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Marin R, Runvik H, Medvedev A, Engblom S. Bayesian monitoring of COVID-19 in Sweden. Epidemics 2023; 45:100715. [PMID: 37703786 DOI: 10.1016/j.epidem.2023.100715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.
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Affiliation(s)
- Robin Marin
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Håkan Runvik
- Division of Systems and Control, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Alexander Medvedev
- Division of Systems and Control, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
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2
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Tuominen KS, Sternberg Lewerin S, Widgren S, Rosendal T. Assessment of control measures against livestock-associated methicillin-resistant Staphylococcus aureus in a farrow-to-finish pig herd using infectious disease modelling. Animal 2023; 17:100840. [PMID: 37209536 DOI: 10.1016/j.animal.2023.100840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/22/2023] Open
Abstract
Pigs are considered to be the main reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA), which is a zoonotic opportunistic pathogen. As LA-MRSA is an occupational hazard, there is an incentive to control its spread in pig herds. Currently, knowledge about effective control measures which do not require culling the whole herd are limited, and the control strategies against LA-MRSA vary between countries. This study uses a stochastic compartment model to simulate possible control measures for LA-MRSA in a farrow-to-finish pig herd. The aims of the study were to (1) extend a previously published disease spread model with additional management and control measures; (2) use the extended model to study the effect of the individual LA-MRSA control measures on the within-herd LA-MRSA prevalence; (3) evaluate the effect of control measures when they are implemented in combinations. From the individual control measures tested in the study, thorough cleaning was found to be most effective in reducing the LA-MRSA prevalence in the herd. When the different control measures were combined, cleaning together with disease surveillance had the largest impact on reducing the LA-MRSA and a higher chance of causing disease elimination. The results of the study showed that achieving disease elimination once LA-MRSA had been introduced in the herd was challenging but was more likely when control measures were introduced early during the outbreak. This emphasises the importance of early detection of the pathogen and subsequent rapid implementation of LA-MRSA control measures.
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Affiliation(s)
- K S Tuominen
- Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences (SLU), Box 7036, SE-750 07 Uppsala, Sweden.
| | - S Sternberg Lewerin
- Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences (SLU), Box 7036, SE-750 07 Uppsala, Sweden
| | - S Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden
| | - T Rosendal
- Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden
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3
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Chang Y, de Jong MCM. A novel method to jointly estimate transmission rate and decay rate parameters in environmental transmission models. Epidemics 2023; 42:100672. [PMID: 36738639 DOI: 10.1016/j.epidem.2023.100672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 12/23/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023] Open
Abstract
In environmental transmission, pathogens transfer from one individual to another via the environment. It is a common transmission mechanism in a wide range of host-pathogen systems. Incorporating environmental transmission in dynamic transmission models is crucial for gauging the effect of interventions, as extrapolating model results to new situations is only valid when the mechanisms are modelled correctly. The challenge in environmental transmission models lies in not jointly identifiable parameters for pathogen shedding, decay, and transmission dynamics. To solve this unidentifiability issue, we present a stochastic environmental transmission model with a novel scaling method for shedding rate parameter and a novel estimation method that distinguishes transmission rate and decay rate parameters. The core of our scaling and estimation method is calculating exposure and relating exposure to infection risks. By scaling shedding rate parameter, we standardize exposure to pathogens contributed by one infectious individual present during one time interval to one. The standardized exposure leads to a standard definition of transmission rate parameter applicable to scenarios with different decay rate parameters. Hence, we unify direct transmission (large decay rate) and environmental transmission in a continuous manner. More importantly, our exposure-based estimation method can correctly estimate back the transmission rate and the decay rate parameters, while the commonly used trajectory-based method failed. The reason is that exposure-based method gives the correct weight to infection data from previous observation periods. The correct estimation from exposure-based method will lead to more reliable predictions of intervention impact. Using the effect of disinfection as an example, we show how incorrectly estimated parameters may lead to incorrect conclusions about the effectiveness of interventions. This illustrates the importance of correct estimation of transmission rate and decay rate parameters for extrapolating environmental transmission models and predicting intervention effects.
