RAPID: Collaborative Research: The effects of evolutionary adaptations on the spreading of COVID-19

自二零一九年十二月开始，新型冠状病毒（已知会导致一种名为COVID-19的呼吸道疾病）迅速广泛传播，并被证明是影响全球人类健康和福祉的最具破坏性事件之一。关于COVID-19的一个关键科学目标是开发数学模型，帮助理解和预测其传播行为，并支持如何限制其传播的指导方针。在这个项目中，PI旨在通过将他们最近对新流行病模型的发现应用于COVID-19的传播来实现这些目标。PI将研究一个模型，该模型考虑了COVID-19突变为具有不同传播特征的不同菌株的可能性。通过考虑环境因素和人类行为变化（季节变化、旅行禁令等）导致的COVID-19传播行为的变化，其目的是获得有助于评估可采取的防止病毒传播的对策的有效性的结果，并帮助更好地为不同的变异情况，包括最坏的情况做好准备。大多数现有的流行病模型都假设，一个传染性个体会将她所感染的同一种病原体菌株传给她接触网络中的易感个体。这一假设在现实生活中可能不成立，因为病原体往往会随着时间的推移而进化，病原体的进化方式可能会导致其传播动力学发生重大变化。该项目旨在改进用于预测COVID-19传播的最先进的数学和计算模型，以纳入进化和突变的影响。这将通过利用PI最近的工作来完成，他们开发了一个数学模型来预测具有突变的多菌株流行病模型的传播动力学。通过将潜在的变化纳入再生数R 0，（由于对策或突变），PI旨在更好地了解COVID-19传播的未来进展;在不同情况下获得COVID-19传播的不同预测，包括广泛禁止旅行，学校/商店关闭，以及可能演变成不同的菌株;并增加公众对可采取的减缓COVID-19传播的各种对策的预期有效性的讨论。项目成果将广泛传播并纳入教学课程。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Started in December 2019, the novel coronavirus (known to cause a respiratory disease known as COVID-19) has spread rapidly and broadly and is proving to be one of the most devastating events that affect the health and well-being of humans all around the world. A key scientific goal concerning COVID-19 is to develop mathematical models that help in understanding and predicting its spreading behavior, as well as supporting guidelines on what can be done to limit its spread. In this project, the PIs aim to achieve these goals by applying their recent findings on a new epidemic model to the spread of COVID-19. The PIs will work on a model that considers the possibility of COVID-19 mutating into different strains with different spreading characteristics. By also considering changes in the spreading behavior of COVID-19 due to environmental factors and changes in human behavior (seasonal changes, travel bans, etc.), they aim to obtain results that will help assess the effectiveness of countermeasures that can be taken against the spread of the virus and to help better prepare for different mutation scenarios, including worst-cases. Most existing models of epidemics assume that an infectious individual passes the same pathogen strain that she was infected with to a susceptible individual in her contact network. This assumption may not hold in real-life as pathogens often evolve over time, and the ways a pathogen evolves might lead to significant changes in its spreading dynamics. This project aims to improve the state-of-the-art mathematical and computational models for predicting the spread of COVID-19 to incorporate the effects of evolution and mutations. This will be done by leveraging recent work of the PIs in which they developed a mathematical model to predict the spreading dynamics for multiple-strain epidemics models with mutations. By incorporating the potential changes in the reproduction number R0 (due to countermeasures or mutations), the PIs aim to obtain a better understanding of the future progress of the COVID-19 spread; obtain different predictions of the spread of COVID-19 under different scenarios including extensive bans on travel, school/shop closures, as well as potential evolution into different strains; and add to the public discourse on the expected effectiveness of various countermeasures that can be taken to slow down the spread of COVID-19. Project outcomes will be disseminated broadly and incorporated into teaching curricula. The project will also engage students from underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.