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Background: Childhood vaccinations can reduce disease burden and associated antibiotic use, in turn reducing the risk of antimicrobial resistance (AMR). We retrospectively estimated the population-level reductions in antibiotic use in India following the introduction of vaccines against Streptococcus pneumoniae and Haemophilius influenzae type B in the national immunization program for children in the mid-2010s and projected future gains to 2028 if vaccination coverage were to be increased., Methods: Using IndiaSim, a dynamic agent-based microsimulation model (ABM) for India, we simulated the spread of Streptococcus pneumoniae and Haemophilius influenzae type B (Hib) among children to estimate reductions in antibiotic use under the scenarios of: (i) pneumococcal and Hib vaccine coverage levels equivalent to the national coverage of pentavalent diphtheria-pertussis-tetanus third dose (DPT3) compared to a baseline of no vaccination, and (ii) near-universal (90%) coverage of the vaccines compared to pre-COVID national DPT3-level coverage. Model parameters, including national DPT3 coverage rates, were based on data from the National Family Household Survey 2015-2016 and other published sources. We quantified reductions in antibiotic consumption nationally and by state and wealth quintiles., Findings: We estimate that coverage of S. pneumoniae and Hib vaccines at the same level as DPT3 in India would translate to a 61.4% [95% UI: 43.8-69.5] reduction in attributable antibiotic use compared to a baseline of zero vaccination coverage. Increases in childhood vaccination coverage between 2004 and 2016 have likely reduced attributable antibiotic demand by as much as 93.4% among the poorest quintile. Increasing vaccination coverage by an additional 11 percentage points from 2016 levels results in mortality and antibiotic use across wealth quintiles becoming increasingly similar (p < 0.05), reducing in health inquities. We project that near-universal vaccine coverage would further reduce inequities in antibiotic demand and may eliminate of outbreak-associated antibiotic use from S. pneumoniae and Hib., Interpretation: Though vaccination has a complex relationship with antibiotic use because both are modulated by socioeconomic factors, increasing vaccinations for S. pneumoniae and Hib may have a significant impact on reducing antibiotic use and improving health outcomes among the poorest individuals., Funding: The Bill & Melinda Gates Foundation (grant numbers OPP1158136 and OPP1190803)., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Background: India has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI's)., Methods: We analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models., Findings: Up to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4-3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29-20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4-6.4] days, and case fatality rate was 2.5% [2.4-2.7] which increased with age., Conclusion: We found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding., Competing Interests: The authors have declared that no competing interests exist.

India is among the top three countries in the world both in COVID-19 case and death counts. With the pandemic far from over, timely, transparent, and accessible reporting of COVID-19 data continues to be critical for India's pandemic efforts. We systematically analyze the quality of reporting of COVID-19 data in over one hundred government platforms (web and mobile) from India. Our analyses reveal a lack of granular data in the reporting of COVID-19 surveillance, vaccination, and vacant bed availability. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Amid rising concerns of undercounting cases and deaths in India, our results highlight a patchy reporting of granular data even among the reported cases and deaths. Furthermore, total vaccination stratified by healthcare workers, frontline workers, and age brackets is reported by only 14 out of India's 36 subnationals (states and union territories). There is no reporting of adverse events following immunization by vaccine and event type. By showing what, where, and how much data is missing, we highlight the need for a more responsible and transparent reporting of granular COVID-19 data in India., Competing Interests: The authors declare that there are no competing interests., (Copyright: © 2022 Vasudevan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: We describe here a multicentric community-dwelling cohort of older adults ( > 60 years of age) established to estimate incidence, study risk factors, healthcare utilisation and economic burden associated with influenza and respiratory syncytial virus (RSV) in India., Participants: The four sites of this cohort are in northern (Ballabgarh), southern (Chennai), eastern (Kolkata) and western (Pune) parts of India. We enrolled 5336 participants across 4220 households and began surveillance in July 2018 for viral respiratory infections with additional participants enrolled annually. Trained field workers collected data about individual-level and household-level risk factors at enrolment and quarterly assessed frailty and grip strength. Trained nurses surveilled weekly to identify acute respiratory infections (ARI) and clinically assessed individuals to diagnose acute lower respiratory infection (ALRI) as per protocol. Nasal and oropharyngeal swabs are collected from all ALRI cases and one-fifth of the other ARI cases for laboratory testing. Cost data of the episode are collected using the WHO approach for estimating the economic burden of seasonal influenza. Handheld tablets with Open Data Kit platform were used for data collection., Findings to Date: The attrition of 352 participants due to migration and deaths was offset by enrolling 680 new entrants in the second year. All four sites reported negligible influenza vaccination uptake (0.1%-0.4%), low health insurance coverage (0.4%-22%) and high tobacco use (19%-52%). Ballabgarh had the highest proportion (54.4%) of households in the richest wealth quintile, but reported high solid fuel use (92%). Frailty levels were highest in Kolkata (11.3%) and lowest in Pune (6.8%). The Chennai cohort had highest self-reported morbidity (90.1%)., Future Plans: The findings of this cohort will be used to inform prioritisation of strategies for influenza and RSV control for older adults in India. We also plan to conduct epidemiological studies of SARS-CoV-2 using this platform., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