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Affiliation(s)
- You Chang
- Quantitative Veterinary Epidemiology Group, Wageningen Institute of Animal Sciences, the Netherlands.
| | - Mart C M de Jong
- Quantitative Veterinary Epidemiology Group, Wageningen Institute of Animal Sciences, the Netherlands
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4
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Bronstein S, Engblom S, Marin R. Bayesian inference in epidemics: linear noise analysis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4128-4152. [PMID: 36899620 DOI: 10.3934/mbe.2023193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of 'best case' as well as a 'worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.
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Affiliation(s)
- Samuel Bronstein
- Department of Mathematics and Applications, ENS Paris, 75005 Paris, France
| | - Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden
| | - Robin Marin
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden
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5
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Hubbart JA, Kellner E, Petersen F. A 22-Site Comparison of Land-Use Practices, E-coli and Enterococci Concentrations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13907. [PMID: 36360790 PMCID: PMC9658064 DOI: 10.3390/ijerph192113907] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Land-use practices can greatly impact water quality. Escherichia (E.) coli and Enterococcus are accepted water quality indicators. However, surprisingly little research has been conducted comparing both organisms' population density relationships to land use practices and water quality. Stream water grab samples were collected monthly (n = 9 months) from 22 stream monitoring sites draining varying land use practice types in a representative mixed-land-use watershed of the northeastern United States. E. coli and enterococci colony forming units (CFU per 100 mL) were estimated (n = 396) and statistically analyzed relative to land use practices, hydroclimate, and pH, using a suite of methods, including correlation analysis, Principal Components Analysis (PCA), and Canonical Correspondence Analysis (CCA). Correlation analyses indicated significant (p < 0.05) relationships between fecal indicator bacteria concentrations, water quality metrics and land use practices but emphasized significant (p < 0.05) negative correlations between pH and instream enterococci concentrations. PCA and CCA results indicated consistent spatial differences between fecal indicator bacteria concentrations, pH, and land use/land cover characteristics. The study showed that pH could be considered an integrated proxy variable for past (legacy) and present land use practice influences. Results also bring to question the comparability of E-coli and enterococci relative to dominant land use practices and variations in pH and provide useful information that will help guide land use practice and water pollutant mitigation decision making.
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Affiliation(s)
- Jason A. Hubbart
- Division of Forestry and Natural Resources, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Percival Hall, Morgantown, WV 26506, USA
| | - Elliott Kellner
- The Donald Danforth Plant Science Center, 975 N. Warson Rd, St. Louis, MO 63132, USA
| | - Fritz Petersen
- Department of Biology, Biology Life Sciences Building, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL 60660, USA
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6
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Rees EM, Minter A, Edmunds WJ, Lau CL, Kucharski AJ, Lowe R. Transmission modelling of environmentally persistent zoonotic diseases: a systematic review. Lancet Planet Health 2021; 5:e466-e478. [PMID: 34245717 DOI: 10.1016/s2542-5196(21)00137-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 05/02/2021] [Accepted: 05/06/2021] [Indexed: 06/13/2023]
Abstract
Transmission of many infectious diseases depends on interactions between humans, animals, and the environment. Incorporating these complex processes in transmission dynamic models can help inform policy and disease control interventions. We identified 20 diseases involving environmentally persistent pathogens (ie, pathogens that survive for more than 48 h in the environment and can cause subsequent human infections), of which indirect transmission can occur from animals to humans via the environment. Using a systematic approach, we critically appraised dynamic transmission models for environmentally persistent zoonotic diseases to quantify traits of models across diseases. 210 transmission modelling studies were identified and most studies considered diseases of domestic animals or high-income settings, or both. We found that less than half of studies validated their models to real-world data, and environmental data on pathogen persistence was rarely incorporated. Model structures varied, with few studies considering the animal-human-environment interface of transmission in the context of a One Health framework. This Review highlights the need for more data-driven modelling of these diseases and a holistic One Health approach to model these pathogens to inform disease prevention and control strategies.
