School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.
University of Otago, PO Box 56, Dunedin, 9054, New Zealand.
Eur J Clin Microbiol Infect Dis. 2019 Jul;38(7):1307-1312. doi: 10.1007/s10096-019-03554-7. Epub 2019 May 8.
Surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness, yet representation of their spatiotemporal intensity remains limited. Using the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method of mapping epidemic evolution over space and time. Weekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by official national statistical region. Weekly standardised epidemic intensity was represented by a standard score (z-score) calculated using the standard deviation of below-median counts in the previous 52 weeks. A geographic information system (GIS) was used to present the epidemic progression. There were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. The GIS maps indicated areas of elevated epidemic intensity across Australia by week and area that were consistent with the observed start, peak and decline of the epidemic when compared with counts aggregated at the state and territory level. This simple, adaptable approach could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also facilitate increased understanding of geographic epidemic dynamics.
流感疫情监测是风险评估和大流行准备的重点，但对其时空强度的描述仍然有限。我们使用 2016 年澳大利亚甲型流感疫情，展示了一种简单但具有统计学意义的自适应方法，用于对疫情的时空演变进行映射。对 2016 年澳大利亚官方国家统计区域内实验室确诊的甲型流感感染人数进行了每周分析。每周的标准化疫情强度通过使用前 52 周中位数以下计数的标准差计算的标准得分（z 分数）来表示。地理信息系统（GIS）用于展示疫情进展。共收到 79628 例甲型流感感染通知。其中，79218 例（99.5%）分配到一个地理区域。GIS 地图按周和地区显示了澳大利亚各地疫情强度升高的区域，与州和地区一级汇总的病例相比，这些区域与疫情的实际起始、高峰和下降情况一致。这种简单、适应性强的方法可以在各种情况下和针对其他疾病提高当地疫情情报的质量。它也可能有助于更好地了解地理疫情动态。
