Savill Nicholas J, St Rose Suzanne G, Woolhouse Mark E J
Centre for Infectious Diseases, Ashworth Laboratories, Institute of Immunology and Infection Research, University of Edinburgh, The King's Buildings, West Mains Road, Edinburgh EH9 3JT, UK.
J R Soc Interface. 2008 Dec 6;5(29):1409-19. doi: 10.1098/rsif.2008.0133.
Rapid detection of infectious disease outbreaks is often crucial for their effective control. One example is highly pathogenic avian influenza (HPAI) such as H5N1 in commercial poultry flocks. There are no quantitative data, however, on how quickly the effects of HPAI infection in poultry flocks can be detected. Here, we study, using an individual-based mathematical model, time to detection in chicken flocks. Detection is triggered when mortality, food or water intake or egg production in layers pass recommended thresholds suggested from the experience of past HPAI outbreaks. We suggest a new threshold for caged flocks--the cage mortality detection threshold--as a more sensitive threshold than current ones. Time to detection is shown to depend nonlinearly on R0 and is particularly sensitive for R0<10. It also depends logarithmically on flock size and number of birds per cage. We also examine how many false alarms occur in uninfected flocks when we vary detection thresholds owing to background mortality. The false alarm rate is shown to be sensitive to detection thresholds, dependent on flock size and background mortality and independent of the length of the production cycle. We suggest that current detection thresholds appear sufficient to rapidly detect the effects of a high R0 HPAI strain such as H7N7 over a wide range of flock sizes. Time to detection of the effects of a low R0 HPAI strain such as H5N1 can be significantly improved, particularly for large flocks, by lowering detection thresholds, and this can be accomplished without causing excessive false alarms in uninfected flocks. The results are discussed in terms of optimizing the design of disease surveillance programmes in general.
快速检测传染病爆发对于有效控制疫情往往至关重要。一个例子是商业家禽群中高致病性禽流感（HPAI），如H5N1。然而，目前尚无关于家禽群中HPAI感染影响能多快被检测到的定量数据。在此，我们使用基于个体的数学模型研究鸡群中疫情的检测时间。当蛋鸡的死亡率、食物或水摄入量或产蛋量超过过去HPAI疫情经验所建议的推荐阈值时，即触发检测。我们提出了一种针对笼养鸡群的新阈值——笼养死亡率检测阈值，作为比当前阈值更敏感的阈值。结果表明，检测时间非线性地依赖于R0，并且对于R0<10时特别敏感。它还对数依赖于鸡群规模和每笼鸡的数量。我们还研究了由于背景死亡率而改变检测阈值时，未感染鸡群中会出现多少误报。结果表明，误报率对检测阈值敏感，依赖于鸡群规模和背景死亡率，且与生产周期长度无关。我们认为，当前的检测阈值似乎足以在广泛的鸡群规模范围内快速检测到高R0的HPAI毒株（如H7N7）的影响。对于低R0的HPAI毒株（如H5N1），通过降低检测阈值，尤其是对于大型鸡群，可以显著缩短检测时间，并且在未感染鸡群中不会引起过多误报。本文从总体上优化疾病监测计划设计的角度对这些结果进行了讨论。