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Affiliation(s)
- Eleanor M Rees
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Amanda Minter
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - W John Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Colleen L Lau
- Research School of Population Health, Australian National University, Canberra, ACT, Australia; School of Public Health, University of Queensland, Brisbane, QLD, Australia
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Rachel Lowe
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
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7
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Nixon E, Brooks-Pollock E, Wall R. Sheep scab transmission: a spatially explicit dynamic metapopulation model. Vet Res 2021; 52:54. [PMID: 33845898 PMCID: PMC8042976 DOI: 10.1186/s13567-021-00924-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/15/2021] [Indexed: 12/02/2022] Open
Abstract
Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain. The model uses agricultural survey and sheep movement data to geo-reference farms and capture realistic movement patterns. Reported data on sheep scab outbreaks from 1973 to 1991 were used for model fitting with Sequential Monte Carlo Approximate Bayesian Computation methods. The outbreak incidence predicted by the model was from the same statistical distribution as the reported outbreak data (\documentclass[12pt]{minimal}
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\begin{document}$${\chi }^{2}$$\end{document}χ2 = 115.3, p = 1) and the spatial location of sheep scab outbreaks predicted was positively correlated with the observed outbreak data by county (\documentclass[12pt]{minimal}
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\begin{document}$$\tau$$\end{document}τ = 0.55, p < 0.001), confirming that the model developed is able to accurately capture the number of farms infected in a year, the seasonality of scab incidence and the spatial patterns seen in the data. This model gives insight into the transmission dynamics of sheep scab and will allow the exploration of more effective control strategies.
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Affiliation(s)
- Emily Nixon
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK. .,Bristol Veterinary School, University of Bristol, Langford House, Bristol, BS40 5DU, UK.
| | - Ellen Brooks-Pollock
- Bristol Veterinary School, University of Bristol, Langford House, Bristol, BS40 5DU, UK.,NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol, Bristol, UK
| | - Richard Wall
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
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8
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Galvis JA, Corzo CA, Prada JM, Machado G. Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus. Transbound Emerg Dis 2021; 69:485-500. [PMID: 33506620 DOI: 10.1111/tbed.14007] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 12/15/2022]
Abstract
Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.
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Affiliation(s)
- Jason A Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St Paul, MN, USA
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
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9
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Galvis JA, Jones CM, Prada JM, Corzo CA, Machado G. The between-farm transmission dynamics of porcine epidemic diarrhoea virus: A short-term forecast modelling comparison and the effectiveness of control strategies. Transbound Emerg Dis 2021; 69:396-412. [PMID: 33475245 DOI: 10.1111/tbed.13997] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/11/2021] [Accepted: 01/18/2021] [Indexed: 01/10/2023]
Abstract
A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly porcine epidemic diarrhoea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems, SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.
