicine, Central Luzon State University, Science City of Muñoz, Nueva Ecija 3120, Philippines;
| | - Wittawat Modethed
- Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand;
| | - Orapun Arjkumpa
- Animal Health Section, The 4th Regional Livestock Office, Department of Livestock Development, Khon Kaen 40260, Thailand;
| | - Chaiwat Jarassaeng
- Faculty of Veterinary Medicine, Khon Kaen University, Khon Kaen 40002, Thailand;
| | - Georgina Limon
- The Pirbright Institute, Pirbright, Surrey GU24 0NF, UK;
| | - Simon Gubbins
- The Pirbright Institute, Pirbright, Surrey GU24 0NF, UK;
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Lambert S, Bauzile B, Mugnier A, Durand B, Vergne T, Paul MC. A systematic review of mechanistic models used to study avian influenza virus transmission and control. Vet Res 2023; 54:96. [PMID: 37853425 PMCID: PMC10585835 DOI: 10.1186/s13567-023-01219-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 09/05/2023] [Indexed: 10/20/2023] Open
Abstract
The global spread of avian influenza A viruses in domestic birds is causing increasing socioeconomic devastation. Various mechanistic models have been developed to better understand avian influenza transmission and evaluate the effectiveness of control measures in mitigating the socioeconomic losses caused by these viruses. However, the results of models of avian influenza transmission and control have not yet been subject to a comprehensive review. Such a review could help inform policy makers and guide future modeling work. To help fill this gap, we conducted a systematic review of the mechanistic models that have been applied to field outbreaks. Our three objectives were to: (1) describe the type of models and their epidemiological context, (2) list estimates of commonly used parameters of low pathogenicity and highly pathogenic avian influenza transmission, and (3) review the characteristics of avian influenza transmission and the efficacy of control strategies according to the mechanistic models. We reviewed a total of 46 articles. Of these, 26 articles estimated parameters by fitting the model to data, one evaluated the effectiveness of control strategies, and 19 did both. Values of the between-individual reproduction number ranged widely: from 2.18 to 86 for highly pathogenic avian influenza viruses, and from 4.7 to 45.9 for low pathogenicity avian influenza viruses, depending on epidemiological settings, virus subtypes and host species. Other parameters, such as the durations of the latent and infectious periods, were often taken from the literature, limiting the models' potential insights. Concerning control strategies, many models evaluated culling (n = 15), while vaccination received less attention (n = 6). According to the articles reviewed, optimal control strategies varied between virus subtypes and local conditions, and depended on the overall objective of the intervention. For instance, vaccination was optimal when the objective was to limit the overall number of culled flocks. In contrast, pre-emptive culling was preferred for reducing the size and duration of an epidemic. Early implementation consistently improved the overall efficacy of interventions, highlighting the need for effective surveillance and epidemic preparedness.
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Affiliation(s)
| | - Billy Bauzile
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | | | - Benoit Durand
- Epidemiology Unit, Laboratory for Animal Health, French Agency for Food, Environment and Occupational Health and Safety (ANSES), Paris-Est University, Maisons-Alfort, France
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Bauzile B, Durand B, Lambert S, Rautureau S, Fourtune L, Guinat C, Andronico A, Cauchemez S, Paul MC, Vergne T. Impact of palmiped farm density on the resilience of the poultry sector to highly pathogenic avian influenza H5N8 in France. Vet Res 2023; 54:56. [PMID: 37430292 DOI: 10.1186/s13567-023-01183-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/22/2023] [Indexed: 07/12/2023] Open
Abstract
We analysed the interplay between palmiped farm density and the vulnerability of the poultry production system to highly pathogenic avian influenza (HPAI) H5N8. To do so, we used a spatially-explicit transmission model, which was calibrated to reproduce the observed spatio-temporal distribution of outbreaks in France during the 2016-2017 epidemic of HPAI. Six scenarios were investigated, in which the density of palmiped farms was decreased in the municipalities with the highest palmiped farm density. For each of the six scenarios, we first calculated the spatial distribution of the basic reproduction number (R0), i.e. the expected number of farms a particular farm would be likely to infect, should all other farms be susceptible. We also ran in silico simulations of the adjusted model for each scenario to estimate epidemic sizes and time-varying effective reproduction numbers. We showed that reducing palmiped farm density in the densest municipalities decreased substantially the size of the areas with high R0 values (> 1.5). In silico simulations suggested that reducing palmiped farm density, even slightly, in the densest municipalities was expected to decrease substantially the number of affected poultry farms and therefore provide benefits to the poultry sector as a whole. However, they also suggest that it would not have been sufficient, even in combination with the intervention measures implemented during the 2016-2017 epidemic, to completely prevent the virus from spreading. Therefore, the effectiveness of alternative structural preventive approaches now needs to be assessed, including flock size reduction and targeted vaccination.
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Affiliation(s)
- Billy Bauzile
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Benoit Durand
- Laboratory for Animal Health, French Agency for Food, Environmental and Occupational Health and Safety (ANSES), University Paris-Est, 14 rue Pierre et Marie Curie, 94700, Maisons-Alfort, France
| | | | | | - Lisa Fourtune
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Claire Guinat
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Alessio Andronico
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris Cité, CNRS UMR2000, 75015, Paris, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris Cité, CNRS UMR2000, 75015, Paris, France
| | | | - Timothée Vergne
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France.
