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" Simulation Modeling as a Tool for the Control of Foot-and-Mouth Disease (FMD) in Endemic Regions "
Zaheer, Muhammad Usman
Rao, Sangeeta
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Latin Dissertation
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English
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| Record Number
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1052795
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| Doc. No
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TL51912
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| Main Entry
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Zaheer, Muhammad Usman
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| Title & Author
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Simulation Modeling as a Tool for the Control of Foot-and-Mouth Disease (FMD) in Endemic Regions\ Zaheer, Muhammad UsmanRao, Sangeeta
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| College
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Colorado State University
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| Date
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2019
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| Degree
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Ph.D.
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2019
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| Note
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222 p.
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| Abstract
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Foot and mouth disease (FMD) is endemic in many parts of the world (Anjum et al., 2006; Farooq et al., 2018, 2017a, 2017b, 2017c, 2016; Gleeson, 2002; Jamal et al., 2010; Navid et al., 2018; Rweyemamu et al., 2008; Yano et al., 2018), and it is associated with substantial economic losses (Ferrari et al., 2014; Jemberu et al., 2014; Knight-Jones and Rushton, 2013), which amount to USD 6.5 and 21 billion, and > USD 1.5 billion in endemic and disease-free settings, respectively (Knight-Jones and Rushton, 2013). International organizations such as the Food and Agriculture Organization of the United Nations (FAO), the World Organization for Animal Health (OIE), the European Commission for the Control of Foot and Mouth Disease (EuFMD) have called for a more targeted control strategy in the ‘Progressive Control Pathway for FMD’ to reduce the disease burden and high economic costs associated with it (Abbas et al., 2014; Jamal and Belsham, 2013; Paton et al., 2009; Rweyemamu et al., 2008a; Sumption et al., 2012). Simulation modeling has become common for investigating the spread of highly contagious diseases such as FMD, and for conducting risk assessments (Dorjee et al., 2016; Guitian and Pfeiffer, 2006; Kao, 2002; Keeling, 2005; Morris et al., 2002). Many models have been developed to mimic the spread of FMD in specific regions or countries (Bates et al., 2003d; Garner and Beckett, 2005; Harvey et al., 2007b; Stevenson et al., 2013; Wongsathapornchai et al., 2008). In disease-free countries, models are used to identify gaps in the preparedness such as estimating required resources (Garner et al., 2016; Roche et al., 2014), whereas, in endemic countries, models can be useful to compare mitigation strategies to guide future directions of FMD control program (Souley Kouato et al., 2018). Most of the reported literature on FMD simulation models is, however, associated with disease-free countries with minimal application of these models in countries with an endemic status of FMD (Pomeroy et al., 2017). Use of simulation models to endemic settings, therefore, would be beneficial in advancing our knowledge and understanding of FMD dynamics, and to facilitate both local and global control of FMD (Pomeroy et al., 2017). The overall goal of this dissertation was to build and demonstrate the application of spatially-explicit stochastic simulation models as a tool to evaluate mitigation strategies for FMD control in endemic settings. Most of the reported literature on FMD simulation models is, however, associated with disease-free countries with minimal application of these models in countries with an endemic status of FMD (Pomeroy et al., 2017). Chapter 1 of this dissertation followed the guidelines (Moher et al., 2009) established in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The aim was to review the existing published original research on spatially explicit stochastic simulation models (SESS) of FMD spread, with a specific focus on assessing these models for their application in FMD-endemic settings. The goal was to identify the specific components of endemic FMD needed to adapt the SESS models for application in FMD-endemic settings. It was revealed that existing SESS should be adapted by incorporating multiple co-circulating serotypes, livestock population dynamics, and routine prophylactic vaccination (RPV) to extend their potential use in FMD-endemic settings. Application of SESS models require datasets on the location and population of individual livestock holdings, and these data are often not available for developing countries. To overcome this lack of data, Chapter 2 of this dissertation demonstrated the methodology to generate these data synthetically for livestock in Pakistan and Thailand. The approach consisted of three main steps, i.e., microsimulating aggregate census data, creating geospatial probability surface, and, finally, distributing the microsimulated dataset on the geospatial probability surface. The resulting simulated dataset is a crucial input for the application of SESS models in endemic regions for modeling FMD spread and evaluating mitigation strategies for its control. The use of SESS models in endemic regions requires adaptation of these models to incorporate necessary components of endemic FMD such as livestock population dynamics, multiple co-circulating serotypes, and routine prophylactic vaccination (RPV). Chapter 3 of this dissertation aimed to modify the underlying modeling framework of the North American Animal Disease Spread Model (NAADSM) to include RPV as an additional mitigation strategy for FMD control. The resulting framework is called “Simulation Model for Infectious Animal Diseases in Endemic Regions (SMIAD-ER).” The SMIAD-ER is a uniquely equipped model to simulate the spread and evaluate alternative mitigation strategies for infectious animal diseases such as FMD in endemic regions. A demonstration of the prototype version of SMIAD-ER to FMD in Punjab, Pakistan, revealed that there was no aberrant behavior of FMD spread, which gave confidence that modification in underlying code did not result in any unintended change to the framework. Besides, the implementation of RPV as a mitigation strategy contributed to building regional herd immunity for FMD control. Model building is an iterative process which moves from being simple to add complexity to the framework gradually. The prototype version of SMIAD-ER did not have the flexibility to incorporate capacity and coverage for RPV. Since the capacity and coverage are critical components of vaccination programs, Chapter 4 of this dissertation aimed to enhance SMIAD-ER by modifying the underlying modeling framework to allow users with the flexibility to parameterize capacity and coverage for RPV to mimic the situation of a control program in endemic regions more realistically. As a demonstration for Sindh province, Pakistan, four scenarios, i.e., baseline, enhanced movement restrictions, improved disease detection, enhanced RPV, were parameterized to compare two performance indices, i.e. outbreak duration, vaccine immune holdings by the end of the outbreak, and the ratio of these two indices. Results indicated that improved FMD detection scenario resulted in the least number of holdings vaccinated with a day increase in outbreak duration followed by baseline, enhanced movement restriction, and enhanced RPV scenario. The results should, however, be considered for decision-making in line with the limitations of the study and assumptions of SMIAD-ER. Like any study, there are limitations to the approach taken by this dissertation. Chapter 5 of this dissertation aimed to present the limitations of the approach and to suggest recommendations for future work. The limitations, for instance, include reliance on the opinions of a very limited number of veterinarians to parameterize SMIAD-ER, the use of FMD outbreaks data as a proxy for prevalence. Future work should select a large number of stakeholders to glean model parameters, obtain reliable estimates on the FMD prevalence preferably by production-type and region, reach a consensus in expert opinions through the Delphi approach. Moreover, endemic countries need to strengthen their monitoring and surveillance systems, implement stricter movement restrictions through legislation and public awareness, and implement aggressive vaccination campaigns to reduce the burden FMD to ensure economic gains for future. Also, a graphical user interface should be added to SMIAD-ER to facilitate novice modelers from endemic settings to benefit from the model. Besides, SMIAD-ER should be enhanced to equip it with the capability to model multiple co-circulating serotypes and livestock population dynamics since these are unequivocally the necessary components of endemic FMD.
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| Descriptor
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Animal diseases
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Epidemiology
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Veterinary services
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Rao, Sangeeta
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Colorado State University
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