From the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Data61, Sydney, Australia.
Kirby Institute, University of New South Wales, Sydney, Australia.
Epidemiology. 2020 Jan;31(1):90-97. doi: 10.1097/EDE.0000000000001133.
Melbourne, Australia, witnessed a thunderstorm asthma outbreak on 21 November 2016, resulting in over 8,000 hospital admissions by 6 P.M. This is a typical acute disease event. Because the time to respond is short for acute disease events, an algorithm based on time between events has shown promise. Shorter the time between consecutive incidents of the disease, more likely the outbreak. Social media posts such as tweets can be used as input to the monitoring algorithm. However, due to the large volume of tweets, a large number of alerts may be produced. We refer to this problem as alert swamping.
We present a four-step architecture for the early detection of the acute disease event, using social media posts (tweets) on Twitter. To curb alert swamping, the first three steps of the algorithm ensure the relevance of the tweets. The fourth step is a monitoring algorithm based on time between events. We experiment with a dataset of tweets posted in Melbourne from 2014 to 2016, focusing on the thunderstorm asthma outbreak in Melbourne in November 2016.
Out of our 18 experiment combinations, three detected the thunderstorm asthma outbreak up to 9 hours before the time mentioned in the official report, and five were able to detect it before the first news report.
With appropriate checks against alert swamping in place and the use of a monitoring algorithm based on time between events, tweets can provide early alerts for an acute disease event such as thunderstorm asthma.
2016 年 11 月 21 日,澳大利亚墨尔本发生雷暴哮喘事件,截至下午 6 点,有超过 8000 人住院。这是一个典型的急性疾病事件。由于急性疾病事件的响应时间很短,因此基于事件之间时间的算法显示出了前景。疾病连续发作之间的时间越短,爆发的可能性就越大。推文等社交媒体帖子可以用作监测算法的输入。但是,由于推文数量庞大,可能会产生大量警报。我们将此问题称为警报泛滥。
我们提出了一个使用 Twitter 上的社交媒体帖子(推文)来早期检测急性疾病事件的四步架构。为了遏制警报泛滥,算法的前三步确保了推文的相关性。第四步是基于事件之间时间的监测算法。我们使用 2014 年至 2016 年在墨尔本发布的推文数据集进行实验,重点关注 2016 年 11 月墨尔本的雷暴哮喘事件。
在我们的 18 个实验组合中,有三个在官方报告中提到的时间之前提前 9 小时检测到了雷暴哮喘事件,有五个在第一个新闻报道之前就检测到了。
通过适当的警报泛滥检查和使用基于事件之间时间的监测算法,推文可以为雷暴哮喘等急性疾病事件提供早期警报。