Marc Aurelien, Schiffer Joshua T, Mentré France, Perelson Alan S, Guedj Jérémie
T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
CPT Pharmacometrics Syst Pharmacol. 2025 Aug;14(8):1289-1297. doi: 10.1002/psp4.70055. Epub 2025 Jun 2.
Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.
数学模型已被使用约30年，以增进我们对病毒与宿主相互作用的理解，尤其是在慢性感染期间。在新冠疫情期间，这些模型被用于深入了解急性SARS-CoV-2感染的自然史、优化抗病毒治疗策略、理解与传播相关的因素以及优化监测系统。动物和人类系统中前所未有的多维免疫数据的可用性加速了建模的影响，这加强了实验人员和理论家之间的合作，并带来了令人兴奋的新建模和统计进展。在这篇小型综述中，我们审视了从新冠疫情中学到的经验教训，并讨论了疫情不同阶段病毒动力学数学模型提供的主要见解。尽管我们专注于呼吸道感染，但我们也考虑了预期未来由新出现或重新出现的病原体引发的急性感染的新发展领域。
