More or less deadly? A mathematical model that predicts SARS-CoV-2 evolutionary direction. (February 2023)

• Record Type: Journal Article
• Authors: Xu, Zhaobin; Wei, Dongqing; Zeng, Qiangcheng; Zhang, Hongmei; Sun, Yinghui; Demongeot, Jacques
• Is Part Of: Computers in biology and medicine. Volume 153(2023)
• Publication Date: 2023-02
• Subjects: SARS-CoV-2 -- Virulence -- Transmissibility -- Mathematical modeling -- Evolution direction; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106510
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: SARS-CoV-2 has caused tremendous deaths globally. It is of great value to predict the evolutionary direction of SARS-CoV-2. In this paper, we proposed a novel mathematical model that could predict the evolutionary trend of SARS-CoV-2. We focus on the mutational effects on viral assembly capacity. A robust coarse-grained mathematical model is constructed to simulate the virus dynamics in the host body. Both virulence and transmissibility can be quantified in this model. A delicate equilibrium point that optimizes the transmissibility can be numerically obtained. Based on this model, the virulence of SARS-CoV-2 might further decrease, accompanied by an enhancement of transmissibility. However, this trend is not continuous; its virulence will not disappear but remains at a relatively stable range. A virus assembly model which simulates the virus packing process is also proposed. It can be explained why a few mutations would lead to a significant divergence in clinical performance, both in the overall particle formation quantity and virulence. This research provides a novel mathematical attempt to elucidate the evolutionary driving force in RNA virus evolution. Highlights: A novel mathematical model that could predict the evolutionary trend of SARS-CoV-2 is proposed. Our model predicts the virulence of SARS-CoV-2 might further decrease, accompanied by an enhancement of transmissibility. Our model provides a possible mechanism behind the intricate trade-off relationship between virulence and transmissibility. Our model explained why a few mutations would lead to a significant divergence in clinical performance.