Mutation induced infection waves in diseases like COVID-19

Abstract

After almost 4 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which can self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages - if vaccination progresses too slowly - mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which can occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. Our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

“…Historically, overshoots in infected cases are unavoidable, even with the implementation of rigorous prevention measures. These phases are usually termed as ‘n-th waves’ [58] and they occur mainly due to non-deterministic transmission from asymptomatic cases, alongside with evolution of variants of SARS-CoV-2 virus [59] , we may conclude that the change in infected population is not only proportional to the current infected number but also the time, in the neighborhood of critical time gap, beyond which the transmission of asymptomatic cases reaches significant levels and the infected number starts overshooting. Due to the random nature of the occurrence of this phenomenon, there is a lack of synchronicity in implemented measures to deal with the corresponding situation, leading to an inverse effect in the number of recovered individuals.…”

“…One of the important objectives of governmental intervention in a pandemic system is to control its spread, which directly implies implementing policies that lead to overall decreasing infected numbers and causalities. The decrease in infected number may slow down due to transmission of the virus from asymptomatic cases, inefficient vaccination schemes and mutation of associated virus [59] . In all variations, the decisions made at a particular time, directly and indirectly, control the unobservable and intractable causes, in terms of time and intensity of the effect, and therefore, the purpose of abstraction of inference model to tackle this situation.…”

How varying intervention, vaccination, mutation and ethnic conditions affect COVID-19 resurgence

“…In Italy, the effects of the two waves on public health and their impact trends, fatality rates, admissions to ICUs and confirmed cases were shown to be statistically different 11 . Lastly, several studies have predicted the occurrences of second waves or COVID-19 resurgence associated with external factors such as easing interventions [12][13][14] or intervention fatigue (government and community reluctance to interventions) 15 , regrowth patterns under different (e.g., mobility) scenarios 16 , estimated the risk or trend of resurgence [17][18][19][20] and the impact of integrating physical distancing and vaccination 21 , quarantine, testing and contact tracing 22 , non-pharmaceutical public health intervention 23 , social distancing and loss of immunity 24 , virus mutation 25 and air pollution 26 on future waves, and urged prompt and effective actions to prevent COVID-19 resurgence. By forecasting second waves based on behavior disease interactions, it is believed that the second waves are related to the interactions between disease dynamics and social processes 27 .…”

Anti-Entry Activity of Natural Flavonoids against SARS-CoV-2 by Targeting Spike RBD

“…As of now, there have been 645 million confirmed cases of COVID-19 worldwide, including 6.64 million deaths [4]. While progress has been made in the development of vaccines and antiviral drugs, the increasing number of infections, the uneven global distribution of vaccines and the emergence of new SARS-CoV-2 variant strains necessitate the ongoing search for potential antiviral agents [5][6][7]. Natural products are an attractive source of antiviral agents in the face of a challenging drug development process [8][9][10].…”