Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time.

Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection-time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant "half-life" relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one "half-life"), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions.

Exploratory Data Analysis of Genomic Sequence of Variants of SARS-CoV-2 Reveals Sequence Divergence and Mutational Localization.

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising.

Identifying Challenges for Clients in Adopting Sustainable Public Cloud Computing

Sustainable Cloud Computing is the modern era’s most popular technology. It is improving daily, offering billions of people sustainable services. Currently, three deployment models are available: (1) public, (2) private, and (3) hybrid cloud. Recently, each deployment model has undergone extensive research. However, relatively little work has been carried out regarding clients’ adoption of sustainable public cloud computing (PCC). We are particularly interested in this area because PCC is widely used worldwide. As evident from the literature, there is no up-to-date systematic literature review (SLR) on the challenges clients confront in PCC. There is a gap that needs urgent attention in this area. We produced an SLR by examining the existing cloud computing models in this research. We concentrated on the challenges encountered by clients during user adoption of a sustainable PCC. We uncovered a total of 29 obstacles that clients confront when adopting sustainable PCC. In 2020, 18 of the 29 challenges were reported. This demonstrates the tremendous threat that PCC still faces. Nineteen of these are considered critical challenges to us. We consider a challenge a critical challenge if its occurrence in the final selected sample of the paper is greater than 20%. These challenges will negatively affect client adoption in PCC. Furthermore, we performed three different analyses on the critical challenges. Our analysis may indicate that these challenges are significant for all the continents. These challenges vary with the passage of time and with the venue of publication. Our results will assist the client’s organization in understanding the issue. Furthermore, it will also help the vendor’s organization determine the potential solutions to the highlighted challenges.

Finding The Impact Of Agricultural Output, Population Growth Rate And Bank Branches On Demand For Money: An Extended Model

Role of money demand occupies a central place in regulating the monetary management of the economy and a topic of keen interest among researchers and academics (Sichei & Kamau, 2012). Growing money demand ensures an upsurge in the economic activity and vice versa. The present study analyzes the money demand function in Pakistan. The ARDL method to co-integration is used on data ranged between 1972 and 2018. The results showed that statistically significant effects of all main hypothesized factors including number of bank branches, population growth rate and agricultural output are found positive, negative and negative, respectively, both in the short-run and long- run. Whereas the effect of traditional factors viz., income and inflation rate on money demand appeared as positive, and statistically significant. The broad money demand function appeared to be stable in Pakistan. Policy to be focused on certain factors is discussed.

Correction to: Optimal control analysis of COVID-19 vaccine epidemic model: a case study.

[This corrects the article DOI: 10.1140/epjp/s13360-022-02365-8.].

Optimal control analysis of COVID-19 vaccine epidemic model: a case study

The purpose of this research is to explore the complex dynamics and impact of vaccination in controlling COVID-19 outbreak. We formulate the classical epidemic compartmental model by introducing vaccination class. Initially, the proposed mathematical model is analyzed qualitatively. The basic reproductive number is computed and its numerical value is estimated using actual reported data of COVID-19 for Pakistan. The sensitivity analysis is performed to analyze the contribution of model embedded parameters in transmission of the disease. Further, we compute the equilibrium points and discussed its local and global stability. In order to investigate the influence of model key parameters on the transmission and controlling of the disease, we perform numerical simulations describing the impact of various scenarios of vaccine efficacy rate and other controlling measures. Further, on the basis of sensitivity analysis, the proposed model is restructured to obtained optimal control model by introducing time-dependent control variables

Numerical simulation of a Caputo fractional epidemic model for the novel coronavirus with the impact of environmental transmission

The coronavirus infectious disease (COVID-19) is a novel respiratory disease reported in 2019 in China. The infection is very destructive to human lives and caused millions of deaths. Various approaches have been made recently to understand the complex dynamics of COVID-19. The mathematical modeling approach is one of the considerable tools to study the disease spreading pattern. In this article, we develop a fractional order epidemic model for COVID-19 in the sense of Caputo operator. The model is based on the effective contacts among the population and environmental impact to analyze the disease dynamics. The fractional models are comparatively better in understanding the disease outbreak and providing deeper insights into the infectious disease dynamics. We first consider the classical integer model studied in recent literature and then we generalize it by introducing the Caputo fractional derivative. Furthermore, we explore some fundamental mathematical analysis of the fractional model, including the basic reproductive number R0 and equilibria stability utilizing the Routh-Hurwitz and the Lyapunov function approaches. Besides theoretical analysis, we also focused on the numerical solution. To simulate the model, we use the well-known generalized Adams-Bashforth Moulton Scheme. Finally, the influence of some of the model essential parameters on the dynamics of the disease is demonstrated graphically.

CFD Simulation of the Airborne Transmission of COVID-19 Vectors Emitted during Respiratory Mechanisms: Revisiting the Concept of Safe Distance.

