The co-infection of Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) remains a significant public health challenge both in Thailand and globally. This dual burden exacerbates disease severity, complicates treatment strategies, and increases mortality rates. In this study, we developed a mathematical model of TB-HIV co-infection to examine the dynamics of TB prevalence, HIV prevalence, co-infection prevalence and combined (aggregated) disease burden across Thailand. The model was calibrated using real-world incidence data of TB-HIV co-infection in the country. The global sensitivity analysis revealed that the transmission rates of TB and HIV had the most significant influence on the basic reproduction numbers, R_0T ​ and R_0H ​, highlighting critical targets for intervention. The model was further used to evaluate the potential impact of various intervention strategies on reducing the prevalence of TB, HIV, co-infection, and combined disease burden over the period 2022–2039. We found that increasing treatment coverage for TB patients reduces TB prevalence, while HIV prevalence and TB–HIV co-infection prevalence increase over time due to the prolonging the survival of HIV-positive individuals and improving case detection. Additionally, treating individuals co-infected with TB and HIV significantly lowers the prevalence of co-infections. Our findings further suggest that reducing the progression rates of both TB and HIV leads to more substantial and sustained reductions in TB prevalence, HIV prevalence, and TB-HIV co-infection prevalence in Thailand. Our findings also indicate that higher HIV acquisition among TB patients amplifies the burden of co-infection, whereas increased TB acquisition among HIV patients accelerates its progression. Therefore, targeting the dual burden of TB and HIV is essential for lower the prevalence of both diseases, protect vulnerable populations, and ultimately work towards eliminating the diseases as a public health threat.

This paper presents a mathematical model (COPSTZ) that simulates the interactions within a marine ecosystem, focusing on six key compartments: dissolved oxygen, organic pollutants, phytoplankton, salinity, temperature, and zooplankton. The model specifically explores how reductions in dissolved oxygen impact the survival of interacting planktonic populations. By solving the model equations both analytically and numerically, the equilibrium states are identified and their stability is analyzed. The analysis of the model results reveals that decreases in dissolved oxygen concentration occur because of the combined effects of salinity, organic pollutants, and plankton. Additionally, the model’s predictions for dissolved oxygen, plankton, zooplankton, and organic pollutants align with findings from other studies. The study concludes that dissolved oxygen levels in marine ecosystems can be improved and the negative impacts of salinity reduced through the strategic use of coastal vegetation and the optimization of plankton nutrient availability.

The unprecedented global impact of the 2019 coronavirus disease (COVID-19) has necessitated a comprehensive understanding of its transmission dynamics and control measures. In this study, we present a detailed analysis of a COVID-19 vaccination model tailored to the context of Bangladesh, incorporating dual-dose vaccination strategies. By employing qualitative and bifurcation analysis techniques, we investigate the equilibrium points, effective reproduction number (R0), and critical thresholds that influence the prevalence and control of COVID-19 in the region. Our findings reveal insights into the effectiveness of vaccination programs and provide a framework for developing targeted control plans. Through a rigorous examination of model parameters and sensitivity analysis, we identify key factors driving COVID-19 transmission dynamics, emphasizing the significance of vaccination rates and other critical parameters. The validation of our model against real-world data underscores its utility in informing evidence-based decision-making for managing the COVID-19 pandemic in Bangladesh and beyond.

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, whereas the disease-endemic equilibrium point is locally stable for 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.

Hepatitis C virus (HCV) is a leading cause of morbidity and mortality worldwide, including in Bangladesh. Although the Ministry of Health in Bangladesh is implementing its nationwide HCV control policies, more specific and cost-effective interventions are needed to control HCV. In this study, we developed a two-strain HCV vaccination model to explore the dynamics of HCV infection transmission in Bangladesh. We calibrated the model with the number of HCV cases in Bangladesh to estimate some setting-specific parameters. We also derived the basic reproduction numbers (R01 and R02) and performed contour plots to find the most significant parameters and found that the transmission rate had the largest impact on HCV dynamics. We evaluated the cost-effectiveness of three basic control strategies: transmission control, vaccination, and treatment, all within the optimal control context. Our findings suggested that a triple control strategy combining transmission control, vaccination, and treatment is the most cost-effective way to reduce the burden of HCV. Our finding also indicated that the transmission control strategy is the most cost-effective for the single intervention strategies compared to vaccination and treatment. Alternative programmes can be adopted to curb HCV, depending on the availability of resources and policymakers' decisions.

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.

