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Nielsen SS, Alvarez J, Bicout DJ, Calistri P, Canali E, Drewe JA, Garin‐Bastuji B, Gortázar C, Herskin MS, Michel V, Miranda Chueca MÁ, Padalino B, Roberts HC, Spoolder H, Stahl K, Velarde A, Viltrop A, Winckler C, Bortolami A, Guinat C, Harder T, Stegeman A, Terregino C, Lanfranchi B, Preite L, Aznar I, Broglia A, Baldinelli F, Gonzales Rojas JL. Vaccination of poultry against highly pathogenic avian influenza - Part 2. Surveillance and mitigation measures. EFSA J 2024; 22:e8755. [PMID: 38638555 PMCID: PMC11024799 DOI: 10.2903/j.efsa.2024.8755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
Selecting appropriate diagnostic methods that take account of the type of vaccine used is important when implementing a vaccination programme against highly pathogenic avian influenza (HPAI). If vaccination is effective, a decreased viral load is expected in the samples used for diagnosis, making molecular methods with high sensitivity the best choice. Although serological methods can be reasonably sensitive, they may produce results that are difficult to interpret. In addition to routine molecular monitoring, it is recommended to conduct viral isolation, genetic sequencing and phenotypic characterisation of any HPAI virus detected in vaccinated flocks to detect escape mutants early. Following emergency vaccination, various surveillance options based on virological testing of dead birds ('bucket sampling') at defined intervals were assessed to be effective for early detection of HPAIV and prove disease freedom in vaccinated populations. For ducks, virological or serological testing of live birds was assessed as an effective strategy. This surveillance could be also applied in the peri-vaccination zone on vaccinated establishments, while maintaining passive surveillance in unvaccinated chicken layers and turkeys, and weekly bucket sampling in unvaccinated ducks. To demonstrate disease freedom with > 99% confidence and to detect HPAI virus sufficiently early following preventive vaccination, monthly virological testing of all dead birds up to 15 per flock, coupled with passive surveillance in both vaccinated and unvaccinated flocks, is recommended. Reducing the sampling intervals increases the sensitivity of early detection up to 100%. To enable the safe movement of vaccinated poultry during emergency vaccination, laboratory examinations in the 72 h prior to the movement can be considered as a risk mitigation measure, in addition to clinical inspection; sampling results from existing surveillance activities carried out in these 72 h could be used. In this Opinion, several schemes are recommended to enable the safe movement of vaccinated poultry following preventive vaccination.
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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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Hayama Y, Sawai K, Yoshinori M, Yamaguchi E, Yamamoto T. Estimation of introduction time window of highly pathogenic avian influenza virus into broiler chicken farms during the 2020 - 2021 winter season outbreak in Japan. Prev Vet Med 2022; 208:105768. [PMID: 36174447 DOI: 10.1016/j.prevetmed.2022.105768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 08/01/2022] [Accepted: 09/21/2022] [Indexed: 10/31/2022]
Abstract
When an infectious disease occurs in an area, early detection of infected farms is important to respond quickly and contain the outbreak on a small scale. Estimating the time window for the introduction of the infection is important for its prevention and control. The aim of this study was to estimate the farm-specific time window from the introduction of the highly pathogenic avian influenza (HPAI) virus into poultry farms using field data from the HPAI H5N8 outbreak in the 2020-2021 winter season in Japan. Daily mortality data from 12 broiler chicken farms during the outbreak were used for the analysis. A mathematical model (Susceptible-Exposed-Infectious-Removed, SEIR model) was applied to generate the within-flock transmission of HPAI. The model-predicted mortality was fitted to the observed excess mortality data induced by HPAI to estimate the farm-specific transmission rate and the time of virus introduction. The estimated value of the transmission rate in each farm was 1.449 day-1 in median (min: 0.661 day-1, max: 3.387 day-1). The time window from the introduction of the virus to notification in each farm was estimated at 14.0 days in median (min: 8.6 days, max: 24.1 days) in the deterministic model. In addition, in the stochastic model considering the randomness of transmission in the early phase of the outbreak, the upper value of 95 % credible interval of the time window ranged from 12 to 34 days, with a median of 21 days. The results suggest that although one to three weeks had elapsed on most farms until notification after the virus introduction, the time window could exceed three weeks considering the stochasticity of disease transmission. As for the potential farm characteristics affecting within-flock transmission, the transmission rate was smaller (p-value=0.02) and the estimated time window from introduction to notification was longer (p-value=0.02) when birds were older. This study provides reliable information for setting up a tracing period for a potential source farm and enhancing the efforts for early detection.