In India, the "low mortality" narrative based on the reported COVID-19 deaths may be causing more harm than benefit. The extent to which COVID-19 deaths get reported depends on the coverage of routine death surveillance [death registration along with medical certification of cause of death (MCCD)] and the errors in MCCD. In India, the coverage of routine death surveillance is 18.1%. This is compounded by the fact that COVID-19 death reporting is focused among reported cases and the case detection ratio is low. To adjust for the coverage of routine death surveillance and errors in MCCD, we calculated a correction (multiplication) factor at national and state level to produce an estimated number of COVID-19 deaths. As on July 31, 2020, we calculated the infection fatality ratio (IFR) for India (0.58:100-1.16:100) using these estimated COVID-19 deaths; this is comparable with the IFR range in countries with near perfect routine death surveillance. We recommend the release of excess deaths data during COVID-19 (at least in states with high death registration) and post-mortem COVID-19 testing as a surveillance activity for a better understanding of under-reporting. In its absence, we should adjust reported COVID-19 deaths for the coverage of routine death surveillance and errors in MCCD. This way we will have a clear idea of the true burden of deaths and our public health response will never be inadequate. We recommend that "reported" or "estimated" is added before the COVID-19 death data and related indicators for better clarity and interpretation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Shewade, Parameswaran, Mazumder and Gupta.)

India imposed one of the world's strictest population-wide lockdowns on March 25, 2020 for COVID-19. We estimated epidemiological parameters, evaluated the effect of control measures on the epidemic in India, and explored strategies to exit lockdown. We obtained patient-level data to estimate the delay from onset to confirmation and the asymptomatic proportion. We estimated the basic and time-varying reproduction number (R 0 and R t ) after adjusting for imported cases and delay to confirmation using incidence data from March 4 to April 25, 2020. Using a SEIR-QDPA model, we simulated lockdown relaxation scenarios and increased testing to evaluate lockdown exit strategies. R 0 for India was estimated to be 2·08, and the R t decreased from 1·67 on March 30 to 1·16 on April 22. We observed that the delay from the date of lockdown relaxation to the start of the second wave increases as lockdown is extended farther after the first wave peak-this delay is longer if lockdown is relaxed gradually. Aggressive measures such as lockdowns may be inherently enough to suppress an outbreak; however, other measures need to be scaled up as lockdowns are relaxed. Lower levels of social distancing when coupled with a testing ramp-up could achieve similar outbreak control as an aggressive social distancing regime where testing was not increased., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Case fatality rate (CFR) and doubling time are important characteristics of any epidemic. For coronavirus disease 2019 (COVID-19), wide variations in the CFR and doubling time have been noted among various countries. Early in the epidemic, CFR calculations involving all patients as denominator do not account for the hospitalised patients who are ill and will die in the future. Hence, we calculated cumulative CFR (cCFR) using only patients whose final clinical outcomes were known at a certain time point. We also estimated the daily average doubling time. Calculating CFR using this method leads to temporal stability in the fatality rates, the cCFR stabilises at different values for different countries. The possible reasons for this are an improved outcome rate by the end of the epidemic and a wider testing strategy. The United States, France, Turkey and China had high cCFR at the start due to low outcome rate. By 22 April, Germany, China and South Korea had a low cCFR. China and South Korea controlled the epidemic and achieved high doubling times. The doubling time in Russia did not cross 10 days during the study period.

Background : After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age,gender distribution and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of active COVID-19 patients after 40 days of lockdown. Methods : We used a cross-sectional descriptive design for first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results : Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion : The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India, but will be unable to prevent the spike in number of cases., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Mazumder A et al.)

Background : After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of  COVID-19 infections after 40 days of lockdown. Methods : We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results : Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion : The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Mazumder A et al.)