Background: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models. Methods: We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. Results:The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant. Conclusion:The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.

Background: Since the onset of the COVID-19 in China, forecasting and projections of the epidemic based on epidemiological models have been in the centre stage. Researchers have used various models to predict the maximum extent of the number of cases and the time of peak. This yielded varying numbers. This paper aims to estimate the effective reproduction number (R) for COVID-19 over time using incident number of cases that are reported by the government. Methods: Exponential Growth method to estimate basic reproduction rate R 0 , and Time dependent method to calculate the effective reproduction number (dynamic) were used. "R0" package in R software was used to estimate these statistics. Results: The basic reproduction number (R 0 ) for India was estimated at 1.

Background Global research is running towards to find a vaccine to stop the threat of the COVID-19. The Bacillus Calmette–Guérin (BCG) vaccine that prevents severe forms of tuberculosis is getting more attention in this scenario. The objective of our study was to determine the association between BCG vaccine coverage and incidence of COVID-19 at a national-level across the Globe. Methods The data of 160 countries were included in the study. Meta-regression was done to estimate the difference in the incidence of COVID-19 cases between countries with BCG vaccination coverage. BCG coverage was categorized as ≤70%, >70% and no vaccination. The analyses were carried out by adjusting for factors such as population density, income group, latitude, and percentage of the total population under age groups 15–64 and above 65 years of each country. Results The countries that had ≤70% coverage of BCG vaccine reported 6.5 (95% CI: −8.4 to −4.5) less COVID-19 infections per 10,000 population as compared to countries that reported no coverage. Those that had >70% coverage reported 10.1 (95% CI: −11.4 to −8.7) less infections per 10,000 population compared to those with no BCG countries. Conclusion Our analysis suggests that BCG is associated with reduced COVID-19 infections if the BCG vaccine coverage is over 70%. The region-wise analyses also suggested similar findings, except the Middle East and North African region.

ObjectiveLarge data on the clinical characteristics and outcome of COVID-19 in the Indian population are scarce. We analysed the factors associated with mortality in a cohort of moderately and severely ill patients with COVID-19 enrolled in a randomised trial on convalescent plasma.DesignSecondary analysis of data from a Phase II, Open Label, Randomized Controlled Trial to Assess the Safety and Efficacy of Convalescent Plasma to Limit COVID-19 Associated Complications in Moderate Disease.Setting39 public and private hospitals across India during the study period from 22 April to 14 July 2020.ParticipantsOf the 464 patients recruited, two were lost to follow-up, nine withdrew consent and two patients did not receive the intervention after randomisation. The cohort of 451 participants with known outcome at 28 days was analysed.Primary outcome measureFactors associated with all-cause mortality at 28 days after enrolment.ResultsThe mean (SD) age was 51±12.4 years; 76.7% were males. Admission Sequential Organ Failure Assessment score was 2.4±1.1. Non-invasive ventilation, invasive ventilation and vasopressor therapy were required in 98.9%, 8.4% and 4.0%, respectively. The 28-day mortality was 14.4%. Median time from symptom onset to hospital admission was similar in survivors (4 days; IQR 3–7) and non-survivors (4 days; IQR 3–6). Patients with two or more comorbidities had 2.25 (95% CI 1.18 to 4.29, p=0.014) times risk of death. When compared with survivors, admission interleukin-6 levels were higher (p<0.001) in non-survivors and increased further on day 3. On multivariable Fine and Gray model, severity of illness (subdistribution HR 1.22, 95% CI 1.11 to 1.35, p<0.001), PaO2/FiO2 ratio <100 (3.47, 1.64–7.37, p=0.001), neutrophil lymphocyte ratio >10 (9.97, 3.65–27.13, p<0.001), D-dimer >1.0 mg/L (2.50, 1.14–5.48, p=0.022), ferritin ≥500 ng/mL (2.67, 1.44–4.96, p=0.002) and lactate dehydrogenase ≥450 IU/L (2.96, 1.60–5.45, p=0.001) were significantly associated with death.ConclusionIn this cohort of moderately and severely ill patients with COVID-19, severity of illness, underlying comorbidities and elevated levels of inflammatory markers were significantly associated with death.Trial registration numberCTRI/2020/04/024775.

Introduction: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. Methodology: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. Results: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Conclusions: Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.

