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Abstract COVID-19 is an infectious disease that causes millions of deaths worldwide, and it is the principal leading cause of morbidity and mortality in all nations. Although the governments of developed and developing countries are enforcing their universal control strategies, more precise and cost-effective single or combination interventions are required to control COVID-19 outbreaks. Using proper optimal control strategies with appropriate cost-effectiveness analysis is important to simulate, examine, and forecast the COVID-19 transmission phase. In this study, we developed a COVID-19 mathematical model and considered two important features including direct link between vaccination and latently population, and practical healthcare cost by separation of infections into Mild and Critical cases. We derived basic reproduction numbers and performed mesh and contour plots to explore the impact of different parameters on COVID-19 dynamics. Our model fitted and calibrated with number of cases of the COVID-19 data in Bangladesh as a case study to determine the optimal combinations of interventions for particular scenarios. We evaluated the cost-effectiveness of varying single and combinations of three intervention strategies, including transmission control, treatment, and vaccination, all within the optimal control framework of the single-intervention policies; enhanced transmission control is the most cost-effective and prompt in declining the COVID-19 cases in Bangladesh. Our finding recommends that a three-intervention strategy that integrates transmission control, treatment, and vaccination is the most cost-effective compared to single and double intervention techniques and potentially reduce the overall infections. Other policies can be implemented to control COVID-19 depending on the accessibility of funds and policymakers’ judgments.

The control of COVID-19 transmission remains a crucial issue despite the administration of multiple doses of vaccines, given the ongoing emergence of new variants. This study proposes a mathematical model of COVID-19 with multiple doses of vaccinations. We find disease-free equilibrium (DFE) and disease-endemic equilibrium (DEE) and perform their stability analysis to explore in which parameters space the disease will be fade-out or persists in the population. This study derives mathematical expressions for the basic reproductive numbers (R0) using the Next-Generation Matrix method. Using surface and contour plots, we brought to light the exciting consequences of disease transmission rate and other parameter values on the basic reproduction number (R0). It demonstrates that the disease-free equilibrium point is locally stable when R0 1. We also perform bifurcation analysis to demonstrate in which conditions the disease will be stable or unstable when R0 = 1. Numerical simulations show that increasing first dose vaccination rate is more effective than second and booster doses vaccination rate for reducing the number of asymptomatic and symptomatic cases in Bangladesh. We use COVID-19 incidence data from WHO between January and December 2022 and estimate parameters using the least-squares technique. We perform the sensitivity index of symptomatic and asymptomatic cases to identify the most significant parameter for disease outbreaks and found that transmission rate had the highest impact on disease prevalence. This research provides insight to prevent and control of COVID-19 transmission with booster-dose vaccination in Bangladesh.

COVID-19 is a significant public health problem around the globe, including in Australia. Despite this, Australia's Ministry of Health has expanded COVID-19 control measures widely, logistical trials exist, and the disease burden still needs more clarity. One of the best methods to comprehend the dynamics of disease transmission is by mathematical modeling of COVID-19, which also makes it possible to quantify factors in many places, including Australia. In order to understand the dynamics of COVID-19 in Australia, we examine a mathematical modeling framework for the virus in this study. Australian COVID-19 actual incidence data from January to December 2021 was used to calibrate the model. We also performed a sensitivity analysis of the model parameters and found that the COVID-19 transmission rate was the primary factor in determining the basic reproduction number (R0). Gradually influential intervention policies were established, with accurate effect and coverage regulated with the help of COVID-19 experts in Australia. We simulated data for the period from April 2022 to August 2023. To ascertain which of these outcomes is most effective in lowering the COVID-19 burden, we here assessed the COVID-19 burden (as shown by the number of incident cases and mortality) under a range of intervention scenarios. Regarding the policy of single intervention, the fastest and most efficient way to lower the incidence of COVID-19 is via increasing the first-dose immunization rate, while an improved treatment rate for the afflicted population is also helps to lower mortality in Australia. Furthermore, our results imply that integrating more therapies at the same time increases their efficacy, particularly for mortality, which significantly reduced with a moderate effort, while lowering the number of COVID-19 instances necessitates a major and ongoing commitment.