In recent years, antibiotic resistance to the most effective treatments has emerged and spread. This has led to a decline in the efficacy of antibiotics used to treat patients, with drug-resistant strain experiencing much higher failure rates and serious side effects. In this study, we considered two-strains (e.g., drug-susceptible and drug-resistant) susceptible-infected-recovery disease model with amplification, nonlinear incidence and treatment. We assumed amplification develops mainly through the choice of naturally happening mutations in the presence of inappropriate treatment. We performed a rigorous analytical analysis of the model properties and solutions to predict late-time behavior of the disease dynamics and find that the model contains four equilibrium points: disease-free equilibrium, monoexistence endemic equilibrium 1 concerning drug-susceptible strain, monoexistence endemic equilibrium 2 concerning drug-resistant strain and coexistence equilibrium regarding to drug-susceptible as well as drug-resistant strains. Two basic reproduction numbers $${R}_{0\mathrm{s}}$$ and $$R_{{0{\text{m}}}}$$ are found, and we have presented that if both are less than one $$({\text{i}}.{\text{e}}.\max \left[ {R_{{0{\text{s}}}} ,{ }R_{{0{\text{m}}}} } \right] < 1)$$ , the disease fade-out, and if both greater than one $$({\text{i}}.{\text{e}}.\max \left[ {R_{{0{\text{s}}}} ,{ }R_{{0{\text{m}}}} } \right] > 1)$$ the epidemic situation occurs. Moreover, epidemics occur regarding to any strain when the basic reproduction number remains above the value 1 and disease fade-out with regard to any strain when the basic reproduction number remains below the value 1. In all equilibrium points, the global stability analysis was determined with the help of appropriate Lyapunov functions. In addition, we also found that the drug-resistant strain prevalence increases when the drug-susceptible strain is treated due to the poor-quality treatment (i.e., amplification). We also performed the sensitivity analysis through evaluation of Partial Rank Correlation Coefficients (PRCC) to identify the most important model parameters and found that transmission rate of both strains had the maximum influence on disease outbreak. To support those analytical results, numerical simulations of the model were performed using ODE45 MATLAB routine.

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.

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 <1 (i.e., max[R01, R02] < 1). In this study, We evaluated that our model contains four equilibrium points: the disease-free equilibrium, mono-existent endemic equilibrium 1, mono-existent endemic equilibrium 2, and a disease-endemic equilibrium. In particular, we applied the Routh-Hurwitz criteria to generalize the stability of the equilibria. We also found that the drug-resistant strain prevalence increases when the drug-susceptible strain is vaccinated on time due to the poor quality of the vaccine. We carried out the sensitivity analysis through the partial rank correlation coefficient (PRCC) technique to identify most important parameters. We found that the transmission rate of both strains had the biggest stimulus on disease outbreak. We also demonstrate how vaccination rates affected the equilibrium level of prevalence and found that a proper vaccination program is more effective for reducing two-strain disease burden. Numerical simulations carried out using MATLAB routines to support the analytical conclusions.

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.

The aim of this study is to analyze the trend of spreading out coronavirus (COVID-19) for the second wave attack in Bangladesh during this winter season. In this study, we included all reported new confirmed and death cases from March 30 to December 14, 2020. Our intension is to explore the trend of dispersion in infected and death cases after January, 2021. In this paper, we have conducted statistical analysis of Covid-19 in Bangladesh to deliver with reliable and perfect forecasts of the outbreak. We used MS excel and SPSS software for regression analysis and found the slope of the trendline of the data and R-squared value for both infected and death cases. The new confirmed cases gradually increase with date (from March, 30 to June) and reach towards the peak for first wave attack. We get the slope of the trendline for infected case which is increasing first as 2    47.562, 0.8799 R and decreasing later as 2     20.709, 0.6816 R (from July to September) for first wave. After then again, the slope is increasing. Death cases are also following the same trend pattern. This analysis may help to take significant role for securing public health for Bangladeshi people. In this situation, Bangladesh needs to take emergency management system to protect the attack of the virus which we going to face for second wave. As well as people should concern about the guidelines provided by IEDCR and WHO up to get vaccine.

This paper is concerned with the deposition of sediment due to dune forests (Nakatajima dune, Japan) and CC block system on a riverbank (Padma, Bangladesh). Measurements of sediment deposition were carried out at Nakatajima dune, Japan and at Padma riverbank, Rajshahi, Bangladesh using Global Positioning System (GPS). 2442 m3 sediment migrates each year to the East side of Nakatajima dune by wind and the edge of the dune develops about 6-7 m/year due to the existence of forests. Sedimentation rate is also greater in shallower mangrove areas. The trapped sediment height varies from 8.0 to 10.0 cm in the mangrove-lined bank. Mangrove forests help to trap 25% more sedimentation at low tide than at high tide. On the other hand, only for setting up the CC block and groynes along the bank, the sedimentation rate is observed almost twice than that of the previous year. An analysis shows that there is a similarity of sedimentation among dune, beach forests, mangrove forests and CC blocks. Journal of Bangladesh Academy of Sciences, Vol. 43, No. 1, 67-78, 2019