Assessment of COVID-19 data reporting in 100+ websites and apps in India

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.

Assessment of COVID-19 data reporting in 100+ websites and apps in India.

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.

Cohort profile: Indian Network of Population-Based Surveillance Platforms for Influenza and Other Respiratory Viruses among the Elderly (INSPIRE).

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.

Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India.

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.

Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies.

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 (R0 and Rt) 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. R0 for India was estimated to be 2·08, and the Rt 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.