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The present novel coronavirus (COVID-19) infection has engendered a worldwide crisis on an enormous scale within a very short period. The effective solution for this pandemic is to recognize the nature and spread of the disease so that appropriate policies can be framed. Mathematical modelling is always at the forefront to understand and provide an...
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The COVID-19 pandemic led authorities all over the world to imposing travel restrictions both on a national and on an international scale. Understanding the effect of such restrictions requires analysis of the role of commuting and calls for a metapopulation modeling that incorporates both local, intra-community infection and population exchange be...
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... Zhao [6] considered asymptomatic out of the infectives in the SEIAR model. Soares [7], Srivasrav et al. [8] and Varghese et al. [9] included mild/moderate compartment and severe/hospitalized compartment in their studies, of which the main results showed that mild/moderate cases played the vital role in infection scale. ...
This paper proposes the symptom-based SEMIR compartmental model with age groups and fluctuations from the perspective of the managers of the local government. The basic reproduction number of the total population is given by the next generation matrix method, the group-based basic reproduction number are then followed. The unique global positive solution is derived by using the Lyapunov function. Furthermore, the sufficient conditions of the stochastic extinction and the stochastic persistence of the SEMIR model are studied. When Fuzhou COVID-19 epidemic is chosen from the surveillance data for the further numerical investigation, the data fittings of the SEMIR model reveal that age groups are profitable to control the infection scale of intensive cases, and that the susceptibility to infection of age groups are higher than those within the total population. Alternatively, the scenario investigations show that the prompt treatments are conducive to the reduction of infection scale for intensive cases.
... In mathematical epidemiology, compartmental models of type SIR or SEIR are widely used to describe and explain outbreaks of epidemics [1][2][3][4][5][6][7][8]. The classical SEIR models monitor compartments (i) S Susceptible individuals (those who have not yet been infected by the disease and may become so), (ii) E Exposed individuals (those in the incubation period), (iii) I Infectious individuals (those able to spread the disease) and (iv) R Recovered/Removed individuals (those who cannot become infectious anymore; they are either recovered or deceased). ...
We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.
... There are few studies involving mathematical models to study the COVID-19 situation in Oman. Abraham Varghese et al. [25] developed a mathematical model to analyze the pandemic's nature using Oman's data. The model is an extension of SEIR where they expanded the infected compartments into mild, moderate, severe and critical, based on the clinical stages of infection. ...
In this paper, we build a mathematical model for the dynamics of COVID-19 to assess the impact of placing healthy individuals in quarantine and isolating infected ones on the number of hospitalization and intensive care unit cases. The proposed model is fully analyzed in order to prove the positivity of solutions, to study the local and global stability of the disease-free equilibria and to drive the basic and control reproduction numbers of the model. Oman COVID-19 data is used to calibrate the model and estimate the parameters. In particular, the published data for the year 2020 is used, when two waves of the disease hit the country. Moreover, this period of time is chosen when no vaccine had been introduced, but only the non-pharmaceutical intervention (NPI) strategies were the only effective methods to control the spread and, consequently, control the hospitalization cases to avoid pressuring the health system. Based on the estimated parameters, the reproduction number and contribution of different transmission routes are approximated numerically. Sensitivity analysis is performed to identify the significant parameters in spreading the disease. Numerical simulation is carried out to demonstrate the effects of quarantine and isolation on the number of hospitalized cases.
... Varghese et al. [10] formulated a deterministic compartmental model including various stages of infection to study the spreading of COVID-19; they estimated the model parameters by fitting the model with the reported data in Oman. They proved the model's steady state, stability, and final pandemic size mathematically. ...
... We also set lower and upper bounds for each parameter depending on the estimation of some biological and precautionary measures criteria. The fixed parameters shown in Table 3 are based on Oman population statistics and a previous study [10] about COVID-19 in Oman. The remaining 24 parameters were fitted and are The graphs in Figure 5 represent the predicted situation of the pandemic for months following the three months used to estimate the parameters. ...
