Mathematical models for devising the optimal SARS-CoV-2 strategy for eradication in China, South Korea, and Italy

Fractional epidemic model of coronavirus disease with vaccination and crowding effects

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh-Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable ifR 0 < 1 and endemic equilibrium is locally asymptotically stable ifR 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable ifR 0 < 1 and endemic equilibrium is globally asymptotically stable ifR 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.

A systematic review on impact of SARS-CoV-2 infection

Innumerable pathogens including RNA viruses have catastrophic pandemic propensity, in turn, SARS-CoV-2 infection is highly contagious. Emergence of SARS-CoV-2 variants with high mutation rate additionally codifies infectious ability of virus and arisen clinical imputations to human health. Although, our knowledge of mechanism of virus infection and its impact on host system has been substantially demystified, uncertainties about the emergence of virus are still not fully understood. To date, there are no potentially curative drugs are identified against the viral infection. Even though, drugs are repurposed in the initial period of infection, many are significantly negative in clinical trials. Moreover, the infection is dependent on organ status, co-morbid conditions, variant of virus and geographic region. This review article aims to comprehensively describe the SARS-CoV-2 infection and the impacts in the host cellular system. This review also briefly provides an overview of genome, proteome and metabolome associated risk to infection and the advancement of therapeutics in SARS-CoV-2 infection management.

Angiotensin-Converting Enzyme 2 SNPs as Common Genetic Loci and Optimal Early Identification Genetic Markers for COVID-19

Background: Angiotensin-converting enzyme 2 (ACE2) is implicated as a host cell receptor that causes infection in the pathogenesis of coronavirus disease 2019 (COVID-19), and its genetic polymorphisms in the ACE2 gene may promote cardiovascular disease and systemic inflammatory injury in COVID-19 patients. Hence, the genetic background may potentially explain the broad interindividual variation in disease susceptibility and/or severity. Methods: Genetic susceptibility to COVID-19 was analyzed by examining single-nucleotide polymorphisms (SNPs) of ACE2 in 246 patients with COVID-19 and 210 normal controls using the TaqMan genotyping assay. Results: We demonstrated that the ACE2 SNPs rs4646142, rs6632677, and rs2074192 were associated with COVID-19 (for all, p  <  0.05), and the differences in the ACE2 SNPs rs4646142 and rs6632677 were correlated with COVID-19-related systemic inflammatory injury and cardiovascular risk. Specifically, rs4646142 was associated with high-sensitivity C-reactive protein (hs-CRP), prealbumin (PAB), apolipoprotein A (APOA), high-density lipoprotein (HDL), and acid glycoprotein (AGP) levels. Rs6632677 was also associated with elevated CRP, acid glycoprotein (AGP), and haptoglobin (HPT). Conclusions: Our results suggest that the ACE2 SNPs rs4646142 and rs6632677 may be common genetic loci and optimal early identification genetic markers for COVID-19 with cardiovascular risk.

Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?

The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19’s impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19’s impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments’ outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19’s impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.

You are only as safe as your riskiest contact: Effective COVID-19 vaccine distribution using local network information

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Using simulation to evaluate nomination of most popular contacts for vaccination.

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Simulating spread of COVID-19 across two contact networks among high-schoolers.

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Targeting in this way can reduce spread to the susceptible population by 20% or more.

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Results are robust in a synthetic network replicating spread in a small town.

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Results are robust across a wide range of infectiousness, and mistaken nomination.

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals – those who are most likely to spread the disease. But surveying the population’s social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals.

Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain

Countries affected by the coronavirus epidemic have reported many infected cases and deaths based on world health statistics. The crowding factor, which we named "crowding effects," plays a significant role in spreading the diseases. However, the introduction of vaccines marks a turning point in the rate of spread of coronavirus infections. Modeling both effects is vastly essential as it directly impacts the overall population of the studied region. To determine the peak of the infection curve by considering the third strain, we develop a mathematical model (susceptible-infected-vaccinated-recovered) with reported cases from August 01, 2021, till August 29, 2021. The nonlinear incidence rate with the inclusion of both effects is the best approach to analyze the dynamics. The model's positivity, boundedness, existence, uniqueness, and stability (local and global) are addressed with the help of a reproduction number. In addition, the strength number and second derivative Lyapunov analysis are examined, and the model was found to be asymptotically stable. The suggested parameters efficiently control the active cases of the third strain in Pakistan. It was shown that a systematic vaccination program regulates the infection rate. However, the crowding effect reduces the impact of vaccination. The present results show that the model can be applied to other countries' data to predict the infection rate.

How optimal allocation of limited testing capacity changes epidemic dynamics

Insufficient testing capacity has been a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and a concentration parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the concentration parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and non-clinical testing, even if extremely unfocused, becomes optimal. We further highlight the advantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.

A model of the innate immune response to SARS-CoV-2 in the alveolar epithelium

We present a differential equation model of the innate immune response to SARS-CoV-2 within the alveolar epithelium. Critical determinants of the viral dynamics and host response, including type I and type II alveolar epithelial cells, interferons, chemokines, toxins and innate immune cells, are included. We estimate model parameters, compute the within-host basic reproductive number, and study the impacts of therapies, prophylactics, and host/pathogen variability on the course of the infection. Model simulations indicate that the innate immune response suppresses the infection and enables the alveolar epithelium to partially recover. While very robust antiviral therapy controls the infection and enables the epithelium to heal, moderate therapy is of limited benefit. Meanwhile interferon therapy is predicted to reduce viral load but exacerbate tissue damage. The deleterious effects of interferon therapy are especially apparent late in the infection. Individual variation in ACE2 expression, epithelial cell interferon production, and SARS-CoV-2 spike protein binding affinity are predicted to significantly impact prognosis.

Cyber Physical Systems Dependability Using CPS-IOT Monitoring

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

Experimental Models of SARS-CoV-2 Infection: Possible Platforms to Study COVID-19 Pathogenesis and Potential Treatments

In December 2019, a novel coronavirus crossed species barriers to infect humans and was effectively transmitted from person to person, leading including vaccines and antiviral drugs that could prevent or limit the burden or transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health priority. It is thus of utmost importance to assess possible therapeutic strategies against SARS-CoV-2 using experimental models that recapitulate aspects of the human disease. Here, we review available models currently being developed and used to study SARS-CoV-2 infection and highlight their application to screen potential therapeutic approaches, including repurposed antiviral drugs and vaccines. Each identified model provides a valuable insight into SARS-CoV-2 cellular tropism, replication kinetics, and cell damage that could ultimately enhance understanding of SARS-CoV-2 pathogenesis and protective immunity. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.