使用确定性集合种群方法模拟南非控制区非洲马病的出现和传播。

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-09-06 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011448
Joanna N de Klerk, Erin E Gorsich, John D Grewar, Benjamin D Atkins, Warren S D Tennant, Karien Labuschagne, Michael J Tildesley
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引用次数: 0

摘要

非洲马病是一种马的轨道病毒,由叮咬侏儒的Latreille库蚊传播。在过去的80年里,它在欧洲、远东和中东、北非、东南亚和撒哈拉以南非洲的马群中引发了几次毁灭性的疫情。该病在南非流行;然而,在西开普省设立了一个独特的控制区,加强了监视和控制措施。开发了一个确定性集合种群模型,通过改变地理位置和输入月份,探讨如果将潜在感染的马输入控制区,是否会发生疫情,以及疫情如何发展。为了做到这一点,之前发表的常微分方程模型是用集合种群方法开发的,其中包括接种疫苗的马种群。使用GIS地图记录和显示疫情爆发时间、感染高峰的时间、感染高峰期的马匹数量、受影响的马匹总数(康复或死亡)、重新出现和Rv(接种疫苗时的基本繁殖数量)。将模型预测与以前的疫情数据进行比较,以确保有效性。较温暖的月份(11月至3月)比较冷的月份(5月至9月)爆发的时间更长,达到峰值需要更多的时间,并且总爆发规模更大,高峰时感染的马更多。在这一模拟中,Rv似乎是疫情动态的较差预测因子。敏感性分析表明,疫苗接种和病媒控制等控制措施可能有效地控制疫情的传播,将疫苗接种窗口缩短至7月至9月可能会降低疫苗相关疫情的风险。
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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.

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PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
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7.10
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