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Affiliation(s)
- Jason A Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA
| | - Chris M Jones
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
| | - Joaquin M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St Paul, MN, USA
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA.,Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
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10
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Mulder AC, van de Kassteele J, Heederik D, Pijnacker R, Mughini‐Gras L, Franz E. Spatial Effects of Livestock Farming on Human Infections With Shiga Toxin-Producing Escherichia coli O157 in Small but Densely Populated Regions: The Case of the Netherlands. GEOHEALTH 2020; 4:e2020GH000276. [PMID: 33283126 PMCID: PMC7682566 DOI: 10.1029/2020gh000276] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 06/12/2023]
Abstract
The role of environmental transmission of typically foodborne pathogens like Shiga toxin-producing Escherichia coli (STEC) O157 is increasingly recognized. To gain more insights into spatially restricted risk factors that play a role in this transmission, we assessed the spatial association between sporadic STEC O157 human infections and the exposure to livestock (i.e. small ruminants, cattle, poultry, and pigs) in a densely populated country: the Netherlands. This was done for the years 2007-2016, using a state-of-the-art spatial analysis method in which hexagonal areas with different sizes (90, 50, 25 and 10 km2) were used in combination with a novel probability of exposure metric: the population-weighted number of animals per hexagon. To identify risk factors for STEC O157 infections and their population attributable fraction (PAF), a spatial regression model was fitted using integrated nested Laplace approximation (INLA). Living in hexagonal areas of 25, 50 and 90 km2 with twice as much population-weighted small ruminants was associated with an increase of the incidence rate of human STEC O157 infections in summer (RR of 1.09 [95%CI;1.01-1.17], RR of 1.17 [95%CI;1.07-1.28] and RR of 1.13 [95%CI;1.01-1.26]), with a PAF of 49% (95%CI;8-72%). Results suggest exposure to small ruminants to be a risk factor, although no evidence on the mode of transmission is provided. Therefore, the underlying mechanisms warrant further investigation and could offer new targets for control. The newly proposed exposure metric has potential to improve existing spatial modeling studies on infectious diseases related to livestock exposure, especially in densely populated countries like the Netherlands.
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Affiliation(s)
- A. C. Mulder
- Centre for Infectious Disease ControlNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
| | - J. van de Kassteele
- Centre for Infectious Disease ControlNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
| | - D. Heederik
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental EpidemiologyUtrecht UniversityUtrechtthe Netherlands
| | - R. Pijnacker
- Centre for Infectious Disease ControlNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
| | - L. Mughini‐Gras
- Centre for Infectious Disease ControlNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental EpidemiologyUtrecht UniversityUtrechtthe Netherlands
| | - E. Franz
- Centre for Infectious Disease ControlNational Institute for Public Health and the Environment (RIVM)Bilthoventhe Netherlands
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11
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Yatabe T, Martínez-López B, Díaz-Cao JM, Geoghegan F, Ruane NM, Morrissey T, McManus C, Hill AE, More SJ. Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures. Front Vet Sci 2020; 7:385. [PMID: 32766292 PMCID: PMC7378893 DOI: 10.3389/fvets.2020.00385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 05/29/2020] [Indexed: 12/18/2022] Open
Abstract
Cardiomyopathy syndrome (CMS) is a severe cardiac disease of Atlantic salmon caused by the piscine myocarditis virus (PMCV), which was first reported in Ireland in 2012. In this paper, we describe the use of data-driven network modeling as a framework to evaluate the transmission of PMCV in the Irish farmed Atlantic salmon population and the impact of different mitigation measures. Input data included live fish movement data from 2009 to 2017, population dynamics events and the spatial location of the farms. With these inputs, we fitted a network-based stochastic infection spread model. After assumed initial introduction of the agent in 2009, our results indicate that it took 5 years to reach a between-farm prevalence of 100% in late 2014, with older fish being most affected. Local spread accounted for only a small proportion of new infections, being more important for sustained infection in a given area. Spread via movement of subclinically infected fish was most important for explaining the observed countrywide spread of the agent. Of the targeted intervention strategies evaluated, the most effective were those that target those fish farms in Ireland that can be considered the most connected, based on the number of farm-to-farm linkages in a specific time period through outward fish movements. The application of these interventions in a proactive way (before the first reported outbreak of the disease in 2012), assuming an active testing of fish consignments to and from the top 8 ranked farms in terms of outward fish movement, would have yielded the most protection for the Irish salmon farming industry. Using this approach, the between-farm PMCV prevalence never exceeded 20% throughout the simulation time (as opposed to the simulated 100% when no interventions are applied). We argue that the Irish salmon farming industry would benefit from this approach in the future, as it would help in early detection and prevention of the spread of viral agents currently exotic to the country.
