Ssematimba Amos, Malladi Sasidhar, Bonney Peter J, St Charles Kaitlyn M, Hutchinson Holden C, Schoenbaum Melissa, Marusak Rosemary, Culhane Marie R, Cardona Carol J
Secure Food Systems Team, University of Minnesota, Saint Paul, Minnesota, United States of America.
Department of Mathematics, Faculty of Science, Gulu University, Gulu, Uganda.
PLoS One. 2024 Dec 13;19(12):e0310733. doi: 10.1371/journal.pone.0310733. eCollection 2024.
Following confirmation of the first case of the ongoing U.S. HPAI H5N1 epizootic in commercial poultry on February 8, 2022, the virus has continued to devastate the U.S. poultry sector and the pathogen has since managed to cross over to livestock and a few human cases have also been reported. Efficient outbreak management benefits greatly from timely detection and proper identification of the pathways of virus introduction and spread. In this study, we used changes in mortality rates as a proxy for HPAI incidence in a layer, broiler and turkey flock together with diagnostic test results to infer within-flock HPAI transmission dynamics. Mathematical modeling techniques, specifically the Approximate Bayesian Computation algorithm in conjunction with a stochastic within-flock HPAI transmission model were used in the analysis. The time window of HPAI virus introduction into the flock (TOI) and the adequate contact rate (ACR) were estimated. Then, using the estimated TOI together with the day when the first HPAI positive sample was collected from the flock, we calculated the most likely time to first positive sample (MTFPS) which reflects the time to HPAI detection. The estimated joint (i.e., all species combined) median of the MTFPS for different flocks was six days, the joint median most likely ACR was 6.8 newly infected birds per infectious bird per day, the joint median R0 was 13 and the joint median number of test days per flock was two. These results were also grouped by species and by epidemic phase and discussed accordingly. We conclude that this findings from this and other related studies are beneficial for the different stakeholders in outbreak management. We recommend that combining TOI analysis with complementary approaches such as phylogenetic analyses is critically important for improved understanding of disease transmission pathways. The estimated parameters can also be used to parametrize mathematical models that can guide the design of surveillance protocols, risk analyses of HPAI spread, and emergency preparedness for HPAI outbreaks.
2022年2月8日美国商业家禽中首次确诊当前高致病性禽流感（HPAI）H5N1疫情病例后，该病毒继续重创美国家禽业，此后病原体已传播至家畜，还报告了一些人类感染病例。高效的疫情管理在很大程度上受益于及时发现以及正确识别病毒引入和传播途径。在本研究中，我们将死亡率变化作为蛋鸡、肉鸡和火鸡群中HPAI发病率的替代指标，并结合诊断测试结果来推断鸡群内HPAI传播动态。分析中使用了数学建模技术，特别是近似贝叶斯计算算法以及鸡群内HPAI随机传播模型。估计了HPAI病毒引入鸡群的时间窗口（TOI）和充足接触率（ACR）。然后，将估计的TOI与从鸡群中采集到首个HPAI阳性样本的日期相结合，计算出首次出现阳性样本的最可能时间（MTFPS），该时间反映了HPAI检测时间。不同鸡群MTFPS的估计联合中位数（即所有物种合并）为6天，联合中位数最可能ACR为每天每只感染鸡有6.8只新感染鸡，联合中位数R0为13，每个鸡群的联合中位数检测天数为2天。这些结果还按物种和疫情阶段进行了分组并相应讨论。我们得出结论，本研究及其他相关研究的这些发现对疫情管理中的不同利益相关者有益。我们建议将TOI分析与系统发育分析等补充方法相结合，对于更好地理解疾病传播途径至关重要。估计的参数还可用于为数学模型设定参数，这些模型可指导监测方案的设计、HPAI传播的风险分析以及HPAI疫情的应急准备。
