Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency

Sharov, Konstantin S.

doi:10.1016/j.chaos.2020.110295

Cited by 43 publications

(36 citation statements)

References 57 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if ART interruption lasts for two-three months and especially six months and more, HIV may become a very dangerous risk factor for COVID-19 clinical course and outcome. Therefore, providing a proper ART to PLWH during the COVID-19 pandemic is crucial for medical systems around the world ( Nakamoto et al, 2020 , Patel et al, 2020 , Sharov, 2020a , Sharov, 2020b ). The healthcare systems must be sustained by governmental actions aimed at prevention of pharmaceutical supply chain disruptions, as in the situation of truly global society such disruptions may bring on tremendous harm to the world population ( Donskikh, 2019 , Etienne et al, 2020 , Guo et al, 2020 , Lysenko, 2019 , Moskovkin, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

HIV/SARS-CoV-2 co-infection: T cell profile, cytokine dynamics and role of exhausted lymphocytes

Sharov

2021

International Journal of Infectious Diseases

Self Cite

View full text Add to dashboard Cite

Highlights HIV-1 and SARS-CoV-2 are likely to exhibit a synergic effect on immunity in serious cases of HIV/SARS-CoV-2 co-infection. Exhausted T lymphocyte dynamics along with IL-10 and TGFβ growth during COVID-19 course may be an effective prognostic marker of this synergy as well as overall prognosis of the co-infection disease. HIV without antiretroviral therapy may be a dangerous comorbidity of COVID-19. Conversely, COVID-19 clinical course and immune status of HIV patients with a proper ART did not differ significantly from the control group of SARS-CoV-2 positive people without HIV. SARS-CoV-2 was detected to exasperate a loss of lymphocytic cytokine-producing ability with respect to IL-2, TNFα and IFNγ.

show abstract

Section: Discussionmentioning

confidence: 99%

HIV/SARS-CoV-2 co-infection: T cell profile, cytokine dynamics and role of exhausted lymphocytes

Sharov

2021

International Journal of Infectious Diseases

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this sense, when the parameters come from the literature, they can generate results slightly different from a real epidemic because of variability concerning environmental, socio-cultural aspects, the virulence of the pathogens, and resistance-susceptibility profile tial equations [31], monte carlo method [32], the [33] gillespie's first reaction method, among other methods.…”

Section: Deterministic Versus Stochasticmentioning

confidence: 99%

Mathematical Modeling of the Infectious Diseases: Key Concepts and Applications

da¹,

Amanajas²,

Lima³

et al. 2021

J Infect Dis Epidemiol

View full text Add to dashboard Cite

Background:The transmission dynamics of infectious diseases is susceptible to changes governed by several factors, whose recognition is critical for the rational development of strategies for prevention and control, as well as for developing health policies. In this context, mathematical modeling can provide useful insights concerning transmission patterns and detection of parameters to mitigate disease in the population.Objectives: To didactically present the mathematical modeling of infectious diseases for health students and professionals as a tool in epidemiology.Methods: A comprehensive literature review was conducted with articles obtained from PubMed, Web of Science, and Google Scholar databases with the term infectious diseases mathematical modeling.Results: There are two main types of models built with a basis on fixed or probabilistic rates that describe individuals' movement in compartments that designate stages in the natural history of the disease. In this sense, deterministic models are non-probabilistic and stochastic models are probabilistic, the first one helps in developing a prospection of possible scenarios in epidemiology, while the second is more applicable in the study of the influence of variables in the transmission dynamics. Conclusion:The infectious agents are in a constant process of biological evolution, as well as the environment and human conceptions, culture, and behavior, implying a constant transformation in the epidemiological profile of infectious diseases, in which the mathematical modeling can provide support to the decision-making processes concerning epidemiology and public health.

show abstract

“…Machine learning models have been used in many ways to study and understand the different aspects of COVID-19 pandemic. These models have been previously used for forecasting the COVID-19 cases [ [27] , [28] , [29] ], propose the potential antibodies [ 30 ], understand the possible evolutions of the virus [ 31 ], understand the economic and social effects of social distancing [ 32 , 33 ], understand the efficiency of lockdowns [ 34 ], study the transmission and spread of the virus [ 35 , 36 ]. Data driven models have also been used to analyze the SARS-CoV-2 mutations.…”

Section: Introductionmentioning

confidence: 99%

Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering

Mullick¹,

Magar²,

Jhunjhunwala³

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

The SARS-CoV-2 virus like many other viruses has transformed in a continual manner to give rise to new variants by means of mutations commonly through substitutions and indels. These mutations in some cases can give the virus a survival advantage making the mutants dangerous. In general, laboratory investigation must be carried to determine whether the new variants have any characteristics that can make them more lethal and contagious. Therefore, complex and time-consuming analyses are required in order to delve deeper into the exact impact of a particular mutation. The time required for these analyses makes it difficult to understand the variants of concern and thereby limiting the preventive action that can be taken against them spreading rapidly. In this analysis, we have deployed a statistical technique Shannon Entropy, to identify positions in the spike protein of SARS Cov-2 viral sequence which are most susceptible to mutations. Subsequently, we also use machine learning based clustering techniques to cluster known dangerous mutations based on similarities in properties. This work utilizes embeddings generated using language modeling, the ProtBERT model, to identify mutations of a similar nature and to pick out regions of interest based on proneness to change. Our entropy-based analysis successfully predicted the fifteen hotspot regions, among which we were able to validate ten known variants of interest. As the situation of SARS-COV-2 virus rapidly evolves we believe that the remaining nine mutational hotspots may contain variants that can emerge in the future. We believe that this may be promising in helping the research community to devise therapeutics based on probable new mutation zones in the viral sequence and resemblance in properties of various mutations

show abstract

Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency

Cited by 43 publications

References 57 publications

HIV/SARS-CoV-2 co-infection: T cell profile, cytokine dynamics and role of exhausted lymphocytes

HIV/SARS-CoV-2 co-infection: T cell profile, cytokine dynamics and role of exhausted lymphocytes

Mathematical Modeling of the Infectious Diseases: Key Concepts and Applications

Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering

Contact Info

Product

Resources

About