Post-COVID-19 tele-survey for persistent symptoms in a single center hospital cohort in India along with a parallel country-wide web-survey (preprint)

Introduction A major concern amidst the ongoing coronavirus pandemic has been the longer term persistence of morbidities in individuals recovering from COVID-19 disease, called long COVID. We aimed at documenting the prevalence and key associations of post-COVID symptoms (PCS) in India in telephonic survey among recovered patients in a single hospital in eastern India as well as a parallel web-survey covering a wider population of the country. Methods Self-reported PCS, ranging up to one year since the original COVID-19 diagnosis, were documented in a telephonic survey of subjects (analyzed N=986), treated for acute COVID-19 in Infectious Diseases and Beleghata General Hospital, Kolkata, between April 1, 2020 and April 13, 2021. In parallel, we ran a web-based survey (analyzed N=580), to evaluate concordance. Results Shortness of breath, fatigue and insomnia were identified to be the most commonly reported PCS in both the surveys, with higher prevalence in females. In the telephonic survey, a 3.65% post-discharge mortality was registered within a median of 39 days since COVID diagnosis. Intensive care during acute disease and hypertension were more often associated with PCS, while fatigue was more often reported by the 20-40 years age-group. The web-survey revealed a gradual decline in PCS with time since COVID-19 diagnosis and type 2 diabetes to be associated with higher prevalence of these symptoms. Conclusions We assessed the predominant PCS among Indian COVID-19 patients and identified key demographic and clinical associations in our surveys, which warrants deeper epidemiological and mechanistic studies for guiding management of long-COVID in the country.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2 (preprint)

BackgroundIndia has been amongst the most affected nations during the SARS-CoV2 pandemic, with sparse data on country-wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS-CoV2 sero-positivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. MethodsCouncil of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi-urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecsys SARS CoV2 specific antibody kit and GENScript cPass SARS-CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings1058 individuals (10{middle dot}14%) had antibodies against SARS-CoV2. A follow-up on 346 sero-positive individuals after three months revealed stable to higher antibody levels against SARS-CoV2 but declining plasma activity for neutralizing SARS-CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of sero-positive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one-fourth reporting loss of taste or smell. Significantly associated risks for sero-positivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1{middle dot}79, 1{middle dot}43 - 2{middle dot}24, 2{middle dot}81561E-06), occupational responsibilities such as security, housekeeping personnel etc. (2{middle dot}23, 1{middle dot}92 - 2{middle dot}59, 6{middle dot}43969E-26), non-smokers (1{middle dot}52, 1{middle dot}16 - 1{middle dot}99, 0{middle dot}02) and non-vegetarianism (1{middle dot}67, 1{middle dot}41 - 1{middle dot}99, 3{middle dot}03821E-08). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for sero-positivity was noted with specific ABO blood groups -O was associated with a lower risk. InterpretationIn a first-of-its-kind study from India, we report the sero-positivity in a country-wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much-sought-for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. FundingCouncil of Scientific and Industrial Research, India (CSIR)

Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model (preprint)

Objective: In absence of any vaccine, the Corona Virus Disease 2019 (COVID-19) pandemic is being contained through a non-pharmaceutical measure termed Social Distancing (SD). However, whether SD alone is enough to flatten the epidemic curve is debatable. Using a Stochastic Computational Simulation Model, we investigated the impact of increasing SD, hospital beds and COVID-19 detection rates in preventing COVID-19 cases and fatalities. Research Design and Methods: The Stochastic Simulation Model was built using the EpiModel package in R. As a proof of concept study, we ran the simulation on Kasaragod, the most affected district in Kerala. We added 3 compartments to the SEIR model to obtain a SEIQHRF (Susceptible-Exposed-Infectious-Quarantined-Hospitalised-Recovered-Fatal) model. Results: Implementing SD only delayed the appearance of peak prevalence of COVID-19 cases. Doubling of hospital beds could not reduce the fatal cases probably due to its overwhelming number compared to the hospital beds. Increasing detection rates could significantly flatten the curve and reduce the peak prevalence of cases (increasing detection rate by 5 times could reduce case number to half). Conclusions: An effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with SD measures and increase in hospital beds.

A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19 (preprint)

Objective: The recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach. Research Design and Methods: Clinical data of 1085 cases of COVID-19 from 13th January to 28th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were selected for the final analysis. Random Forest classification algorithm was implemented on the dataset to identify the important predictors and their effects on mortality. Results: The Area under the ROC curve obtained during model validation on the test dataset was 0.97. Age was the most important variable in predicting mortality followed by the time gap between symptom onset and hospitalization. Conclusions: Patients aged beyond 62 years are at higher risk of fatality whereas hospitalization within 2 days of the onset of symptoms could reduce mortality in COVID-19 patients.