COVID -19 pandemic has resulted in more than 257 million infections and 5.15 million deaths worldwide. Several drug interventions targeting multiple stages of the pathogenesis of COVID -19 can significantly reduce induced infection and thus mortality. In this study, we first develop SIV model at within-host level by incorporating the intercellular time delay and analyzing the stability of equilibrium points. The model dynamics admits a disease-free equilibrium and an infected equilibrium with their stability based on the value of the basic reproduction number R
0. We then formulate an optimal control problem with antiviral drugs and second-line drugs as control measures and study their roles in reducing the number of infected cells and viral load. The comparative study conducted in the optimal control problem suggests that if the first-line antiviral drugs show adverse effects, considering these drugs in reduced amounts along with the second-line drugs would be very effective in reducing the number of infected cells and viral load in a COVID-19 infected patient. Later, we formulate a time-optimal control problem with the goal of driving the system from any initial state to the desired infection-free equilibrium state in finite minimal time. Using Pontryagin’s Minimum Principle, it is shown that the optimal control strategy is of the bang-bang type, with the possibility of switching between two extreme values of the optimal controls. Numerically, it is shown that the desired infection-free state is achieved in a shorter time when the higher values of the optimal controls. The results of this study may be very helpful to researchers, epidemiologists, clinicians and physicians working in this field.
Background Leprosy is one of the neglected tropical diseases (NTD) experiencing relative stagnancy in the global number of new cases. Objective This study aims to find the proportion of antimicrobial resistance in the clinical specimens of leprosy patients attending a tertiary care leprosy Institute in South India. Materials and methods Skin biopsy specimens (31 Nos) were tested by Polymerase chain reaction (PCR) for identification of M. leprae, and PCR followed by DNA sequencing for antimicrobial resistance (AMR) detection. Results All 31 specimens tested for M. leprae identification were positive. Resistance to dapsone was 6.5%, while rifampicin and ofloxacin showed 100% sensitivity. Conclusion This study warrants the need for a vigilant surveillance mechanism to monitor the trend of antimicrobial resistance in leprosy.
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