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.

Role of Multi-resolution Vulnerability Indices in COVID-19 spread: A Case Study in India (preprint)

Introduction: The outbreak of COVID-19 has differentially affected countries in the world, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of the COVID-19 spread. Vulnerability of a geographical region/country to COVID-19 has been a topic of interest, particularly in low- and middle-income countries like India to assess the multi-factorial impact of COVID-19 on the incidence, prevalence or mortality data. Datasets and Methods Based on publicly reported socio-economic, demographic, health-based and epidemiological data from national surveys in India, we compute contextual, COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These multi-resolution cVIs were used in regression models to assess their impact on indicators of the spread of COVID-19 such as the average time-varying instantaneous reproduction number. Results Our observational study was focused on 30 districts of the eastern Indian state of Odisha. It is an agrarian state, prone to natural disasters and one of the largest contributors of an unprotected migrant workforce. Our analyses identified housing and hygiene conditions, availability of health care and COVID preparedness as important spatial indicators. Conclusion Odisha has demonstrated success in containing the COVID-19 infection to a reasonable level with proactive measures to contain the spread of the virus during the first wave. However, with the onset of the second wave of COVID, the virus has been making inroads into the hinterlands and peripheral districts of the state, burdening the already deficient public health system in these areas. The vulnerability index presented in this paper identified vulnerable districts in Odisha. While some of them may not have a large number of COVID-19 cases at a given point of time, they could experience repercussions of the pandemic. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions leading to more effective mitigation strategies for the COVID-19 pandemic and beyond.

Plasma gradient of soluble urokinase-type plasminogen activator receptor is linked to pathogenic plasma proteome and immune transcriptome and stratifies outcomes in severe COVID-19 (preprint)

Disease caused by SARS-CoV-2 coronavirus (COVID-19) has resulted in significant morbidity and mortality world-wide. A systemic hyper-inflammation characterizes the severe COVID-19 disease often associated with acute respiratory distress syndrome (ARDS). Blood biomarkers capable of risk stratification are of great importance in effective triage and critical care of severe COVID-19 patients. In the present study we report higher plasma abundance of soluble urokinase-type plasminogen activator receptor (sUPAR), expressed by an abnormally expanded circulating myeloid cell population, in severe COVID-19 patients with ARDS. Plasma sUPAR level was found to be linked to a characteristic proteomic signature of plasma, linked to coagulation disorders and complement activation. Receiver operator characteristics curve analysis identified a cut-off value of sUPAR at 1996.809 pg/ml that could predict survival in our cohort (Odds ratio: 2.9286, 95% confidence interval 1.0427-8.2257). Lower sUPAR level than this threshold concentration was associated with a differential expression of the immune transcriptome as well as favourable clinical outcomes, both in terms of survival benefit (Hazard ratio: 0.3615, 95% confidence interval 0.1433-0.912) and faster disease remission in our patient cohort. Thus we identified sUPAR as a key pathogenic circulating molecule linking systemic hyperinflammation to the hypercoagulable state and stratifying clinical outcomes in severe COVID-19 patients with ARDS.

Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India (preprint)

Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India, and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an \href{https://bayesrx.shinyapps.io/COV-N/}{interactive dashboard} summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi, and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.

Pooling RT-PCR or NGS samples has the potential to cost-effectively generate estimates of COVID-19 prevalence in resource limited environments (preprint)

Background: COVID-19 originated in China and has quickly spread worldwide causing a pandemic. Countries need rapid data on the prevalence of the virus in communities to enable rapid containment. However, the equipment, human and laboratory resources required for conducting individual RT-PCR is prohibitive. One technique to reduce the number of tests required is the pooling of samples for analysis by RT-PCR prior to testing. Methods: We conducted a mathematical analysis of pooling strategies for infection rate classification using group testing and for the identification of individuals by testing pooled clusters of samples. Findings: On the basis of the proposed pooled testing strategy we calculate the probability of false alarm, the probability of detection, and the average number of tests required as a function of the pool size. We find that when the sample size is 256, with a maximum pool size of 64, with only 7.3 tests on the average, we can distinguish between prevalences of 1% and 5% with a probability of detection of 95% and probability of false alarm of 4%. Interpretation: The pooling of RT-PCR samples is a cost-effective technique for providing much-needed course-grained data on the prevalence of COVID-19. This is a powerful tool in providing countries with information that can facilitate a response to the pandemic that is evidence-based and saves the most lives possible with the resources available.