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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. Author summary: African Horse Sickness is a devastating global equine disease which results in high numbers of deaths. In South Africa it is considered endemic, and this poses implications for trade and exportation of horses out of the country. As a result, South Africa has developed a "control area" where there are increased surveillance and control strategies to keep it free from African horse sickness. Our research explores where and how an outbreak may occur in the "control area" if a horse harbouring the virus were to be imported into the region. We developed a mathematical simulation which considered the differences in horse population numbers, midge population numbers, environmental temperature, and vaccination status of horses in different areas across the "control area". This helped to determine how many horses would be affected at the end of the outbreak as well as at the peak, how long the outbreak would occur, and the reproduction number (Rv), and how this varied across the study area. Our research uncovers insights into the African horse sickness outbreak dynamics of the "control area" and helps provide information to further develop control strategies. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike. [ABSTRACT FROM AUTHOR]

Combines daily station rainfall data in South Africa from 1936 to 1999 into homogeneous rainfall regions using Ward's clustering method. Various rainfall characteristics are calculated for the summer season, defined as December to February. The length of the period between rain days has a low correlation to season totals, demonstrating that seasons with a high total rainfall may still contain prolonged dry periods. These additional rainfall characteristics are important to end-users, and the analysis offers a starting point for seeking physical relationships between rainfall characteristics and the general circulation. Preliminary studies show that the vertical mean wind is related to rainfall characteristics in South Africa. Given that general circulation models capture this part of the circulation adequately, seasonal forecasts of rainfall characteristics become plausible.