Retkute Renata, Jewell Chris P, Van Boeckel Thomas P, Zhang Geli, Xiao Xiangming, Thanapongtharm Weerapong, Keeling Matt, Gilbert Marius, Tildesley Michael J
School of Life Sciences and Institute of Mathematics, University of Warwick, UK.
Faculty of Health and Medicine, Furness College, Lancaster University, UK.
Prev Vet Med. 2018 Nov 1;159:171-181. doi: 10.1016/j.prevetmed.2018.09.014. Epub 2018 Sep 19.
The Highly Pathogenic Avian Influenza (HPAI) subtype H5N1 virus persists in many countries and has been circulating in poultry, wild birds. In addition, the virus has emerged in other species and frequent zoonotic spillover events indicate that there remains a significant risk to human health. It is crucial to understand the dynamics of the disease in the poultry industry to develop a more comprehensive knowledge of the risks of transmission and to establish a better distribution of resources when implementing control. In this paper, we develop a set of mathematical models that simulate the spread of HPAI H5N1 in the poultry industry in Thailand, utilising data from the 2004 epidemic. The model that incorporates the intensity of duck farming when assessing transmision risk provides the best fit to the spatiotemporal characteristics of the observed outbreak, implying that intensive duck farming drives transmission of HPAI in Thailand. We also extend our models using a sequential model fitting approach to explore the ability of the models to be used in "real time" during novel disease outbreaks. We conclude that, whilst predictions of epidemic size are estimated poorly in the early stages of disease outbreaks, the model can infer the preferred control policy that should be deployed to minimise the impact of the disease.
高致病性禽流感(HPAI)H5N1亚型病毒在许多国家持续存在,并在家禽、野生鸟类中传播。此外,该病毒已在其他物种中出现,频繁的人畜共患病溢出事件表明对人类健康仍存在重大风险。了解家禽业中该疾病的动态对于更全面地了解传播风险以及在实施控制时更好地分配资源至关重要。在本文中,我们利用2004年疫情的数据,开发了一组数学模型来模拟HPAI H5N1在泰国家禽业中的传播。在评估传播风险时纳入养鸭强度的模型最符合观察到的疫情的时空特征,这意味着集约化养鸭推动了泰国HPAI的传播。我们还使用顺序模型拟合方法扩展了我们的模型,以探索这些模型在新疾病爆发期间“实时”使用的能力。我们得出结论,虽然在疾病爆发的早期阶段对疫情规模的预测估计不佳,但该模型可以推断出应部署的首选控制策略,以尽量减少疾病的影响。