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Kirkeby C, Brookes VJ, Ward MP, Dürr S, Halasa T. A Practical Introduction to Mechanistic Modeling of Disease Transmission in Veterinary Science. Front Vet Sci 2021; 7:546651. [PMID: 33575275 PMCID: PMC7870987 DOI: 10.3389/fvets.2020.546651] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Computer-based disease spread models are frequently used in veterinary science to simulate disease spread. They are used to predict the impacts of the disease, plan and assess surveillance, or control strategies, and provide insights about disease causation by comparing model outputs with real life data. There are many types of disease spread models, and here we present and describe the implementation of a particular type: individual-based models. Our aim is to provide a practical introduction to building individual-based disease spread models. We also introduce code examples with the goal to make these techniques more accessible to those who are new to the field. We describe the important steps in building such models before, during and after the programming stage, including model verification (to ensure that the model does what was intended), validation (to investigate whether the model results reflect the modeled system), and convergence analysis (to ensure models of endemic diseases are stable before outputs are collected). We also describe how sensitivity analysis can be used to assess the potential impact of uncertainty about model parameters. Finally, we provide an overview of some interesting recent developments in the field of disease spread models.
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Affiliation(s)
- Carsten Kirkeby
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark,*Correspondence: Carsten Kirkeby
| | - Victoria J. Brookes
- School of Animal and Veterinary Sciences, Faculty of Science, Charles Sturt University, Wagga, NSW, Australia,Graham Centre for Agricultural Innovation (Charles Sturt University and NSW Department of Primary Industries), Wagga, NSW, Australia
| | - Michael P. Ward
- Faculty of Veterinary Science, Sydney School of Veterinary Science, University of Sydney, Sydney, NSW, Australia
| | - Salome Dürr
- Department of Clinical Research and Public Health, Veterinary Public Health Institute, University of Bern, Bern, Switzerland
| | - Tariq Halasa
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
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Mendes ÂJ, Haydon DT, McIntosh E, Hanley N, Halliday JEB. Socially vs. Privately Optimal Control of Livestock Diseases: A Case for Integration of Epidemiology and Economics. Front Vet Sci 2020; 7:558409. [PMID: 33324694 PMCID: PMC7723844 DOI: 10.3389/fvets.2020.558409] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 10/19/2020] [Indexed: 12/29/2022] Open
Abstract
This paper aims to illustrate the interdependencies between key epidemiological and economic factors that influence the control of many livestock infectious diseases. The factors considered here are (i) farmer heterogeneity (i.e., differences in how farmers respond to a perceived disease risk), (ii) off-farm effects of farmers' actions to control a disease (i.e., costs and benefits borne by agents that are external to the farm), and (iii) misalignment between privately and socially optimal control efforts (i.e., privately optimal behavior not conducive to a socially optimal outcome). Endemic chronic diseases cause a wide range of adverse social and economic impacts, particularly in low-income countries. The actions taken by farmers to control livestock diseases minimize some of these impacts, and heterogeneity in those actions leads to variation in prevalence at the farm level. While some farmers respond to perceived disease risks, others free-ride on the actions of these individuals, thereby compromising the potential benefits of collective, coordinated behavior. When evaluating a plausible range of disease cost to price of control ratios and assuming that farmers choose their privately optimal control effort, we demonstrate that achievement of a socially optimal disease control target is unlikely, occurring in <25% of all price-cost combinations. To achieve a socially optimal disease control outcome (reliant on farmers' voluntary actions), control policies must consider farmer heterogeneity, off-farm effects, and the predicted uptake of control measures under the assumption of optimized behavior.
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Affiliation(s)
- Ângelo J Mendes
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Daniel T Haydon
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Emma McIntosh
- College of Medical, Veterinary and Life Sciences, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Nick Hanley
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Jo E B Halliday
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
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Halasa T, Græsbøll K, Denwood M, Christensen LE, Kirkeby C. Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread. Front Vet Sci 2020; 7:513. [PMID: 33062646 PMCID: PMC7477293 DOI: 10.3389/fvets.2020.00513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 07/06/2020] [Indexed: 01/09/2023] Open
Abstract
The worldwide outbreak of Sars-CoV-2 resulted in modelers from diverse fields being called upon to help predict the spread of the disease, resulting in many new collaborations between different institutions. We here present our experience with bringing our skills as veterinary disease modelers to bear on the field of human epidemiology, building models as tools for decision makers, and bridging the gap between the medical and veterinary fields. We describe and compare the key steps taken in modeling the Sars-CoV-2 outbreak: criteria for model choices, model structure, contact structure between individuals, transmission parameters, data availability, model validation, and disease management. Finally, we address how to improve on the contingency infrastructure available for Sars-CoV-2.
