Assessing The Impact of Medical Treatment and Fumigation on The Superinfection of Malaria: A Study of Sensitivity Analysis

Malaria is a disease caused by the parasite Plasmodium, transmitted by the bite of an infected female Anopheles. In general, five species of Plasmodium that can cause malaria. Of the five species, Plasmodium falciparum and Plasmodium vivax are two species of Plasmodium that can allow malaria superinfection in the human body. Typically, the popular intervention for malaria eradication is the use of fumigation to control the vector population and provide good medical services for malaria patients. Here in this article, we formulate a mathematical model based on a host-vector interaction. Our model considering two types of plasmodium in the infection process and the use of medical treatment and fumigation for the eradication program. Our analytical result succeeds in proving the existence of all equilibrium points and how their existence and local stability criteria depend not only on the control reproduction number but also in the invasive reproduction number. This invasive reproduction number represent how one plasmodium can dominate other plasmodium. Our sensitivity analysis shows that fumigation is the most influential parameter in determining all control reproduction numbers. Furthermore, we find that the order in which numerous intervention measures are taken will be very crucial to determine the level of success of our malaria eradication program

ANTIOXIDANTS, BIOCHEMICAL, AND HEMATOLOGICAL PARAMETERS CHANGE IN WORKERS OCCUPATIONALLY EXPOSED TO RADON INHALATION AT CERTAIN CONSTRUCTION MATERIAL INDUSTRIES IN ERBIL, IRAQ

This study examined the effects of radon on the endogenous antioxidants, biochemical, and hematological parameters of workers in Erbil, Iraqi Kurdistan. This was carried out to ascertain how radon affects the health of those who work in certain factories producing building materials. The case study group consisted of 70 workers, who were then divided into seven subgroups (gypsum, cement plant, lightweight block, marble, red brick 1, crushed stone, and concrete block 2), while the control group consisted of 20 healthy volunteers. The total antioxidant capacity (TAC), levels of carcinoembryonic antigen (CEA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX), the complete blood count (CBC), and liver function tests were evaluated. The statistical analysis revealed that the antioxidant activities and CEA levels between the case study group and the control group differed significantly. Also, antioxidant enzyme activities and indoor radon concentration, the annual effective dosage, were found to be highly significantly correlated by Pearson and Spearman analyses in the case study group. Additionally, the results demonstrated a substantial correlation in the data between the levels of CEA biomarkers and radon (r=0.478, p˂0.000). The present results showed that radon concentration increased alanine aminotransferase (ALT) activity in a radon concentration-dependent manner (r=0.263 and p ˂0.05). The aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin activities, on the other hand, were not significantly affected by radon. The most significantly influenced CBC parameter was the low white blood cells (WBC) in the case study group compared to the controls. Low platelet count (PLT) was the second-highest problematic metric. The other CBC values, however, did not significantly differ between the research group and the control group. This study offers a preliminary image of the endogenous antioxidant systems in employees, especially to show a connection between radon and the occurrence of cancer among workers in Iraq Kurdistan Region

Investigating the Impact of Social Awareness and Rapid Test on A COVID-19 Transmission Model

In this article, we propose and analyze a mathematical model of COVID-19 transmission among a closed population, with social awareness and rapid test intervention as the control variables. For this, we have constructed the model using a compartmental system of the ordinary differential equations. Dynamical analysis regarding the existence and local stability of equilibrium points is conducted rigorously. Our analysis shows that COVID-19 will disappear from the population if the basic reproduction number is less than one, and persist if the basic reproduction number is greater than one. In addition, we have shown a trans-critical bifurcation phenomenon based on our proposed model when the basic reproduction number equals one. From the elasticity analysis, we have observed that rapid testing is more promising in reducing the basic reproduction number as compared to a media campaign to improve social awareness on COVID-19. Using the Pontryagin Maximum Principle (PMP), the characterization of our optimal control problem is derived analytically and solved numerically using the forward-backward iterative algorithm. Our cost-effectiveness analysis shows that using rapid test and media campaigns partially are the best intervention strategy to reduce the number of infected humans with the minimum cost of intervention. If the intervention is to be implemented as a single intervention, then using solely the rapid test is a more promising and low-cost option in reducing the number of infected individuals vis-a-vis a media campaign to increase social awareness as a single intervention

Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis

Nowadays, there are a variety of descriptive studies of available clinical data for coronavirus disease (COVID-19). Mathematical modelling and computational simulations are effective tools that help global efforts to estimate key transmission parameters. The model equations often require computational tools and dynamical analysis that play an important role in controlling the disease. This work reviews some models for coronavirus first, that can address important questions about the global health care and suggest important notes. Then, we model the disease as a system of differential equations. We develop previous models for the coronavirus, some key computational simulations and sensitivity analysis are added. Accordingly, the local sensitivities for each model state with respect to the model parameters are computed using three different techniques: non-normalizations, half normalizations and full normalizations. Results based on sensitivity analysis show that almost all model parameters may have role on spreading this virus among susceptible, exposed and quarantined susceptible people. More specifically, communicate rate person–to–person, quarantined exposed rate and transition rate of exposed individuals have an effective role in spreading this disease. One possible solution suggests that healthcare programs should pay more attention to intervention strategies, and people need to self-quarantine that can effectively reduce the disease

How Containment Can Effectively Suppress the Outbreak of COVID-19: A Mathematical Modeling

In this paper, the aim is to capture the global pandemic of COVID-19 with parameters that consider the interactions among individuals by proposing a mathematical model. The introduction of a parsimonious model captures both the isolation of symptomatic infected individuals and population lockdown practices in response to containment policies. Local stability and basic reproduction numbers are analyzed. Local sensitivity indices of the parameters of the proposed model are calculated, using the non-normalization, half-normalization, and full-normalization techniques. Numerical investigations show that the dynamics of the system depend on the model parameters. The infection transmission rate (as a function of the lockdown parameter) for both reported and unreported symptomatic infected peoples is a significant parameter in spreading the infection. A nationwide public lockdown decreases the number of infected cases and stops the pandemic’s peak from occurring. The results obtained from this study are beneficial worldwide for developing different COVID-19 management programs

An Optimal Control Model to Understand the Potential Impact of the New Vaccine and Transmission-Blocking Drugs for Malaria: A Case Study in Papua and West Papua, Indonesia

Malaria is one of the major causes of a high death rate due to infectious diseases every year. Despite attempts to eradicate the disease, results have not been very successful. New vaccines and other treatments are being constantly developed to seek optimal ways to prevent malaria outbreaks. In this article, we formulate and analyze an optimal control model of malaria incorporating the new pre-erythrocytic vaccine and transmission-blocking treatment. Sufficient conditions to guarantee local stability of the malaria-free equilibrium were derived based on the controlled reproduction number condition. Using the non-linear least square fitting method, we fitted the incidence data from the province of Papua and West Papua in Indonesia to estimate the model parameter values. The optimal control characterization and optimality conditions were derived by applying the Pontryagin Maximum Principle, and numerical simulations were also presented. Simulation results show that both the pre-erythrocytic vaccine and transmission-blocking treatment significantly reduce the spread of malaria. Accordingly, a high doses of pre-erythrocytic vaccine is needed if the number of infected individuals is relatively small, while transmission blocking is required if the number of infected individuals is relatively large. These results suggest that a large-scale implementation of both strategies is vital as the world continues with the effort to eradicate malaria, especially in endemic regions across the globe