A COVID-19 deterministic compartmental mathematical model with different types of quarantine and isolation is proposed to investigate their role in the disease transmission dynamics. The quarantine compartment is subdivided into short and long quarantine classes, and the isolation compartment is subdivided into tested and non-tested home-isolated individuals and institutionally isolated individuals. The proposed model has been fully analyzed. The analysis includes the positivity and boundedness of solutions, calculation of the control reproduction number and its relation to all transmission routes, existence and stability analysis of disease-free and endemic equilibrium points and bifurcation analysis. The model parameters have been estimated using a dataset for Oman. Using the fitted parameters, the estimated values of the control reproduction number and the contribution of all transmission routes to the reproduction number have been calculated. Sensitivity analysis of the control reproduction number to model parameters has also been performed. Finally, numerical simulations to demonstrate the effect of some model parameters related to the different types of quarantine and isolation on the disease transmission dynamics have been carried out, and the results have been demonstrated graphically.
... Susceptible implies capable of contracting a pathogen, infected implies capable of replicating and spreading the pathogen and removed refers to either recovery (expulsion of the pathogen and immunity) or death. Additional compartments [1,49] and stratified or heterogenous populations [50][51][52][53] result in more sophisticated deterministic, compartment models. ...
The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic epidemic compartment model and an alternative to the Susceptible-Infected-Removed (SIR) model prototype. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and some common host behavioural characteristics. The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number () for the United Kingdom (UK) could have been projected on the individual states of the United States of America (USA) prior to being detected in the USA. The Imperial College London (ICL) R0 estimate for the UK is projected onto each USA state. The difference between these projections and ICL estimates for USA states is either not statistically significant on the paired student t-test or epidemiologically insignificant. Projection provides a baseline for evaluating the real-time impact of an intervention. Sensitivity analysis was conducted because of considerable variance in parameter estimates across studies. Although the HI-STR predicts that increasing symptomatic ratio and inherently immune ratio reduce R0, relative to the uncertainty in the estimates of R0 for the ancestral SARS-CoV2, the projection is insensitive to the inherently immune ratio and the symptomatic ratio.
... In 2021, some new deterministic and stochastic models have been proposed. Regarding the deterministic ones (Carcione et al., 2020;Kumar et al., 2021;Lawal & Vincent, 2021;Mandal et al., 2021;Piccirillo, 2021;Varghese et al., 2021), the main difference, when compared to past studies (Choi & Ki, 2020;Ferguson, Laydon et al., 2020;Ferguson, Walker et al., 2020;Khan & Atangana, 2020;T. M. Chen et al., 2020;Yang et al., 2021), is the compartmental susceptible-exposed-infective-recovered (SEIR) approach (for more details see Bartlett, 1957). ...
This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID‐19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID‐19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business‐as‐usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.
... COVID-19 victims eventually die as a result of persistent elevations in inflammatory mediators and extensive organ damage throughout the culmination stage [98][99][100]. Antibody-mediated humoral immune responses to SARS-CoV-2 infection are critical in the progression of COVID-19 illness [101,102]. In addition, outcomes from clinical research employing convalescent plasma and intravenous immunoglobulins (IVIG) to treat COVID-19 patients with passive antibody therapy have been published [103][104][105]. ...
... Changing fluid antibodies to a dry powder frame is one approach to improve immunization toughness [232,341,350]. In terms of drying, vacuum drying, drying with a desiccant, and lyophilization are all methods of drying biopharmaceuticals such as immunizations [78,100,102]. The prescribed procedure for drying biopharmaceuticals is lyophilization [33,40,339,367]. ...
To prevent the coronavirus disease 2019 (COVID-19) pandemic and aid restoration to prepandemic normality, global mass vaccination is urgently needed. Inducing herd immunity through mass vaccination has proven to be a highly effective strategy for preventing the spread of many infectious diseases, which protects the most vulnerable population groups that are unable to develop immunity, such as people with immunodeficiencies or weakened immune systems due to underlying medical or debilitating conditions. In achieving global outreach, the maintenance of the vaccine potency, transportation, and needle waste generation become major issues. Moreover, needle phobia and vaccine hesitancy act as hurdles to successful mass vaccination. The use of dissolvable microneedles for COVID-19 vaccination could act as a major paradigm shift in attaining the desired goal to vaccinate billions in the shortest time possible. In addressing these points, we discuss the potential of the use of dissolvable microneedles for COVID-19 vaccination based on the current literature.