Background COVID-19 vaccines were authorised for emergency use to mitigate the impact of the pandemic. This study evaluated the effect of prior vaccination with either Oxford Astra Zeneca’s Covishield TM or Bharath Biotech’s Covaxin® on mortality among symptomatic COVID-19 patients during the second wave of the pandemic in India. Methodology In this cohort study comprising of RT-PCR confirmed symptomatic COVID-19 patients presenting during April and May 2021, the effect of prior vaccination on mortality (primary outcome), need for hospitalization, oxygen therapy, non-invasive ventilation (NIV) and intensive care unit (ICU) admission were assessed and expressed as risk ratio (RR) with 95% confidence intervals (CI). Results The mean (SD) age of the cohort (n=4183) was 46.3 (15.5) years; 17.9% (748/4183) had received at least one dose of Covishield TM and 4.8% (201/4183) had received Covaxin®. Mortality was 0.2% (95% CI: -0.2% - 0.7%), 3.5% (1.9% - 5.2%), 6.2% (0.3% - 12%) and 12.9% (11.8% - 14.1%) among fully vaccinated (>2 weeks after two doses), partially vaccinated (>2 weeks after one dose or <2 weeks after two doses), indeterminate (<2 weeks after one dose) and unvaccinated patients respectively. The difference in mortality among unvaccinated vs. fully vaccinated was 12.7% (95% CI: 11.4% - 13.9%), unvaccinated vs. partially vaccinated was 9.4% (7.4% - 11.4%) and unvaccinated vs. indeterminate vaccinated was 6.8% (0.8% - 12.7%). On adjusted analysis, as compared to unvaccinated patients, at least one dose of vaccine reduced the need for hospitalization (RR: 0.40; 95% CI: 0.35 - 0.47), oxygen (0.33; 0.27 - 0.40), NIV (0.23; 0.17 - 0.32), ICU admission (0.18; 0.12 - 0.27) and mortality (0.18; 0.11 - 0.29). Conclusion Among symptomatic COVID-19 patients, prior vaccination with Covishield TM or Covaxin® impacted the severity of illness and reduced mortality during a period of widespread delta variant circulation. Full vaccination conferred greater protection than partial vaccination.

There has been a reduction in the reported cases of acute myocardial infarction (MI) across the globe during the outbreak of coronavirus disease 2019 (COVID-19) (severe acute respiratory distress syndrome coronavirus 2). An attempt was made to find out the number of acute MI cases treated during the COVID-19 lockdown period (April 2020) and highlight the possible reasons for the changes in the occurrence. A multicentric retrospective observational study was performed to collect the selected data from 12 private hospitals distributed in 4 citiesdMadurai, Trichy (Thiruchirapalli), Erode, and Salemdof the Tamil Nadu state in southern India. There was a significant (P<.001) reduction in ST-segment elevation MI (STEMI), non-STEMI (NSTEMI), and total (STEMI and NSTEMI together) cases during the lockdown period (April 1 to 30, 2020) as compared with no-lockdown periods such as January and February 2020 and April 2019 and April 2018 in all cities, whereas the reduction was not significant for NSTEMI in Trichy when data for the lockdown period was compared with those for January and February 2020. Overall, there is a reduction in acute MI cases, which may be due to alterations in modifiable risk factors during the COVID-19 lockdown period. Hence, implementation of public education and polices on controlling modifiable risk factors is likely to pay dividends.

Background and Aims: Non-invasive blood pressure (NiBP) varies with the arm and body position. In the lateral decubitus position (LDP), the non-dependent arm reads lower, and the dependent arm reads higher pressure. We aimed to study the correlation between the NiBP and invasive arterial blood pressure (ABP) as anaesthesia progressed and its correlation in different BP ranges. Methods: American Society of Anesthesiologists (ASA I–III) patients, between 18–70 years undergoing neurosurgical procedures in the LDP were studied. All were anaesthetised using a standard protocol, positioned in the LDP. NiBP was measured every 15 min in both dependent and non-dependent arms and correlated with the ABP. Results: Intra-class correlation (ICC) done between the dependent arm NiBP and ABP showed good correlation for mean and systolic BP and moderate correlation for diastolic BP. ICC was 0.800, 0.846 and 0.818 for mean and 0.771, 0.782, 0.792 for systolic BP at 15 min, 1 h, and 2 h, respectively. The ICC between the non-dependent arm NiBP and the invasive ABP showed poor correlation for all BP (systolic, diastolic and mean). As anaesthesia progressed, the mean difference between the NiBP and the ABP decreased in the dependent arm and increased in the non-dependent arm. The strength of agreement between the NiBP and the ABP in various BP ranges showed moderate correlation for the dependent arm NiBP (0.45–0.54) and poor correlation (0.21–0.38) for the non-dependent arm. Conclusion: The NiBP of the dependent arm correlated well with ABP in LDP under general anaesthesia (GA). It is better to defer measuring NiBP in the non-dependent arm as the correlation with ABP is poor.