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Affiliation(s)
- Yoko Hayama
- Division of Transboundary Animal Disease Research, National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan.
| | - Kotaro Sawai
- Division of Transboundary Animal Disease Research, National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Murato Yoshinori
- Division of Transboundary Animal Disease Research, National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Emi Yamaguchi
- Division of Transboundary Animal Disease Research, National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Takehisa Yamamoto
- Division of Transboundary Animal Disease Research, National Institute of Animal Health, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
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Ewing DA, Pooley CM, Gamado KM, Porphyre T, Marion G. Exact Bayesian inference of epidemiological parameters from mortality data: application to African swine fever virus. J R Soc Interface 2022; 19:20220013. [PMID: 35259955 PMCID: PMC8905154 DOI: 10.1098/rsif.2022.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Pathogens such as African swine fever virus (ASFV) are an increasing threat to global livestock production with implications for economic well-being and food security. Quantification of epidemiological parameters, such as transmission rates and latent and infectious periods, is critical to inform efficient disease control. Parameter estimation for livestock disease systems is often reliant upon transmission experiments, which provide valuable insights in the epidemiology of disease but which may also be unrepresentative of at-risk populations and incur economic and animal welfare costs. Routinely collected mortality data are a potential source of readily available and representative information regarding disease transmission early in outbreaks. We develop methodology to conduct exact Bayesian parameter inference from mortality data using reversible jump Markov chain Monte Carlo incorporating multiple routes of transmission (e.g. within-farm secondary and background transmission from external sources). We use this methodology to infer epidemiological parameters for ASFV using data from outbreaks on nine farms in the Russian Federation. This approach improves inference on transmission rates in comparison with previous methods based on approximate Bayesian computation, allows better estimation of time of introduction and could readily be applied to other outbreaks or pathogens.
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Affiliation(s)
- David A Ewing
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Edinburgh, UK
| | - Christopher M Pooley
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Edinburgh, UK
| | - Kokouvi M Gamado
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Edinburgh, UK
| | - Thibaud Porphyre
- The Epidemiology, Economics and Risk Assessment (EERA) Group, The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Roslin, UK.,Université de Lyon, Université Lyon 1, CNRS, VetAgro Sup, Laboratoire de Biométrie et Biologie Evolutive, Marcy l'Étoile, France
| | - Glenn Marion
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Edinburgh, UK
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Avian influenza transmission risk along live poultry trading networks in Bangladesh. Sci Rep 2021; 11:19962. [PMID: 34620890 PMCID: PMC8497497 DOI: 10.1038/s41598-021-98989-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Live animal markets are known hotspots of zoonotic disease emergence. To mitigate those risks, we need to understand how networks shaped by trading practices influence disease spread. Yet, those practices are rarely recorded in high-risk settings. Through a large cross-sectional study, we assessed the potential impact of live poultry trading networks’ structures on avian influenza transmission dynamics in Bangladesh. Networks promoted mixing between chickens sourced from different farming systems and geographical locations, fostering co-circulation of viral strains of diverse origins in markets. Viral transmission models suggested that the observed rise in viral prevalence from farms to markets was unlikely explained by intra-market transmission alone, but substantially influenced by transmission occurring in upstream network nodes. Disease control interventions should therefore alter the entire network structures. However, as networks differed between chicken types and city supplied, standardised interventions are unlikely to be effective, and should be tailored to local structural characteristics.