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Affiliation(s)
- Tariq Halasa
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Kaare Græsbøll
- Department of Applied Mathematics and Computer Sciences, Technical University of Denmark, Lyngby, Denmark
| | - Matthew Denwood
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Lasse Engbo Christensen
- Department of Applied Mathematics and Computer Sciences, Technical University of Denmark, Lyngby, Denmark
| | - Carsten Kirkeby
- Section for Animal Welfare and Disease Control, Institute of Veterinary and Animal Sciences, Faculty of Medical and Health Sciences, University of Copenhagen, Frederiksberg, Denmark
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Hidano A, Gates MC. Why sold, not culled? Analysing farm and animal characteristics associated with livestock selling practices. Prev Vet Med 2019; 166:65-77. [PMID: 30935507 DOI: 10.1016/j.prevetmed.2019.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 01/27/2019] [Accepted: 03/08/2019] [Indexed: 11/18/2022]
Abstract
Livestock disease simulation models that incorporate animal movements often assume (1) that farmers' livestock trading practices remain consistent over time in future, (2) that animals sold to other farms are chosen randomly from a herd, and (3) that the animals' fate on the destination farm is not influenced by their past production and movement histories. The objective of this study was to assess the extent to which these assumptions are violated in the real world using records from a national database in New Zealand that captures both milk production and movement data for individual dairy cattle. All individual animal milk test records from 2006 through 2010 were extracted from the database and processed to generate different animal and herd level variables including cow demographics, previous movement history, milk volume, and milk composition (somatic cell counts (SCC), protein percentage, and fat percentage). Various statistical models were used to explore factors associated with farms' selling practice and characteristics of animals being sold. The results showed farms' livestock selling practices were highly influenced by both external factors such as market milk price and internal factors such as previous year's cow mortality and how long farms had been in business. Higher milk price increased both the number of cows being sold and the number of farms selling cows. Compared with cows that remained in the herd at the end of lactation, cows sold to other farms had lower fat and protein percentages, but similar milk volumes and SCCs. Cows that had been sold more often in the past were more likely to be sold after controlling for the effects of age. Overall, these findings highlight the potential need for disease simulation models to account for dynamics in selling practices and animal characteristics when determining which animals will be sold to other herds.
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Affiliation(s)
- Arata Hidano
- EpiCentre, School of Veterinary Science, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.
| | - M Carolyn Gates
- EpiCentre, School of Veterinary Science, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand
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Mourant JR, Fenimore PW, Manore CA, McMahon BH. Decision Support for Mitigation of Livestock Disease: Rinderpest as a Case Study. Front Vet Sci 2018; 5:182. [PMID: 30234131 PMCID: PMC6129599 DOI: 10.3389/fvets.2018.00182] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 07/17/2018] [Indexed: 12/25/2022] Open
Abstract
A versatile, interactive model to predict geographically resolved epidemic progression after pathogen introduction into a population is presented. Deterministic simulations incorporating a compartmental disease model run rapidly, facilitating the analysis of mitigations such as vaccination and transmission reduction on epidemic spread and progression. We demonstrate the simulation model using rinderpest infection of cattle, a devastating livestock disease. Rinderpest has been extinguished in the wild, but it is still a threat due to stored virus in some laboratories. Comparison of simulations to historical outbreaks provides some validation of the model. Simulations of potential outbreaks demonstrate potential consequences of rinderpest virus release for a variety of possible disease parameters and mitigations. Our results indicate that a rinderpest outbreak could result in severe social and economic consequences.
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Affiliation(s)
- Judith R. Mourant
- Bioscience Division, Los Alamos National Laboratory (DOE), Los Alamos, NM, United States
| | - Paul W. Fenimore
- Theoretical Division, Los Alamos National Laboratory (DOE), Los Alamos, NM, United States
| | - Carrie A. Manore
- Theoretical Division, Los Alamos National Laboratory (DOE), Los Alamos, NM, United States
| | - Benjamin H. McMahon
- Theoretical Division, Los Alamos National Laboratory (DOE), Los Alamos, NM, United States
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Hidano A, Enticott G, Christley RM, Gates MC. Modeling Dynamic Human Behavioral Changes in Animal Disease Models: Challenges and Opportunities for Addressing Bias. Front Vet Sci 2018; 5:137. [PMID: 29977897 PMCID: PMC6021519 DOI: 10.3389/fvets.2018.00137] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 06/04/2018] [Indexed: 11/13/2022] Open
Abstract
Over the past several decades, infectious disease modeling has become an essential tool for creating counterfactual scenarios that allow the effectiveness of different disease control policies to be evaluated prior to implementation in the real world. For livestock diseases, these models have become increasingly sophisticated as researchers have gained access to rich national livestock traceability databases, which enables inclusion of explicit spatial and temporal patterns in animal movements through network-based approaches. However, there are still many limitations in how we currently model animal disease dynamics. Critical among these is that many models make the assumption that human behaviors remain constant over time. As many studies have shown, livestock owners change their behaviors around trading, on-farm biosecurity, and disease management in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations, and the direct imposition of new national animal health regulations; all of which may significantly influence how a disease spreads within and between farms. Failing to account for these dynamics may produce a substantial layer of bias in infectious disease models, yet surprisingly little is currently known about the effects on model inferences. Here, we review the growing evidence on why these assumptions matter. We summarize the current knowledge about farmers' behavioral change in on-farm biosecurity and livestock trading practices and highlight the knowledge gaps that prohibit these behavioral changes from being incorporated into disease modeling frameworks. We suggest this knowledge gap can be filled only by more empirical longitudinal studies on farmers' behavioral change as well as theoretical modeling studies that can help to identify human behavioral changes that are important in disease transmission dynamics. Moreover, we contend it is time to shift our research approach: from modeling a single disease to modeling interactions between multiple diseases and from modeling a single farmer behavior to modeling interdependencies between multiple behaviors. In order to solve these challenges, there is a strong need for interdisciplinary collaboration across a wide range of fields including animal health, epidemiology, sociology, and animal welfare.
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Affiliation(s)
- Arata Hidano
- EpiCentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Gareth Enticott
- Cardiff School of Geography and Planning, Cardiff University, Cardiff, United Kingdom
| | - Robert M. Christley
- Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Neston, United Kingdom
- Institute of Veterinary Science, University of Liverpool, Neston, United Kingdom
| | - M. Carolyn Gates
- EpiCentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
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