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Affiliation(s)
- Tadaishi Yatabe
- Department of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Beatriz Martínez-López
- Department of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - José Manuel Díaz-Cao
- Department of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | | | - Neil M Ruane
- Fish Health Unit, Marine Institute, Galway, Ireland
| | | | | | - Ashley E Hill
- California Animal Health and Food Safety Laboratories (CAHFS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Simon J More
- Centre for Veterinary Epidemiology and Risk Analysis (CVERA), UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland
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12
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Salines M, Andraud M, Rose N, Widgren S. A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network. PLoS One 2020; 15:e0230257. [PMID: 32658910 PMCID: PMC7357762 DOI: 10.1371/journal.pone.0230257] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 02/25/2020] [Indexed: 12/28/2022] Open
Abstract
Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible co-infection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix. Between-herd transmission was modelled by coupling within-herd and network dynamics using the SimInf package. Different introduction scenarios were tested as well as a decrease in the prevalence of IMV-infected farms. After introduction of a single infected gilt, the model showed that the transmission pathway as well as the prevalence of HEV-infected pigs at slaughter age were affected by the type of the index farm, the health status of the population and the type of the infected farms. These outcomes could help design HEV control strategies at a territorial scale based on the assessment of the farms' and network's risk.
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Affiliation(s)
- Morgane Salines
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Ploufragan-Plouzané-Niort Laboratory, Epidemiology, Health and Welfare Research Unit, France
| | - Mathieu Andraud
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Ploufragan-Plouzané-Niort Laboratory, Epidemiology, Health and Welfare Research Unit, France
| | - Nicolas Rose
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Ploufragan-Plouzané-Niort Laboratory, Epidemiology, Health and Welfare Research Unit, France
| | - Stefan Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden
- * E-mail:
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13
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Ezanno P, Andraud M, Beaunée G, Hoch T, Krebs S, Rault A, Touzeau S, Vergu E, Widgren S. How mechanistic modelling supports decision making for the control of enzootic infectious diseases. Epidemics 2020; 32:100398. [PMID: 32622313 DOI: 10.1016/j.epidem.2020.100398] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/07/2020] [Accepted: 05/29/2020] [Indexed: 12/28/2022] Open
Abstract
Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.
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Affiliation(s)
- P Ezanno
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - M Andraud
- Unité épidémiologie et bien-être du porc, Anses Laboratoire de Ploufragan-Plouzané, Ploufragan, France.
| | - G Beaunée
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - T Hoch
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Krebs
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - A Rault
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, 44307 Nantes, France.
| | - S Touzeau
- INRAE, CNRS, Université Côte d'Azur, ISA, France; Inria, INRAE, CNRS, Université Paris Sorbonne, Université Côte d'Azur, BIOCORE, France.
| | - E Vergu
- INRAE, Université Paris-Saclay, MaIAGE, 78350 Jouy-en-Josas, France.
| | - S Widgren
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden.
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Spatial and Temporal Characterization of Escherichia coli, Suspended Particulate Matter and Land Use Practice Relationships in a Mixed-Land Use Contemporary Watershed. WATER 2020. [DOI: 10.3390/w12051228] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Understanding land use practice induced increases in Escherichia (E.) coli and suspended particulate matter (SPM) concentrations is necessary to improve water quality. Weekly stream water samples were collected from 22 stream gauging sites with varying land use practices in a representative contemporary mixed-land use watershed of the eastern USA. Over the period of one annual year, Escherichia (E.) coli colony forming units (CFU per 100 mL) were compared to suspended particulate matter (SPM) concentrations (mg/L) and land use practices. Agricultural land use sub-catchments comprised elevated E. coli concentrations (avg. 560 CFU per 100 mL) compared to proximate mixed development (avg. 330 CFU per 100 mL) and forested (avg. 206 CFU per 100 mL) sub-catchments. Additionally, agricultural land use showed statistically significant relationships (p < 0.01) between annual E. coli and SPM concentration data. Quarterly PCA biplots displayed temporal variability in land use impacts on E. coli and SPM concentrations, with agricultural land use being closely correlated with both pollutants during Quarters 2 and 3 but not Quarters 1 and 4. The data collected during this investigation advance the understanding of land use impacts on fecal contamination in receiving waters, thereby informing land use managers on the best management practices to reduce exposure risks.
