Understanding the dynamics and drivers of the COVID-2019 epidemic using real-time outbreak analytics

跟踪和预测流行病可以帮助制定公共卫生措施，以便迅速和适当地应对不断变化的威胁。我们将对新出现的数据进行统计模型拟合，以监测与传播性和严重性相关的关键流行病学和临床参数，以及它们对卫生服务的可能影响。数学模型将适用于监测数据，以预测未来的病例和住院情况，并评估不同公共卫生政策的影响。重要的是，我们还将随着时间的推移衡量人们的行为，以发现应对疫情的变化，评估他们采取了哪些措施来降低风险，以及他们从哪里获得证据。这些信息将用于改进我们的模型和改进流行病学预测。它还将允许公共卫生当局调整其信息传递，以确保人们对风险变化作出适当反应。我们将在易于使用的网站上迅速总结和分享从我们对新数据的分析、流行病预测或我们的受访者小组获得的信息，以便科学家、卫生工作者和公众能够最好地了解COVID-19构成的威胁以及降低风险的不同措施的效果。

Tracking and predicting an epidemic can help tailor public health measures so that werespond quickly and appropriately to evolving threats. We will fit statistical models toemerging data to monitor key epidemiological and clinical parameters related totransmissibility and severity, and their likely impact on health services. Mathematicalmodels will be fitted to surveillance data to forecast future cases and hospitalisations andevaluate the impact of different public health policies. Importantly, we will also measurepeople’s behaviour over time to detect changes in response to the epidemic, assessingwhat measures they take to reduce risk and where they get their evidence from. Thisinformation will be used to refine our models and improve epidemiological forecasting. Itwill also allow public health authorities to tailor their messaging to ensure that peoplerespond appropriately to changes in risk. The information that we generate, either fromour analyses of the emerging data, epidemic forecasts, or from our panel of respondents,will be rapidly summarised and shared on easy-to-use websites so that scientists, healthworkers and the public can be best informed about the threats posed by COVID-19 andthe efficacy of different measures to reduce risk.