Antimicrobial resistance (AMR) is the microorganism's resistance to an antimicrobial drug developed and designed to handle an infection. A recent study showed that there were 1.27 million global deaths in 2019 attributable to AMR. In current decades, vaccination is the most effective treatment that can reduce the growing AMR by preventing the incidence of infectious diseases (directly or indirectly). Vaccination has become a popular treatment as this can reduce the number of circulating AMR strains and reduce the need for antimicrobial use. With a double-dose vaccination campaign, we analyzed drug-susceptible and drug-resistant pathogens for this survey. We considered the circumstances of positive invariances and boundedness with appropriate primary conditions. We have determined two basic reproduction numbers: one associated with drug-susceptible strain (R01) and the other with drug-resistant strain (R02). We also revealed that at any stage, one of the strain could spread in a population if max[R01, R02] > 1 and the disease fade-out if both are

The scope of the paper is to analyse the different similarities of air entrainment among the hydraulic jumps, plunging jets, and plunging breaking waves and to discuss current practices. The measured data are reexamined and scrutinised to investigate the gas exchange phenomena through an air-water interface. In particular, oxygen transfer efficiency and penetration depth by air bubbles are discussed. The calculated results highlight that the oxygen transfer efficiency is decreased with the increase of energy dissipation rate both in plunging jets and breaking waves. In contrast, it is shifted almost parallel in the case of hydraulic jumps. In addition, the aeration lengths in the hydraulic jumps and penetration depths both in plunging jets and plunging breaking waves were dependent on the jet impact velocity. HIGHLIGHTS Air bubble entrainments through hydraulic jumps, plunging jets, and plunging breaking waves are studied.; Aeration lengths in the hydraulic jump and penetration depths in the plunging jets and plunging breaking waves were determined.; Dissolved oxygen, as well as oxygen transfer efficiency, were measured and compared among the phenomena.; Scale effects were observed among the phenomena in terms of the oxygenation.;

The emergence of the new coronavirus variant from the coronaviridae family has become a global concern, and all nations, including Bangladesh, are battling to contain the spread of the disease. In this article, we discuss a COVID-19 vaccination model with the Caputo–Fabrizio (CF) fractional order derivative approach to reveal the complicated action in Bangladesh. We determine the existence and uniqueness properties of the outcomes acquired from our proposed model subjected to the Picard–Lindelöf theorem. We perform the stability analysis by using the fixed-point theorem. We utilize Laplace Transform to evaluate the approximate solution of the model. In the numerical simulation, we consider a new approach called the four-step Adams-Bashforth Predictor–Corrector iteration scheme, which simulates that the fractional order provides more precise results. Finally, several numerical results are displayed with the different numbers of the order of the system. We depict the influence of vaccination in the coronavirus model with different parameter values. By analyzing the transmission dynamics of the virus, we bring to light the importance of several doses of vaccination to prevent disease transmission and contagions. We also conducted the sensitivity analysis for the model parameters to evaluate the impact of those parameters on disease outbreaks. The numerical results demonstrate significant information in the Caputo–Fabrizio fractional derivative concept and provide important insights into predicting disease transmission and control policies.

COVID-19 is an infectious disease that kills millions of people each year and it is a major public health problem around the globe. The current COVID-19 situation is still now concerning, though the vaccination program is running. In this study, we considered a COVID-19 model with a double-dose vaccination strategy to control the current outbreak situation in Bangladesh. The fundamental qualitative analysis of this mathematical model has been performed. The conditions of positive invariance, boundedness with suitable initial conditions were analyzed. We have estimated the basic reproduction number (R0) for disease transmission and determined that our model contains two equilibrium points: the disease-free equilibrium and a disease-endemic equilibrium. We used the Routh-Hurwitz criteria to determine the stability of the equilibria. The disease will be eradicated from the community if R0