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Mugisha JYT, Ssebuliba J, Nakakawa JN, Kikawa CR, Ssematimba A. Mathematical modeling of COVID-19 transmission dynamics in Uganda: Implications of complacency and early easing of lockdown. PLoS One 2021; 16:e0247456. [PMID: 33617579 PMCID: PMC7899325 DOI: 10.1371/journal.pone.0247456] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/06/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Uganda has a unique set up comprised of resource-constrained economy, social-economic challenges, politically diverse regional neighborhood and home to long-standing refuge crisis that comes from long and protracted conflicts of the great lakes. The devastation of the on-going global pandemic outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is likely to be escalated by these circumstances with expectations of the impact of the disease being severe. MATERIALS AND METHODS In this study, we formulate a mathematical model that incorporates the currently known disease characteristics and tracks various intervention measures that the government of Uganda has implemented since the reporting of the first case in March 2020. We then evaluate these measures to understand levels of responsiveness and adherence to standard operating procedures and quantify their impact on the disease burden. Novel in this model was the unique aspect of modeling the trace-and-isolate protocol in which some of the latently infected individuals tested positive while in strict isolation centers thereby reducing their infectious period. RESULTS The study findings show that even with elimination of all imported cases at any given time it would take up to nine months to rid Uganda of the disease. The findings also show that the optimal timing of easing of lockdowns while mitigating the possibility of re-emergence of a second epidemic wave requires avoiding the scenario of releasing too-many-too-soon. It is even more worrying that enhancing contact tracing would only affect the magnitude and timing of the second wave but cannot prevent it altogether. CONCLUSION We conclude that, given the prevailing circumstances, a phased-out lifting of lockdown measures, minimization of COVID-19 transmissibility within hospital settings, elimination of recruitment of infected individuals as well as enhanced contact tracing would be key to preventing overwhelming of the healthcare system that would come with dire consequences.
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Affiliation(s)
- Joseph Y. T. Mugisha
- Department of Mathematics, College of Natural Sciences, Makerere University, Kampala, Uganda
| | - Joseph Ssebuliba
- Department of Mathematics, College of Natural Sciences, Makerere University, Kampala, Uganda
| | - Juliet N. Nakakawa
- Department of Mathematics, College of Natural Sciences, Makerere University, Kampala, Uganda
| | - Cliff R. Kikawa
- Department of Economics and Statistics, Faculty of Economics and Management Sciences, Kabale University, Kabale, Uganda
| | - Amos Ssematimba
- Department of Mathematics, Faculty of Science, Gulu University, Gulu, Uganda
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Estimating epidemiological parameters using diagnostic testing data from low pathogenicity avian influenza infected turkey houses. Sci Rep 2021; 11:1602. [PMID: 33452377 PMCID: PMC7810853 DOI: 10.1038/s41598-021-81254-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/05/2021] [Indexed: 11/28/2022] Open
Abstract
Limiting spread of low pathogenicity avian influenza (LPAI) during an outbreak is critical to reduce the negative impact on poultry producers and local economies. Mathematical models of disease transmission can support outbreak control efforts by estimating relevant epidemiological parameters. In this article, diagnostic testing data from each house on a premises infected during a LPAI H5N2 outbreak in the state of Minnesota in the United States in 2018 was used to estimate the time of virus introduction and adequate contact rate, which determines the rate of disease spread. A well-defined most likely time of virus introduction, and upper and lower 95% credibility intervals were estimated for each house. The length of the 95% credibility intervals ranged from 11 to 22 with a mean of 17 days. In some houses the contact rate estimates were also well-defined; however, the estimated upper 95% credibility interval bound for the contact rate was occasionally dependent on the upper bound of the prior distribution. The estimated modes ranged from 0.5 to 6.0 with a mean of 2.8 contacts per day. These estimates can be improved with early detection, increased testing of monitored premises, and combining the results of multiple barns that possess similar production systems.
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Estimating the introduction time of highly pathogenic avian influenza into poultry flocks. Sci Rep 2020; 10:12388. [PMID: 32709965 PMCID: PMC7381656 DOI: 10.1038/s41598-020-68623-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 06/24/2020] [Indexed: 01/25/2023] Open
Abstract
The estimation of farm-specific time windows for the introduction of highly-pathogenic avian influenza (HPAI) virus can be used to increase the efficiency of disease control measures such as contact tracing and may help to identify risk factors for virus introduction. The aims of this research are to (1) develop and test an accurate approach for estimating farm-specific virus introduction windows and (2) evaluate this approach by applying it to 11 outbreaks of HPAI (H5N8) on Dutch commercial poultry farms during the years 2014 and 2016. We used a stochastic simulation model with susceptible, infectious and recovered/removed disease stages to generate distributions for the period from virus introduction to detection. The model was parameterized using data from the literature, except for the within-flock transmission rate, which was estimated from disease-induced mortality data using two newly developed methods that describe HPAI outbreaks using either a deterministic model (A) or a stochastic approach (B). Model testing using simulated outbreaks showed that both method A and B performed well. Application to field data showed that method A could be successfully applied to 8 out of 11 HPAI H5N8 outbreaks and is the most generally applicable one, when data on disease-induced mortality is scarce.
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