Background: Long COVID or long-term complication after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe cases. We did this study to estimate the prevalence and identify the characteristics and predictors of Long COVID among our patients. Methodology: We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristic of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID. Results: We have analyzed 487 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47). Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) participants. Prevalence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n=72). The most common Long COVID symptom was fatigue (64.8%) followed by cough (32.4%). Statistically significant predictors of Long COVID were - Pre-existing medical conditions (Adjusted Odds ratio (aOR)=2.00, 95% CI: 1.16,3.44), having a more significant number of symptoms during acute phase of COVID-19 disease (aOR=11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR=2.32, 95% CI: 1.17,4.58), the severity of illness (aOR=5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR)=3.89, 95% CI: 2.49,6.08). Conclusion: A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

Long coronavirus disease (COVID) or postacute sequelae of coronavirus disease of 2019 (COVID‐19) is widely reported but the data of long COVID after infection with the Omicron variant is limited. This study was conducted to estimate the incidence, characteristics of symptoms, and predictors of long COVID among COVID‐19 patients diagnosed during the Omicron wave in Eastern India. The cohort of COVID‐19 patients included were adults (≥18 years) diagnosed as severe acute respiratory syndrome coronavirus 2 positive with Reverse Transcription Polymerase Chain Reaction. After 28 days of diagnosis; participants were followed up with a telephonic interview to capture data on sociodemographic, clinical history, anthropometry, substance use, COVID‐19 vaccination status, acute COVID‐19 symptoms, and long COVID symptoms. The long COVID symptoms were self‐reported by the participants. Logistic regression was used to determine the predictors of long COVID. The median follow‐up of participants was 73 days (Interquartile range; 67–83). The final analysis had 524 participants' data; among them 8.2% (95% Confidence Interval [CI]: 6%–10.9%) self‐reported long COVID symptoms. Fatigue (34.9%) was the most common reported symptom followed by cough (27.9%). In multivariable logistic regression only two predictors were statistically significant—number of acute COVID‐19 symptoms ≥ five (Adjusted odds ratio (aOR) = 2.95, 95% CI: 1.30–6.71) and past history of COVID‐19 (aOR = 2.66, 95% CI: 1.14–6.22). The proportion of self‐reported long COVID is considerably low among COVID‐19 patients diagnosed during the Omicron wave in Eastern India when compared with estimates during Delta wave in the same setting.

Background Long COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients. Methodology We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID. Results We have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were—Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08). Conclusion A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

This paper presents the current situation and how to minimize its effect in India through a mathematical model of infectious Coronavirus disease (COVID-19). This model consists of six compartments to population classes consisting of susceptible, exposed, home quarantined, government quarantined, infected individuals in treatment, and recovered class. The basic reproduction number is calculated, and the stabilities of the proposed model at the disease-free equilibrium and endemic equilibrium are observed. The next crucial treatment control of the Covid-19 epidemic model is presented in India's situation. An objective function is considered by incorporating the optimal infected individuals and the cost of necessary treatment. Finally, optimal control is achieved that minimizes our anticipated objective function. Numerical observations are presented utilizing MATLAB software to demonstrate the consistency of present-day representation from a realistic standpoint.

A BSTRACT Intimate partner violence (IPV) is considered any type of behavior involving the premeditated use of physical, emotional, or sexual force between two people in an intimate relationship. The prevalence of health-seeking attitude towards IPV in India is very low among victims affected by it. The chances of facing violence or even in their maternal life were substantially high among women having lesser education or without any financial empowerment. Data have been quite supportive whenever elevated odds of risk of experiencing controlling behavior from their spouses were concerned. Safety strategies for violence programming could increase monitoring and evaluation efforts to reduce violence. Women with vulnerabilities like being marginalized, least resourced, and disabled are likely to suffer violence in an intimate relationship. Primary care physicians have a definitive role and involvement of other stakeholders like ward members and self-help groups to mitigate such occurrences.