Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach.

African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemic in South Africa; however, a unique control area has been set up in the Western Cape where increased surveillance and control measures have been put in place. A deterministic metapopulation model was developed to explore if an outbreak might occur, and how it might develop, if a latently infected horse was to be imported into the control area, by varying the geographical location and months of import. To do this, a previously published ordinary differential equation model was developed with a metapopulation approach and included a vaccinated horse population. Outbreak length, time to peak infection, number of infected horses at the peak, number of horses overall affected (recovered or dead), re-emergence, and Rv (the basic reproduction number in the presence of vaccination) were recorded and displayed using GIS mapping. The model predictions were compared to previous outbreak data to ensure validity. The warmer months (November to March) had longer outbreaks than the colder months (May to September), took more time to reach the peak, and had a greater total outbreak size with more horses infected at the peak. Rv appeared to be a poor predictor of outbreak dynamics for this simulation. A sensitivity analysis indicated that control measures such as vaccination and vector control are potentially effective to manage the spread of an outbreak, and shortening the vaccination window to July to September may reduce the risk of vaccine-associated outbreaks.

Parameterisation of a bluetongue virus mathematical model using a systematic literature review.

Bluetongue virus (BT) is a vector-borne virus that causes a disease, called bluetongue, which results in significant economic loss and morbidity in sheep, cattle, goats and wild ungulates across all continents of the world except Antarctica. Despite the geographical breadth of its impact, most BT epidemiological models are informed by parameters derived from the 2006-2009 BTV-8 European outbreak. The aim of this study was to develop a highly adaptable model for BT which could be used elsewhere in the world, as well as to identify the parameters which most influence outbreak dynamics, so that policy makers can be properly informed with the most current information to aid in disease planning. To provide a framework for future outbreak modelling and an updated parameterisation that reflects natural variation in infections, a newly developed and parameterised two-host, two-vector species ordinary differential equation model was formulated and analysed. The model was designed to be adaptable to be implemented in any region of the world and able to model both epidemic and endemic scenarios. It was parameterised using a systematic literature review of host-to-vector and vector-to-host transmission rates, host latent periods, host infectious periods, and vaccine protection factors. The model was demonstrated using the updated parameters, with South Africa as a setting based on the Western Cape's known cattle and sheep populations, local environmental parameters, and Culicoides spp. presence data. The sensitivity analysis identified that the duration of the infectious period for sheep and cows had the greatest impact on the outbreak length and number of animals infected at the peak of the outbreak. Transmission rates from cows and sheep to C. imicola midges greatly influenced the day on which the peak of the outbreak occurred, along with the duration of incubation period, and infectious period for cows. Finally, the protection factor of the vaccine had the greatest influence on the total number of animals infected. This knowledge could aid in the development of control measures. Due to gradual climate and anthropological change resulting in alterations in vector habitat suitability, BT outbreaks are likely to continue to increase in range and frequency. Therefore, this research provides an updated BT modelling framework for future outbreaks around the world to explore transmission, outbreak dynamics and control measures.

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

BACKGROUND: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. METHODS: Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. RESULTS: Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. CONCLUSION: This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.

Drivers and hazards of consumption of unpasteurised bovine milk and milk products in high-income countries.

Introduction: The consumption of dairy products contributes to health, nutrition, and livelihoods globally. However, dairy products do not come without microbiological food safety risks for consumers. Despite this risk, common hygiene measures in high-income countries, particularly pasteurisation, ensures that milk is safe, and is indeed frequently mandated by law. Nevertheless, over the past two decades, there has been a global increase in the number of consumers in high-income developed countries actively seeking out unpasteurised milk in liquid and product forms for perceived nutritional and health benefits, and improved taste. The often-anecdotal claims upon which consumers make such choices are not all supported by scientific evidence; however, some recent research studies have investigated (and in some cases demonstrated) the positive impact of unpasteurised milk consumption on the prevalence of asthma, atopy, rectal cancer and respiratory illness. Methods: To investigate the significance of unpasteurised milk and milk product consumption for human health in high-income countries, outbreak data between the years 2000 and 2018 were obtained for the United States of America, Canada, the European Union, the United Kingdom, Japan, New Zealand and Australia, which were then categorized into three World Health Organisation subregions: AMR A, EUR A and WPR A. Outbreak dynamic variables such as pathogens, the place of consumption, numbers of outbreaks and deaths per million capita, the average number of cases per outbreak and regulations were described and analysed using R Studio. To provide an overview of unpasteurised milk-related disease outbreaks, a rapid evidence review was also undertaken to establish an overview of what is known in the current literature about hazards and drivers of consumption. Results: Foodborne outbreaks associated with unpasteurised dairy consumption have risen in high-income countries over the period 2000 to 2018, with Campylobacter spp. being the most common aetiological agent responsible, followed by Escherichia coli and Salmonella spp. The most common places of consumption are on farms or in households, indicating individuals choose to drink unpasteurised milk, rather than a widespread distribution of the product, for example, at social events and in schools. Further study is needed to better understand contributing factors, such as cultural differences in the consumption of dairy products. Conclusion: There are several observable health benefits linked to consuming raw milk, but outbreaks associated with unpasteurised milk and milk products are on the rise. It cannot be definitively concluded whether the benefits outweigh the risks, and ultimately the decision lies with the individual consumer. Nevertheless, many countries have regulations in place to protect consumer health, acknowledging the definite risks to human health that unpasteurised dairy foods may pose, particularly from microbial hazards.

Socio-economic impacts of working horses in urban and peri-urban areas of the Cape Flats, South Africa.

In the Cape Flats townships, Cape Town, South Africa, there are more than 250 working cart horses. They serve the community with scrap metal and garden refuse removal, human transport and the selling of goods. A questionnaire was undertaken to understand the social and economic impacts of a horse and cart in the Cape Flats on individual owners and/or drivers, their households and the community. A mixture of classical quantitative questions combined with qualitative participatory technique questions were used. A total of 100 participants took part in the questionnaire, who cart with 163 horses between them. The majority (89%) identified the cart horse income as their primary income source. Apart from the participants, an additional 716 people were supported financially through this income, where the mean number of children supported was 2.9 (95% confidence interval [CI]: ±0.42) per interviewed participant. Scrap metal transportation was the most common work and the season (winter) had a negative impact on their ability to work. The spatial extent to which a cart horses work was determined and related back to the impact on the horse and participant of the survey. It was demonstrated that the cart horse industry had an impact not only on those who worked in the industry, but also on the surrounding residents, either through their work or through supporting others with their income. This study revealed that the concepts of 'One Health' and 'Health in Social-Ecological Systems', in action as horse and human health within the Cape Flats are closely intertwined.