Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ ( R 0 = 3) and ‘slow’ ( R 0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
East Africa is a hotspot for foodborne diseases, including infection by nontyphoidal Salmonella (NTS), a zoonotic pathogen that may originate from livestock. Urbanization and increased demand for animal protein drive intensification of livestock production and food processing, creating risks and opportunities for food safety. We built a probabilistic mathematical model, informed by prior beliefs and dedicated stakeholder interviews and microbiological research, to describe sources and prevalence of NTS along the beef supply chain in Moshi, Tanzania. The supply chain was conceptualized using a bow tie model, with terminal livestock markets as pinch point, and a forked pathway postmarket to compare traditional and emerging supply chains. NTS was detected in 36 (7.7%) of 467 samples throughout the supply chain. After combining prior belief and observational data, marginal estimates of true NTS prevalence were 4% in feces of cattle entering the beef supply and 20% in raw meat at butcheries. Based on our model and sensitivity analyses, true NTS prevalence was not significantly different between supply chains. Environmental contamination, associated with butchers and vendors, was estimated to be the most likely source of NTS in meat for human consumption. The model provides a framework for assessing the origin and propagation of NTS along meat supply chains. It can be used to inform decision making when economic factors cause changes in beef production and consumption, such as where to target interventions to reduce risks to consumers. Through sensitivity and value of information analyses, the model also helps to prioritize investment in additional research.
This was a retrospective cohort study using data collected from a large-scale dairy herd in Kenya (n = 328 female animals), to investigate the effects of foot-and-mouth disease (FMD) on herd fertility performance following a confirmed outbreak in a regularly vaccinated herd. Kaplan-Meier graphs were used to depict differences in survival functions between exposure groups and Cox regression models were used to calculate hazard ratios (HR) for associations between being clinical FMD cases and the following fertility outcomes: age at first calving; fertility failure related culling (not in calf); time to first service; time to conception. Potential confounding variables investigated and controlled for were age, breed, parity, stage of lactation/gestation and eligibility for service. A case control study was nested within the cohort to investigate the effects of disease on conception HR following calving by comparing animals susceptible to fertility suppression at the time of the outbreak (cases) to animals that had conceived prior to the outbreak (controls).The median age of first calving in clinically affected young-stock was 2.7 months higher than non-clinical cases (adjusted HR = 0.37, 95%CI 0.21–0.67, P = 0.01). There was no evidence of a difference in fertility related culling and times to first service and conception. Animals susceptible to fertility suppression at the time of the outbreak had a lower hazard of conception compared to animals served prior to the outbreak (HR = 0.56, 95%CI 0.41–0.75, P = 0.01). Within the herd, the odds of being a case decreased with parity and age likely related to the lifetime number of vaccination doses received which may reduce the impact among older animals in the herd. Moreover, one would expect the impact to be higher in a non-vaccinating herd to be higher. Notwithstanding these limitations, the results of this study provide evidence that FMD outbreaks in endemic settings impact herd fertility performance. An increased age at first calving is likely to increase rearing costs and reduce an animal’s lifetime productivity while poorer conception rates will likely extend calving intervals. Impaired herd fertility and production will incur higher costs to the farmer and society as animals are less productive which for FMD can extend beyond the outbreak period where economic studies tend to focus. These impacts of FMD on herd fertility should be considered when conducting benefit-cost analyses of FMD control to inform resource allocation.
Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)—a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance.
Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF) --- a vaccine-preventable arboviral fever. While detailed livestock movement data are available in many countries, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of imperfect movement data on targeted vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modelling approach demonstrates how targeted interventions can be carefully applied to inform animal and public health policies on disease control planning in settings where detailed data on livestock movements are unavailable or imperfect due to a lack of data-gathering resources.
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