Viral immune evasion by sequence variation is a significant barrier to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine design and coronavirus disease-2019 diffusion under lockdown are unpredictable with subsequent waves. Our group has developed a computational model rooted in physics to address this challenge, aiming to predict the fitness landscape of SARS-CoV-2 diffusion using a variant of the bidimensional Ising model (2DIMV) connected seasonally. The 2DIMV works in a closed system composed of limited interaction subjects and conditioned by only temperature changes. Markov chain Monte Carlo method shows that an increase in temperature implicates reduced virus diffusion and increased mobility, leading to increased virus diffusion.

中文翻译：

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锁定条件下COVID-19的扩散建模

通过序列变异进行病毒免疫逃逸是严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）疫苗设计的重要障碍，并且在随后的浪潮中无法预测锁定下的冠状病毒疾病-2019扩散。我们的小组已经开发出了一个扎根于物理学的计算模型来应对这一挑战，旨在使用季节性连接的二维伊辛模型（2DIMV）的变体来预测SARS-CoV-2扩散的适应度景观。2DIMV在封闭的系统中工作，该系统由有限的交互对象组成，并且仅受温度变化的影响。马尔可夫链蒙特卡罗方法显示，温度升高意味着病毒扩散减少和迁移率增加，导致病毒扩散增加。