Rossi Gianluigi, De Leo Giulio A, Pongolini Stefano, Natalini Silvano, Vincenzi Simone, Bolzoni Luca
Dipartimento di Bioscienze, Università di Parma, Parco Area delle Scienze 11/A, I-43124 Parma, Italy.
Stanford University, Hopkins Marine Station, Pacific Grove, CA 93950, USA.
Epidemics. 2015 Jun;11:62-70. doi: 10.1016/j.epidem.2015.02.007. Epub 2015 Mar 6.
Assessing the performance of a surveillance system for infectious diseases of domestic animals is a challenging task for health authorities. Therefore, it is important to assess what strategy is the most effective in identifying the onset of an epidemic and in minimizing the number of infected farms. The aim of the present work was to evaluate the performance of the bovine tuberculosis (bTB) surveillance system in the network of dairy farms in the Emilia-Romagna (ER) Region, Italy. A bTB-free Region since 2007, ER implements an integrated surveillance strategy based on three components, namely routine on-farm tuberculin skin-testing performed every 3 years, tuberculin skin-testing of cattle exchanged between farms, and post-mortem inspection at slaughterhouses. We assessed the effectiveness of surveillance by means of a stochastic network model of both within-farm and between-farm bTB dynamics calibrated on data available for ER dairy farms. Epidemic dynamics were simulated for five scenarios: the current ER surveillance system, a no surveillance scenario that we used as the benchmark to characterize epidemic dynamics, three additional scenarios in which one of the surveillance components was removed at a time so as to outline its significance in detecting the infection. For each scenario we ran Monte Carlo simulations of bTB epidemics following the random introduction of an infected individual in the network. System performances were assessed through the comparative analysis of a number of statistics, including the time required for epidemic detection and the total number of infected farms during the epidemic. Our analysis showed that slaughterhouse inspection is the most effective surveillance component in reducing the time for disease detection, while routine surveillance in reducing the number of multi-farms epidemics. On the other hand, testing exchanged cattle improved the performance of the surveillance system only marginally.
对家畜传染病监测系统的性能进行评估，对卫生当局来说是一项具有挑战性的任务。因此，评估哪种策略在识别疫情爆发和尽量减少受感染农场数量方面最有效非常重要。本研究的目的是评估意大利艾米利亚 - 罗马涅（ER）地区奶牛场网络中牛结核病（bTB）监测系统的性能。自2007年以来，ER地区一直无牛结核病，该地区实施了一项基于三个组成部分的综合监测策略，即每3年进行一次农场常规结核菌素皮肤试验、对农场间交换的牛进行结核菌素皮肤试验以及在屠宰场进行尸检。我们通过一个基于ER奶牛场可用数据校准的农场内和农场间bTB动态随机网络模型，评估了监测的有效性。针对五种情况模拟了疫情动态：当前的ER监测系统、我们用作表征疫情动态基准的无监测情况、另外三种情况，即每次去除一个监测组成部分，以概述其在检测感染方面的重要性。对于每种情况，我们在网络中随机引入一个受感染个体后，对bTB疫情进行了蒙特卡罗模拟。通过对包括疫情检测所需时间和疫情期间受感染农场总数在内的一些统计数据进行比较分析，评估了系统性能。我们的分析表明，屠宰场检查是减少疾病检测时间方面最有效的监测组成部分，而常规监测在减少多农场疫情数量方面效果显著。另一方面，对交换牛进行检测仅略微提高了监测系统的性能。
