COVID 19 - Improving COVID-19 forecasts by accounting for seasonality and environmental responses

政策制定者迫切需要对SARS-CoV-2进行中长期预测，但我们目前对病毒的季节性动态一无所知。在缺乏数据的情况下，预测者被迫假设SARS-CoV-2将对其他冠状病毒产生同样强烈的环境和季节性反应。我们将通过测量跨气候梯度（如温度和湿度）的传输变化来预测季节响应，从而解决这一重要的不确定性来源。我们将利用与生态位建模（测量物种的环境耐受性）和时空替代（使用跨空间的变化来预测随时间的变化）相关的经典生态学理论来实现这一点。我们将把这一方法整合到伦敦帝国理工学院COVID-19应对小组开发的现有预测模型中，该模型已为全球公共卫生应对提供了信息。这将使缓解战略考虑到气候驱动的传播空间变化，强调需要在何时何地采取更强有力的干预措施。由于SARS-CoV-2正在进化，因此在传播时可能会适应，我们还将开展初步工作，以评估病毒在现场适应环境条件的程度。该项目将为SARS-CoV-2传播率、季节性和环境因素之间的关系提供新的见解，这将为制定有针对性的COVID-19最佳干预策略提供信息。我们利用由流行病学家、定量生态学家、进化生物学家和生物信息学家组成的跨学科团队，优先考虑在秋冬之前提供预测Kissler et al. (2020) Science eabb5793。

Policy-makers urgently need medium- and long-term forecasts for SARS-CoV-2, but we are currently ignorant of the virus' seasonal dynamics. In the absence of data, forecasters have been forced to assume that SARS-CoV-2 will have identically strong environmental and seasonal responses to other coronaviruses[1]. We will address this important source of uncertainty by measuring changes in transmission across climatic gradients (e.g., temperature and humidity) to forecast seasonal responses. We will do so by leveraging classical ecological theory related to niche modelling (the measurement of species' environmental tolerances) and space-for-time substitution (using variation across space to predict variation through time). We will integrate this approach into existing forecasting models developed by the Imperial College London COVID-19 Response Team, which have informed the public-health response worldwide. This will allow mitigation strategy to account for climate-driven spatial changes in transmission, highlighting where and when stronger interventions are needed. As SARS-CoV-2 is evolving, and so potentially adapting, as it spreads, we will also conduct preliminary work to assess the extent to which the virus is adapting in situ to environmental conditions. This project will provide new insight into the relationships between SARS-CoV-2 transmission rates, seasonality, and environmental factors, which will inform the development of targeted optimal intervention strategies against COVID-19. We have prioritised our efforts around delivering forecasts before autumn and winter, leveraging an inter-disciplinary team of epidemiologists, quantitative ecologists, evolutionary biologists, and bioinformaticians.[1] Kissler et al. (2020) Science eabb5793.