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

COVID 19 - 通过考虑季节性和环境反应来改进 COVID-19 预测

• 批准号: NE/V009710/1
• 负责人: William Pearse
• 金额: $ 36.58万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV009710%2F1
• 关键词: COVID; 19; Improving; forecasts; accounting

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.

政策制定者迫切需要对SARS-CoV-2的中长期预测，但我们目前对该病毒的季节性动态一无所知。在缺乏数据的情况下，预测人员被迫假设SARS-CoV-2将对其他冠状病毒产生同样强烈的环境和季节反应[1]。我们将通过测量跨气候梯度的传输变化来解决这一重要的不确定性来源（例如，温度和湿度）来预测季节性反应。我们将通过利用与生态位建模（物种环境耐受性的测量）和时空替代（使用空间变化来预测时间变化）相关的经典生态学理论来实现这一目标。我们将把这种方法整合到帝国理工学院伦敦COVID-19应对小组开发的现有预测模型中，这些模型为全球的公共卫生应对提供了信息。这将使减缓战略能够考虑到气候驱动的传输空间变化，突出强调何时何地需要更强有力的干预措施。由于SARS-CoV-2正在进化，因此在传播过程中可能会发生适应性变化，我们还将开展初步工作，以评估该病毒在原位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，这将为制定针对COVID-19的有针对性的最佳干预策略提供信息。我们的工作重点是在秋季和冬季之前提供预测，利用流行病学家，定量生态学家，进化生物学家和生物信息学家的跨学科团队。[1]Kissler et al.（2020）Science eabb5793.

项目成果

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions.

• DOI: 10.1073/pnas.2019284118
• 发表时间: 2021-06-22
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Smith TP;Flaxman S;Gallinat AS;Kinosian SP;Stemkovski M;Unwin HJT;Watson OJ;Whittaker C;Cattarino L;Dorigatti I;Tristem M;Pearse WD
• 通讯作者: Pearse WD

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

AREAdata：随时间变化、不同尺度的空间单位平均的全球气候数据集

• DOI: 10.1101/2021.10.05.463057
• 发表时间: 2021
• 作者: Smith T

Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology

北美地甲虫（步甲科）形态的强性状相关性和系统发育信号

• DOI: 10.1002/ecs2.3832
• 发表时间: 2021
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Stachewicz J

Disorder or a new order: How climate change affects phenological variability

无序还是新秩序：气候变化如何影响物候变异

• DOI: 10.1002/ecy.3846
• 发表时间: 2022
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Stemkovski, Michael;Bell, James R.;Ellwood, Elizabeth R.;Inouye, Brian D.;Kobori, Hiromi;Lee, Sang Don;Lloyd‐Evans, Trevor;Primack, Richard B.;Templ, Barbara;Pearse, William D.
• 通讯作者: Pearse, William D.

Phylogenetic generalized linear mixed modeling presents novel opportunities for eco-evolutionary synthesis

• DOI: 10.1111/oik.08048
• 发表时间: 2021-03-25
• 期刊: OIKOS
• 影响因子: 3.4
• 作者: Gallinat, Amanda S.;Pearse, William D.
• 通讯作者: Pearse, William D.