The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.
The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.
Prev Vet Med. 2024 Nov;232:106328. doi: 10.1016/j.prevetmed.2024.106328. Epub 2024 Aug 23.
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.
蓝舌病病毒(BT)是一种经媒介传播的病毒,可引起蓝舌病,导致除南极洲以外的各大洲的绵羊、牛、山羊和野生有蹄类动物遭受严重的经济损失和发病。尽管 BT 的影响范围很广,但大多数 BT 流行病学模型都是根据 2006-2009 年 BTV-8 欧洲疫情的参数得出的。本研究的目的是开发一种适用于世界各地的高度适应性 BT 模型,并确定对疫情动态影响最大的参数,以便决策者能够获得最新信息,为疾病规划提供适当的信息。为了为未来的疫情建模提供框架,并反映感染的自然变化,我们制定并分析了一个新开发的、具有两个宿主、两个媒介的物种的常微分方程模型,对其进行了参数化。该模型旨在适应世界任何地区的实施,并能够模拟流行和地方性疫情。该模型使用宿主向媒介和媒介向宿主传播率、宿主潜伏期、宿主感染期和疫苗保护因子的系统文献综述进行参数化。该模型使用更新的参数进行了演示,以南非西开普省已知的牛和羊种群、当地环境参数和库蠓属的存在数据为基础。敏感性分析表明,绵羊和牛的感染期持续时间对疫情持续时间和疫情高峰期感染动物的数量影响最大。牛和绵羊向库蠓属传播率极大地影响了疫情高峰期的出现时间以及牛的潜伏期和感染期的持续时间。最后,疫苗的保护因子对感染的动物总数影响最大。这些知识可以帮助制定控制措施。由于气候和人类学的逐渐变化导致媒介栖息地适宜性的改变,BT 疫情的范围和频率可能会继续增加。因此,本研究为世界各地未来的疫情提供了一个更新的 BT 建模框架,以探索传播、疫情动态和控制措施。