A mathematical model that simulates control options for African swine fever virus (ASFV)

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Materials and methods

This is to certify that any sampling of live or dead pigs described within this chapter on Estimating the basic reproductive number (R0) for African swine fever virus (ASFV) transmission between pig herds in Uganda by Barongo et al, was conducted in close collaboration between the District veterinary office in Gulu District and scientists from Uganda, Kenya and Sweden as part of disease investigations for African swine fever, and funded through a collaborative research project. Disease surveillance and disease investigations lie within the mandate of the District veterinary office. The district veterinarian thus holds a general permission to sample animals for this purpose. Data from these disease investigations were reported to the National Animal Disease Diagnostic and Epidemiology Centre, NADDEC, under the ministry of Agriculture Animal Industry and Fisheries in Entebbe. The data also formed part of the basis for international reporting to the OIE.

Data source

We used data collected during previous research activities from villages in Gulu District with laboratory confirmed outbreaks of ASF (material described in [113,114]). The two hundred eleven outbreaks included in this study occurred between April 2010 and November 2011. In brief, villages that reported outbreaks of disease characterized by fever and mortality in pigs to the district veterinary authorities were visited. Within each village, samples (blood and serum) were collected from clinically diseased and/or apparently healthy pigs from all affected households. Samples were kept cool awaiting transportation to the Molecular Biology Laboratory at Makerere University Institute of Environment and Natural Resources (MUIENR) in Kampala for storage at -20°C until further processing. In the laboratory, outbreaks were confirmed by detection of ASFV nucleic acids using a commercially available real-time PCR (Tetracore Inc., Rockville, Maryland) in accordance with the instructions of the manufacturer [115]. During a second visit to all villages with laboratory confirmed outbreaks, additional data was collected using semi-structured questionnaires from a total of 211 households. The data collected included farm location (GPS coordinates), start month of the confirmed outbreak, number of pigs that had died, number that were still alive, disposal mechanism of carcasses, feed source and production system practiced.
A herd, here defined as a collection of all pigs in a pig-keeping household, was taken to be the epidemiological unit of interest [102]. Thus, our estimates of R0 reflect spread between herds. All outbreaks in the district during the period of study are assumed to have been reported. Additionally, it was assumed that all herds in the district were susceptible during the study period and the pigs from different herds were homogeneously mixing [15]. The exact number of herds present during the period of the study could not be directly determined and we estimated from the National Livestock Census Report (2008) on distribution of livestock in Uganda that there were 6,200 pig herds (mean herd size of 4.3) distributed over the 289 villages in Gulu district.

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Epidemic doubling time method

During the initial phase of an epidemic, the number of secondary cases increases exponentially, with each infection producing R0 new infections per generation assuming a constant doubling time (td) [102]. Anderson and May [103] defined a relationship between doubling time (td) and R0 as   0 1 T / td *log 2 e R   where T is the herd infectious period. For the outbreaks studied here, the herd from which the first case of death was reported was considered the index case. Each herd that was subsequently infected was considered to present a new outbreak. Outbreaks were ordered by month and the average time for the number of outbreaks to double (td) for all possible combinations during this phase were computed using Microsoft Excel® 2010. We assumed an infectious period of one month because data was aggregated at a monthly scale and as mentioned there is evidence from the literature that herds can remain infectious for a prolonged period. We then used the doubling time and infectious period to estimate R0 from the above equation.

Chapter 1 Introduction
1.1 Background
1.2 Problem statement
1.3 Significance of the study
1.4 Study Objectives
1.5 Structure of the thesis
Chapter 2 Literature Review
2.1 African swine fever and its causative virus
2.2 Distribution of ASF
2.3 ASF Maintenance Cycles and Transmission routes
2.4 ASF prevention and control strategies
Chapter 3 Estimating the basic reproductive number (R0) for African swine fever virus (ASFV) transmission between pig herds in Uganda
3.1 Abstract
3.2 Introduction
3.3 Materials and methods
3.4 Data source
3.5 Data Analysis
3.6 Results
3.7 Discussion
Chapter 4 A mathematical model that simulates control options for African swine fever virus (ASFV)
4.1 Abstract
4.2 Introduction
4.3 Materials and Methods
4.4 Results
4.5 Discussion
Chapter 5 An Analysis of Key Parameters of Low Input Pig Production Systems along the Kenya-Uganda Border
5.1 Abstract
5.2 Introduction
5.3 Materials and Methods
5.4 Results
5.5 Discussion
5.6 Conclusion
Chapter 6 Spatial-Deterministic Model for the Transmission Dynamics of African swine fever virus
6.1 Abstract
6.2 Introduction
6.3 Methods
6.4 Results
6.5 Discussion
Chapter 7 Summarising Discussion and Recommendations
7.1 Summarising Discussion
7.2 Recommendations


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