Contribution of market value chain to the control of African swine fever in Zambia.

African swine fever (ASF) is a worldwide disease of pigs endemic in most sub-Saharan African countries. Zambia has been experiencing outbreaks of ASF for many years because the disease is endemic in the eastern part of the country, with incursion into the central part of Lusaka Province. The latest outbreaks of ASF in Lusaka occurred in 2013 with substantial pig mortalities, loss in trade, and cost of control measures and compensation of affected farmers. The aims of the study were to identify market value chain-related factors that were associated with ASF outbreaks and assess why these outbreaks are becoming frequent despite control measures being put in place. Using a mixed-method design, participants involved in the value chain were purposively sampled. Some pig farmers were included using a respondent-driven technique. Farmers came from Lusaka, Chilanga, Kafue, and Chongwe districts. Other participants included district veterinary officers, veterinary assistants, police officers, and veterinary staff manning veterinary checkpoints, abattoir and processing plant managers, meat inspectors, market chairpersons, and traders. Semi-structured questionnaires, in-depth interviews, and direct observations were used to collect data to come up with narrations, tables, and flow charts. In assessing the contribution of the value chain in ASF, aspects of ASF screening, market availability and procedures, knowledge on ASF transmission, occurrence of ASF outbreak, and regulation of pig movement were investigated. Despite government ASF control measures being applied, the following were noted: (1) low awareness levels of ASF transmission among pig farmers and traders; (2) only 50% of farmers had their animals screened for ASF before sale; (3) all the markets did not have the pork inspected; (4) laxity in enforcing livestock movement control because of inadequate police and veterinary staff manning checkpoints; (5) lack of enforcement of meat inspection and food safety regulations at pig markets; and (6) inadequate and bureaucratic ASF screening. Improving biosecurity; sensitizing farmers, traders, and all stakeholders in the pig value chain on ASF prevention and control; reinforcement of staff at checkpoints; and regulation of pig markets are some of the ways in which future outbreaks can be prevented.

Epidemiological Dynamics of Foot-and-Mouth Disease in the Horn of Africa: The Role of Virus Diversity and Animal Movement.

The Horn of Africa is a large area of arid and semi-arid land, holding about 10% of the global and 40% of the entire African livestock population. The region's livestock production system is mainly extensive and pastoralist. It faces countless problems, such as a shortage of pastures and watering points, poor access to veterinary services, and multiple endemic diseases like foot-and-mouth disease (FMD). Foot-and-mouth disease is one of the most economically important livestock diseases worldwide and is endemic in most developing countries. Within Africa, five of the seven serotypes of the FMD virus (FMDV) are described, but serotype C is not circulating anymore, a burden unseen anywhere in the world. The enormous genetic diversity of FMDV is favored by an error-prone RNA-dependent RNA polymerase, intra-typic and inter-typic recombination, as well as the quasi-species nature of the virus. This paper describes the epidemiological dynamics of foot-and-mouth disease in the Horn of Africa with regard to the serotypes and topotypes distribution of FMDV, the livestock production systems practiced, animal movement, the role of wildlife, and the epidemiological complexity of FMD. Within this review, outbreak investigation data and serological studies confirm the endemicity of the disease in the Horn of Africa. Multiple topotypes of FMDV are described in the literature as circulating in the region, with further evolution of virus diversity predicted. A large susceptible livestock population and the presence of wild ungulates are described as complicating the epidemiology of the disease. Further, the husbandry practices and legal and illegal trading of livestock and their products, coupled with poor biosecurity practices, are also reported to impact the spread of FMDV within and between countries in the region. The porosity of borders for pastoralist herders fuels the unregulated transboundary livestock trade. There are no systematic control strategies in the region except for sporadic vaccination with locally produced vaccines, while literature indicates that effective control measures should also consider virus diversity, livestock movements/biosecurity, transboundary trade, and the reduction of contact with wild, susceptible ungulates.

Livestock movement patterns in the main livestock production provinces of Lao PDR.

Foot-and-mouth disease (FMD) is a highly infectious transboundary disease that is endemic and affects the livelihood of smallholder farmers in Lao People's Democratic Republic (PDR). Knowledge about livestock movement patterns is important for preventing the spread of FMD between villages. This study describes the livestock movement patterns in Champasak, Savannakhet and Xiangkhouang provinces of Lao PDR. Face-to-face interviews were conducted with randomly selected villagers (n = 195) and traders (n = 169) in 115 villages between February and March 2019. Livestock owners commonly purchased (mainly breeding) animals from other smallholders (81%) and sold (mainly slaughter) animals to traders (76%) or other smallholders (16%), typically within the same district and province. The median inter-village trade distance was 20-30 km, with an average frequency of 4 trades per village per month. Traders purchased animals from smallholders (71%) and middlemen (25%) located within their district. It was common for many traders (74%) to retain animals at their property before selling, typically a median of 4 beef cattle per trader. Local trades within the district were far more common (72%) than distant trades. The movements of grazing/fattening large ruminants between villages were reported in 30% of the villages in all three provinces and occurred mostly within the same district or province in short distance (6 km). Social Network Analysis has identified animal movement hubs in the three provinces which could be targeted for FMD control and surveillance. Movements of animals for further use (fattening/ reproduction), long-distance movements and frequent local movements described in this area have important implications for FMD circulation. The findings from the study will inform FMD spread simulation models for Lao PDR. The knowledge gained from these data will also help the Lao PDR authorities understand the patterns of animal movements associated with disease spread.

Leveraging livestock movements to urban slaughterhouses for wide-spread Rift Valley fever virus surveillance in Western Kenya.