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15
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Socially engaged calves are more likely to be colonised by VTEC O157:H7 than individuals showing signs of poor welfare. Sci Rep 2020; 10:6320. [PMID: 32286399 PMCID: PMC7156447 DOI: 10.1038/s41598-020-63186-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/20/2020] [Indexed: 11/24/2022] Open
Abstract
In cattle herds, the transmission and persistence of VTEC O157:H7 (a serotype of verotoxin-producing Escherichia coli – known for its life threatening complications in humans) is dependent on a small proportion of cattle who become colonised and shed high numbers of the bacteria. Reducing the proportion of these animals is considered key for decreasing the prevalence of VTEC O157:H7. In this study, observations of calf behaviour and animal-based welfare indicators were used to explore individual risk factors and underlying drivers of colonisation in Swedish dairy calves. Interdependencies between variables led to three different approaches being used to visualize and explore the associations. Combining the results of all methods revealed similar patterns and suggest that healthy animals, actively grooming and interacting with others calves in the group have a higher risk of colonisation than small dairy calves in poor condition (diarrhoea, poor ruminal fill, poor body condition score and nasal discharge). This lends no support to the hypothesis that reduced welfare is a risk factor for VTEC O157:H7, but implies that individual differences in calf behaviour affect oral exposure to the bacteria so driving the risk of colonisation. This new finding has important implications for understanding of VTEC O157:H7 transmission within farms.
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16
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Advancing Understanding of Land Use and Physicochemical Impacts on Fecal Contamination in Mixed-Land-Use Watersheds. WATER 2020. [DOI: 10.3390/w12041094] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Understanding mixed-land-use practices and physicochemical influences on Escherichia (E.) coli concentrations is necessary to improve water quality management and human health. Weekly stream water samples and physicochemical data were collected from 22 stream gauging sites representing varying land use practices in a contemporary Appalachian watershed of the eastern USA. Over the period of one annual year, Escherichia (E.) coli colony forming units (CFU) per 100 mL were compared to physicochemical parameters and land use practices. Annual average E. coli concentration increased by approximately 112% from acid mine drainage (AMD) impacted headwaters to the lower reaches of the watershed (approximate averages of 177 CFU per 100 mL vs. 376 CFU per 100 mL, respectively). Significant Spearman’s correlations (p < 0.05) were identified from analyses of pH and E. coli concentration data representing 77% of sample sites; thus highlighting legacy effects of historic mining (AMD) on microbial water quality. A tipping point of 25–30% mixed development was identified as leading to significant (p < 0.05) negative correlations between chloride and E. coli concentrations. Study results advance understanding of land use and physicochemical impacts on fecal contamination in mixed-land-use watersheds, aiding in the implementation of effective water quality management practices and policies.
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Brock J, Lange M, More SJ, Graham D, Thulke HH. Reviewing age-structured epidemiological models of cattle diseases tailored to support management decisions: Guidance for the future. Prev Vet Med 2019; 174:104814. [PMID: 31743817 DOI: 10.1016/j.prevetmed.2019.104814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 10/22/2019] [Accepted: 10/23/2019] [Indexed: 12/31/2022]
Abstract
Mechanistic simulation models are being increasingly used as tools to assist with animal health decision-making in the cattle sector. We reviewed scientific literature for studies reporting age-structured cattle management models in application to infectious diseases. Our emphasis was on papers dedicated to support decision making in the field. In this systematic review we considered 1290 manuscripts and identified 76 eligible studies. These are based on 52 individual models from 10 countries addressing 9 different pathogens. We provide an overview of these models and present in detail their theoretical foundations, design paradigms and incorporated processes. We propose a structure of the characteristics of cattle disease models using three main features: [1] biological processes, [2] farming-related processes and [3] pathogen-related processes. It would be of benefit if future cattle disease models were to follow this structure to facilitate science communication and to allow increased model transparency.