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Moonchai S, Himakalasa A, Rojsiraphisal T, Arjkumpa O, Panyasomboonying P, Kuatako N, Buamithup N, Punyapornwithaya V. Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data, 2021-2022. Infect Dis Model 2023; 8:282-293. [PMID: 36915647 PMCID: PMC10006505 DOI: 10.1016/j.idm.2023.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Lumpy skin disease (LSD) is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa, Europe and Asia. In 2021, LSD outbreaks have been reported in almost all of Thailand's provinces. Indeed, fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling. Thus, the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models. The first- and second-order models in the forms of Lorentzian, Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth, Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases. Based on the root-mean-squared error (RMSE) and Akaike information criterion (AIC), results showed that both first and second orders of Pearson-type VII models and Richard's growth model (RGM) were fit to the data better than other models used in the present study. The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand. For disease preparedness purposes, we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down. Furthermore, the period when the growth rate changes at a slower rate, known as the inflection time, obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized. This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand. This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.
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Affiliation(s)
- Sompop Moonchai
- Advanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai, 50200, Thailand.,Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Adsadang Himakalasa
- Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Thaned Rojsiraphisal
- Advanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai, 50200, Thailand.,Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Data Science Research Center, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Orapun Arjkumpa
- The 4th Regional Livestock Office, Department of Livestock Development, Khon Kaen, 40206, Thailand
| | - Pawares Panyasomboonying
- Bureau of Disease Control and Veterinary Services, Department of Livestock Development, Bangkok, 10400, Thailand
| | - Noppasorn Kuatako
- Bureau of Disease Control and Veterinary Services, Department of Livestock Development, Bangkok, 10400, Thailand
| | - Noppawan Buamithup
- Bureau of Disease Control and Veterinary Services, Department of Livestock Development, Bangkok, 10400, Thailand
| | - Veerasak Punyapornwithaya
- Center of Excellence in Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand.,Department of Food Animal Clinics, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
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Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information. Pathogens 2021; 10:pathogens10060691. [PMID: 34199439 PMCID: PMC8230294 DOI: 10.3390/pathogens10060691] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 11/25/2022] Open
Abstract
Highly pathogenic avian influenza (HPAI) virus is one of the most virulent and infectious pathogens of poultry. As a response to HPAI epidemics, veterinary authorities implement preemptive depopulation as a controlling strategy. However, mass culling within a uniform radius of the infection site can result in unnecessary depopulation. Therefore, it is useful to quantify the transmission distance from infected premises (IPs) before determining the optimal area for preemptive depopulation. Accordingly, we analyzed the transmission risk within spatiotemporal clusters of IPs using transmission kernel estimates derived from phylogenetic clustering information on 311 HPAI H5N6 IPs identified during the 2016–2017 epidemic, Republic of Korea. Subsequently, we explored the impact of varying the culling radius on the local transmission of HPAI given the transmission risk estimates. The domestic duck farm density was positively associated with higher transmissibility. Ring culling over a radius of 3 km may be effective for areas with high dense duck holdings, but this approach does not appear to significantly reduce the risk for local transmission in areas with chicken farms. This study provides the first estimation of the local transmission dynamics of HPAI in the Republic of Korea as well as insight into determining an effective ring culling radius.
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Wolf JJ, Xia C, Studstill CJ, Ngo H, Brody SL, Anderson PE, Hahm B. Influenza A virus NS1 induces degradation of sphingosine 1-phosphate lyase to obstruct the host innate immune response. Virology 2021; 558:67-75. [PMID: 33730651 PMCID: PMC8109848 DOI: 10.1016/j.virol.2021.02.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/11/2021] [Accepted: 02/17/2021] [Indexed: 12/14/2022]
Abstract
The type I interferon (IFN)-mediated innate immune response is one of the central obstacles influenza A virus (IAV) must overcome in order to successfully replicate within the host. We have previously shown that sphingosine 1-phosphate (S1P) lyase (SPL) enhances IKKϵ-mediated type I IFN responses. Here, we demonstrate that the nonstructural protein 1 (NS1) of IAV counteracts the SPL-mediated antiviral response by inducing degradation of SPL. SPL was ubiquitinated and downregulated upon IAV infection or NS1 expression, whereas NS1-deficient IAV failed to elicit SPL ubiquitination or downregulation. Transiently overexpressed SPL increased phosphorylation of IKKϵ, resulting in enhanced expression of type I IFNs. However, this induction was markedly inhibited by IAV NS1. Collectively, this study reveals a novel strategy employed by IAV to subvert the type I IFN response, providing new insights into the interplay between IAV and host innate immunity.
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Affiliation(s)
- Jennifer J Wolf
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA; Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA
| | - Chuan Xia
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA; Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA; Present Address: State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Caleb J Studstill
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA; Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA
| | - Hanh Ngo
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA; Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA
| | - Steven L Brody
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Paul E Anderson
- Laboratory for Infectious Disease Research, University of Missouri, Columbia, MO, 65212, USA
| | - Bumsuk Hahm
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA; Department of Molecular Microbiology and Immunology, University of Missouri, Columbia, MO, 65212, USA.
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