Diffusion modeling of COVID-19 under lockdown

Paola Di Carlo, Consolato M. Sergi, Teresa Rea, Nicola Serra

Risultato della ricerca: Articlepeer review

Abstract

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.
Lingua originaleEnglish
pagine (da-a)041903-
Numero di pagine6
RivistaPhysics of Fluids
Volume33
Stato di pubblicazionePublished - 2021

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.2200.2206???
  • ???subjectarea.asjc.3100.3104???
  • ???subjectarea.asjc.2200.2211???
  • ???subjectarea.asjc.2200.2210???
  • ???subjectarea.asjc.1500.1507???

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