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Affiliation(s)
- Jonas Brock
- Helmholtz Centre for Environmental Research GmbH - UFZ, Dept Ecological Modelling, PG Ecological Epidemiology, Leipzig, Germany; Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim, Ireland.
| | - Martin Lange
- Helmholtz Centre for Environmental Research GmbH - UFZ, Dept Ecological Modelling, PG Ecological Epidemiology, Leipzig, Germany
| | - Simon J More
- Centre for Veterinary Epidemiology and Risk Analysis, UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland
| | - David Graham
- Animal Health Ireland, Carrick-on-Shannon, Co. Leitrim, Ireland
| | - Hans-Hermann Thulke
- Helmholtz Centre for Environmental Research GmbH - UFZ, Dept Ecological Modelling, PG Ecological Epidemiology, Leipzig, Germany
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Tamminen LM, Söderlund R, Wilkinson DA, Torsein M, Eriksson E, Churakov M, Dicksved J, Keeling LJ, Emanuelson U. Risk factors and dynamics of verotoxigenic Escherichia coli O157:H7 on cattle farms: An observational study combining information from questionnaires, spatial data and molecular analyses. Prev Vet Med 2019; 170:104726. [PMID: 31421496 DOI: 10.1016/j.prevetmed.2019.104726] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 07/02/2019] [Indexed: 01/29/2023]
Abstract
The increasing number of human cases infected with a highly virulent type of verotoxigenic Escherichia coli (VTEC) O157:H7 in Sweden is the result of domestic transmission originating in regional clusters of infected cattle farms. To control the spread of the bacteria a comprehensive picture of infection dynamics, routes of transmission between farms and risk factors for persistence is urgently needed. The aim of the study was to investigate different aspects of the epidemiology of VTEC O157:H7 on the Swedish island of Öland by combining information from environmental sampling of VTEC O157:H7 from 80 farms with information from farmer questionnaires, spatial and molecular analyses. The farms were sampled in the spring and fall of 2014 and on four of them additional samples were collected during summer and winter. The results show a high prevalence of VTEC O157:H7 and a high proportion of strains belonging to the virulent clade 8. Farms that became infected between samplings were all located in an area with high cattle density. The most important risk factors identified are generally associated with biosecurity and indicate that visitors travelling between farms may be important for transmission. In addition, whole genome sequencing of a subset of isolates from the four farms where additional sampling was performed revealed ongoing local transmission that cannot be observed with a lower resolution typing method. Our observations also show that VTEC O157:H7 may persist in the farm environment for extended periods of time, suggesting that specific on-farm measures to reduce environmental prevalence and spread between groups of animals may be required in these cases.
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Affiliation(s)
- Lena-Mari Tamminen
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7054, SE-75007, Uppsala, Sweden.
| | | | - David A Wilkinson
- Molecular Epidemiology and Public Health Laboratory (mEpilab), Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Private Bag 11-222, Palmerston North 4442, New Zealand; New Zealand Food Safety Science and Research Centre, Massey University, Private Bag 11-222, Palmerston North 4442, New Zealand
| | - Maria Torsein
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Box 234, SE-53223, Skara, Sweden
| | - Erik Eriksson
- National Veterinary Institute (SVA), SE-75189, Uppsala, Sweden
| | - Mikhail Churakov
- Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Box 7024, SE-75007, Uppsala, Sweden
| | - Johan Dicksved
- Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Box 7024, SE-75007, Uppsala, Sweden
| | - Linda J Keeling
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Box 7068, SE-75007, Uppsala, Sweden
| | - Ulf Emanuelson
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7054, SE-75007, Uppsala, Sweden
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