... For example, model [26], called SEIHRD, consists of seven states, including Susceptible (S), Exposed (E), Infected (I), Hospitalized (H), Recovered (R), and Death (D), and considers social distancing, as an attitude or behavior which can change the behaviors and decrease contact rates that makes to reduce the transmission of infectious and control the diseases. Paper [27] has proposed deterministic compartmental model SEAMHCRD, which includes various stages of infection, such as Mild, Moderate, Severe, Critical, based on clinical stages of infection. The simulation results have shown that there is no need for complete lockdown, and values on transmission rates can be reduced by proper contact tracing mechanisms and effective social distancing measures. ...
An outbreak of a new coronavirus infection was first recorded in Wuhan, China, in December 2019. On January 30, 2020, the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on March 11, it a pandemic. As of January 2022, over 340 million cases have been reported worldwide ; more than 5.5 million deaths have been confirmed, making the COVID-19 pandemic one of the deadliest in history. The digitalization of all spheres of society makes it possible to use mathematical and simulation mod-eling to study the development of the virus. Building adequate models of the epidemic process will make it possible not only to predict its dynamics but also to conduct experimental studies to identify factors affecting the development of a pandemic, determine the behavior of the virus in certain areas, assess the effectiveness of measures aimed at stopping the spread of infection, as well as assess the resources needed to counter the epidemic growth of the disease. This study aims to develop three regression models of the COVID-19 epidemic process in given territories and to investigate the experimental results of the simulation. The research is targeted at the COVID-19 epidemic process. The research subjects are methods and models of epidemic process simulation, which include machine learning methods, particularly linear regression, Ridge regression, and Lasso regression. To achieve the research aim, we have used forecasting methods and have built the COVID-19 epidemic process and regression models. As a result of experiments with the developed model, the predictive dynamics of the epidemic process of COVID-19 in Ukraine, Germany, Japan, and South Korea for 3, 7, 10, 14, 21, and 30 days were obtained. The authorities making decisions on the implementation of anti-epidemic measures can use such predictions to solve the problems of operational analysis of the epidemic situation, an analysis of the effectiveness of already implemented anti-epidemic measures, medium-term planning of resources needed to combat the pandemic, etc. Conclusions. This paper describes experimental research on implementing three regression models of the COVID-19 epidemic process. These are models of linear regression, Ridge regression, and Lasso regression. COVID-19 daily new cases statistics were verified by these models for Ukraine, Germany, Japan, and South Korea, provided by the Johns Hopkins Coronavirus Resource Center. All built models have sufficient accuracy to make decisions on the implementation of anti-epidemic measures to combat the COVID-19 pandemic in the selected area. Depending on the forecast period, regression models can be used to solve different Public Health tasks.
The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-STR) prototype is a deterministic compartment model for epidemics and an alternative to the Susceptible-Infected-Removed (SIR) model. The HI-STR predicts that pathogen transmission depends on host population characteristics including population size, population density and social behaviour common within that population.
The HI-STR prototype is applied to the ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) to show that the original estimates of the Coronavirus Disease 2019 (COVID-19) basic reproduction number R 0 for the United Kingdom (UK) could have been projected onto the individual states of the United States of America (USA) prior to being detected in the USA.
The Imperial College London (ICL) group’s estimate of R 0 for the UK is projected onto each USA state. The difference between these projections and the ICL’s estimates for USA states is either not statistically significant on the paired Student t -test or not epidemiologically significant.
The SARS-CoV2 Delta variant’s R 0 is also projected from the UK to the USA to prove that projection can be applied to a Variant of Concern (VOC). Projection provides both a localised baseline for evaluating the implementation of an intervention policy and a mechanism for anticipating the impact of a VOC before local manifestation.