The airborne transmission of the COVID-19 virus has been suggested as a major mode of transmission in recent studies. In this context, we studied the spatial transmission of COVID-19 vectors in an indoor setting representative of a typical office room. Computational fluid dynamics (CFD) simulations were performed to study the airborne dispersion of particles ejected due to different respiratory mechanisms, i.e., coughing, sneezing, normal talking, and loud talking. Number concentration profiles at a distance of 2 m in front of the emitter at the ventilation rates of 4, 6, and 8 air changes per hour (ACH) were estimated for different combinations of inlet-outlet positions and emitter-receptor configurations. Apart from respiratory events, viz., coughing and sneezing characterized by higher velocity and concentration of ejected particles, normal as well as loud talking was seen to be carrying particles to the receptor for some airflow patterns in the room. This study indicates that the ″rule of thumb based safe distance approach″ cannot be a general mitigation strategy for infection control. Under some scenarios, events with a lower release rate of droplets such as talking (i.e., asymptomatic transmission) can lead to a high concentration of particles persisting for long times. For better removal, the study suggests ″air curtains″ as an appropriate approach, simultaneously highlighting the pitfalls in the ″higher ventilation rate for better removal″ strategy. The inferences for talking-induced particle transmissions are crucial considering that large populations of COVID-19-infected persons are projected to be asymptomatic transmitters.

Particle removal from air by face masks made from Sterilization Wraps: Effectiveness and Reusability.

Wearing face masks is highly recommended to prevent SARS-CoV-2 transmission in health care workers and for the general public. The demand for high quality face masks has seen an upsurge in the recent times, leading to exploration of alternative economic and easily available options, without compromising on the quality. Particle removal from air in terms of capture efficiency of the filter media or the face mask is a crucial parameter for testing and quality assurance. Short-term reusability of the face masks is also an important aspect as the demand for masks will potentially outstrip the supply in future. Sterilization Wraps, which are used to wrap sterile surgical instruments, have shown a promising performance in terms of removal of particles from air. In this study, we evaluate the particle filtration characteristics of face masks made of 2 different metric weights [45 and 60 gram per square metre (GSM)] respectively, using locally available Sterilization Wraps. The aerosol filtration characteristics were also studied after sterilisation by different techniques such as heat with 50% humidity (thermal treatment), ethylene oxide (ETO), steam and radiation dose of 30kGy. We found that 60 GSM face mask had particle capture efficiency of 94% for total particles greater than 0.3 microns and this capture efficiency was maintained even after sterilisation with ETO and thermal treatment. The cost of producing these masks was 30 US cents/mask at our institute. Our study suggests that sterilization wrap material made of non-woven polypropylene spunbond-meltblown-spunbond (SMS) fibres could be an appropriate readily available inexpensive material for making face masks or N95 respirators.

Emerging Prevention and Treatment Strategies to Control COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has now become a serious global threat after inflicting more than 8 million infections and 425,000 deaths in less than 6 months. Currently, no definitive treatment or prevention therapy exists for COVID-19. The unprecedented rise of this pandemic has rapidly fueled research efforts to discover and develop new vaccines and treatment strategies against this novel coronavirus. While hundreds of vaccines/therapeutics are still in the preclinical or early stage of clinical development, a few of them have shown promising results in controlling the infection. Here, in this review, we discuss the promising vaccines and treatment options for COVID-19, their challenges, and potential alternative strategies.

nCOV-19 peptides mass fingerprinting identification, binding, and blocking of inhibitors flavonoids and anthraquinone of Moringa oleifera and hydroxychloroquine.

An rare pandemic of viral pneumonia occurs in December 2019 in Wuhan, China, which is now recognized internationally as Corona Virus Disease 2019 (COVID-19), the etiological agent classified as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). According to the World Health Organization (WHO), it has so far expanded to more than 213 countries/territories worldwide. Our study aims to find the viral peptides of SARS-COV-2 by peptide mass fingerprinting (PMF) in order to predict its novel structure and find an inhibitor for each viral peptide. For this reason, we calculated the mass of amino acid sequences translated from the SARS-CoV2 whole genome and identify the peptides that may be a target for inhibition. Molecular peptide docking with Moringa oleifera, phytochemicals (aqueous and ethanolic) leaf extracts of flavonoids (3.56 ± 0.03), (3.83 ± 0.02), anthraquinone (11.68 ± 0.04), (10.86 ± 0.06) and hydroxychloroquine present therapy of COVID-19 in Pakistan for comparative study. Results indicate that 15 peptides of SARS-CoV2 have been identified from PMF, which is then used as a selective inhibitor. The maximum energy obtained from AutoDock Vina for hydroxychloroquine is -5.1 kcal/mol, kaempferol (flavonoid) is -6.2 kcal/mol, and for anthraquinone -6 kcal/mol. Visualization of docking complex, important effects are observed regarding the binding of peptides to drug compounds. In conclusion, it is proposed that these compounds are effective antiviral agents against COVID-19 and can be used in clinical trials.Communicated by Ramaswamy H. Sarma.