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1.
Comput Math Methods Med ; 2022: 3816492, 2022.
Article in English | MEDLINE | ID: mdl-35720041

ABSTRACT

For the analysis of the recent deadly pandemic Sars-Cov-2, we constructed the mathematical model containing the whole population, partitioned into five different compartments, represented by SEIQR model. This current model especially contains the quarantined class and the factor of loss of immunity. Further, we discussed the stability of the SEIQR model (constructed on the basis of system of coupled differential equations). The basic reproduction that indicates the behavior of the disease is also estimated by the use of next-generation matrix method. Numerical simulation of this model is provided, the results are analyzed by theoretically strong numerical methods, and computationally known tool MATLAB Simulink is also used for visualization of the results. Validation of results by Simulink software and numerical methods shows that our model and adopted methodology are appropriate and accurate and could be used for further predictions on COVID-19. Our results suggest that the isolation of the active cases and strong immunization of patients or individuals play a major role to fight against the deadly Sars-Cov-2.


Subject(s)
COVID-19 , COVID-19/epidemiology , Disease Outbreaks , Humans , Models, Theoretical , Quarantine , SARS-CoV-2
2.
J Am Coll Emerg Physicians Open ; 1(5): 782-789, 2020 Oct.
Article in English | MEDLINE | ID: mdl-33145519

ABSTRACT

OBJECTIVE: Using a physician-directed, patient "opt-out" approach to prescriptive smoking cessation in the emergency department (ED) setting, we set out to describe patient actions as they related to smoking cessation behaviors. METHODS: A convenience sample of smokers at 2 Pennsylvania hospital EDs who met inclusion/exclusion criteria were approached to participate in a brief intervention known as screening, treatment initiation, and referral (STIR) counseling that included phone follow-up. Demographic information, current smoking status, and specific physician prescription and follow-up recommendations were collected. Approximately 3 months later, patients were contacted to determine current smoking status and actions taken since their ED visit. RESULTS: One hundred six patients were approached and 7 (6.6%) opted out of the intervention. Patients who did not opt out were evaluated for appropriate use of smoking cessation-related medications; 35 (35.4%) opted out of the prescription(s) and 6 (6.1%) were not indicated. Twenty-one (21.2%) patients opted out of ambulatory referral follow-ups with primary care and/or tobacco treatment program; one (1.0%) was not indicated for referral. Nineteen (32.8%) patients who received prescription(s) for smoking cessation-related medications initially also followed the prescription(s). Seventeen (22.1%) patients participated in referral follow-up. CONCLUSION: In this small ED pilot, using the STIR concepts in an opt-out method, few smokers opted out of the smoking cessation intervention. About one-third of the patients declined prescriptions for smoking cessation-related medications and less than one-quarter declined ambulatory referrals for follow-up. These findings support a willingness of patients to participate in STIR and the benefits of intervention in this setting.

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