Rift Valley fever virus (RVFV) is an economically devastating, zoonotic arbovirus endemic across Africa with potential to cause severe disease in livestock and humans. Viral spread is primarily driven by movement of domestic ruminants and there is a high potential for transboundary spread. Despite influx of livestock to urban areas in response to the high demand for meat and animal products, RVFV has not been detected in any urban center. The objectives of this study were to determine the feasibility of assessing risk of RVFV introduction to urban Kisumu, Kenya, by testing slaughtered livestock for RVFV exposure and mapping livestock origins. Blood was collected from cattle, sheep, and goats directly after slaughter and tested for anti-RVFV IgG antibodies. Slaughterhouse businessmen responded to a questionnaire on their individual animals' origin, marketplace, and transport means. Thereafter, we mapped livestock flow from origin to slaughterhouse using participatory methods in focus group discussions with stakeholders. Qualitative data on route choice and deviations were spatially integrated into the map. A total of 304 blood samples were collected from slaughtered livestock in October and November 2021. Most (99%) of animals were purchased from 28 different markets across eight counties in Western Kenya. The overall RVFV seroprevalence was 9% (19% cattle, 3% in sheep, and 7% in goats). Migori County bordering Tanzania had the highest county-level seroprevalence (34%) and 80% of all seropositive cattle were purchased at the Suba Kuria market in Migori County. Road quality and animal health influenced stakeholders' decisions for choice of transport means. Overall, this proof-of-concept study offers a sampling framework for RVFV that can be locally implemented and rapidly deployed in response to regional risk. This system can be used in conjunction with participatory maps to improve active livestock surveillance and monitoring of RVFV in Western Kenya, and these methods could be extrapolated to other urban centers or livestock diseases.

Why sold, not culled? Analysing farm and animal characteristics associated with livestock selling practices.

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.

Understanding Pig and Poultry Trade Networks and Farming Practices Within the Pacific Islands as a Basis for Surveillance.

Pacific Island countries have large pig and poultry populations. Yet little is known about patterns of contact between animals and how this influences disease spread in these islands. The objectives of this study were to examine farmer practices and the movements of pig and poultry within the Pacific Islands using questionnaires and social network analysis (SNA) tools to understand disease spread in the region. Questionnaire-based surveys were conducted in Fiji, Papua New Guinea (PNG), Solomon Islands and Vanuatu with interviews of 310 pig farmers and 491 poultry farmers. Pacific Island farmers were found to have few animals (median = 7 pigs/farm, IQR 4-12), (median = 50 chicken/farm, IQR 23-52), (median = 10 ducks/farm, IQR 4-25), (median = 12 Muscovy ducks/farm, IQR 7-28) and a diversified number of species. A large proportion of farmers (44.6-61.3%) do not implement any preventive or control measures, yet the majority (80.6-88%) did not experience any animal diseases over the past 12 months. Most farmers never ask for veterinary care, never engage in laboratory testing and do not report when their animals show clinical signs. Many pig farmers (31.8%) trade within their communities only and sell (24.5%) directly to consumers which reduces the risk of diseases spreading. Our results show an association between farmers that report having had disease on their farm in the past 12 months and movements of animals on and off their farms. The capitals of the studied provinces in PNG, Vanuatu and Solomon Islands were identified as the most connected nodes of both pig and poultry trade, while Fiji networks appeared much less connected. Our study found that farmer practices increased the risk of disease spread, but this was currently limited by trading practices. The SNA results serve as a basis for more targeted disease surveillance and better use of available resources for disease prevention and control.

Evaluating the efficacy of regionalisation in limiting high-risk livestock trade movements.

Many countries implement regionalisation as a measure to control economically important livestock diseases. Given that regionalisation highlights the difference in disease risk between animal subpopulations, this may discourage herd managers in low-risk areas from purchasing animals from high-risk areas to protect the disease-free status of their herds. Using bovine tuberculosis (bTB) in New Zealand as a case example, we develop a novel network simulation model to predict how much the frequency of cattle movements between different disease control areas (DCAs) could theoretically change if herd managers adopted the safest practices (preferentially purchasing cattle from areas with the lowest risk of bTB), if herd managers adopted the riskiest practices (preferentially purchasing cattle from areas with the greatest risk of bTB), or if herd managers made trade decisions completely at random (purchasing cattle without consideration for bTB disease risk). A modified configuration wiring algorithm was used in the network simulation model to preserve key temporal, spatial, and demographic attributes of cattle movement patterns. The simulated frequencies of cattle movements between DCAs in each of the three behavioural scenarios were compared with the actual frequency of cattle movements that occurred between 1st July 2010 and 30th June 2011. Our results showed that the observed frequency of cattle movements from high-risk areas into low-risk areas was significantly less than if trade decisions were made completely at random, but still significantly greater than if herd managers made the safest possible trade decisions. This suggests that while New Zealand cattle farmers may have adopted risk-averse trading behaviour in response to regionalisation, there are other underlying factors driving livestock trade, such as established supplier-buyer relationships and heterogeneous individual perceptions towards disease risk, which may reduce the potential efficacy of regionalisation as a disease control strategy. Physical constraints and socio-psychological factors that determine herd managers' livestock trading behaviour warrant further studies to better understand how herd managers respond to future livestock disease regulations. The flexibility of a network re-wiring framework presented in this study allows such a behavioural response to be incorporated into a disease simulation model, which will in turn facilitate a better evaluation of disease control strategies.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

Evaluating empirical contact networks as potential transmission pathways for infectious diseases.

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.