Estimating the proportion and IgG antibody response to SARS-CoV-2 in individuals joining a central educational institute from different parts of India by Enzyme-linked immunosorbent assay (ELISA) based serology.

BACKGROUND: Antibody response to SARS-CoV may be estimated to give trends and patterns emerging in a population during an evolving epidemic. The novel coronavirus has opened a new chapter in the history of pandemics and understanding the disease epidemiology. METHODS: The study was a cross-sectional descriptive study. Institutional Ethical clearance and informed consent were taken for participation in the study. The study population included all personnel reporting to the institute for training courses, permanent posting or joining back from leave during the study period of 2 months (16 June to 16 August 2020). The sample size was calculated assuming the prevalence of COVID-19 to be 1% with the absolute precision of 0.5% and 5% level of significance, and finite correction for population size of 500, and the calculated sample size was 377. Inclusion criteria were all personnel reporting to the institute from different states and districts. Exclusion criteria-Any personnel reported for a short visit of lesser than 14 days. Demographic details and details of any likely exposure to a confirmed COVID-19 case were noted. A blood sample was collected, and serological tests were done using ErbaLisa COVID-19 IgG kit by Calbiotech, as per the manufacturer's instructions. RESULTS: Overall seropositivity of IgG COVID-19 antibodies was 7.5% (31/413) (95% CI: 5.3-10.4%). Study population (n = 413) comprised of an adult population in the age range of 21 years-53 years, and the mean age was 31.4 years (SD = 6.2 years). CONCLUSION: As the personnel joining the institute have come from various parts of the country the study provides an estimation of antibodies against COVID-19.

Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India.

BACKGROUND: The World Health Organization on 11 March 2020, declared COVID-19 as a pandemic. India initiated social distancing measures to combat the epidemic of COVID-19. The course of the epidemic of COVID-19 for India was predicted using stochastic probability-based mathematical modeling. METHODS: Data synthesis for the top few countries affected was studied for various factors affecting the epidemic. For projections of infected cases for India, the modified susceptible-exposed-infectious-removed/recovered framework modified for the effect of social distancing (Rho) was used. Simulation was carried out for 10,000 runs using Python. Projections for infected cases and hospitalization requirement were estimated. RESULTS: The epidemic curve will peak in the third week of June in India with 17,525,869 and 2,153,200 infected people with reproduction number of 1.8 and Rho of 0.7 and 0.6, respectively. Compared with the baseline scenario of no social distancing, for transmissibility with R0 = 1.8, the reduction in infections due to social distancing measure is 78% (Rho = 0.7) and 97% (Rho = 0.6). Similarly for R0 = 2.2 and 2.4, the reduction in infected numbers slightly lowers to 62% and 66% with Rho = 0.7 and 92% and 75% with Rho = 0.6, respectively. With R0 = 1.8 and Rho = 0.6, the Intensive Care Unit (ICU) bed requirement is 107,660, whereas if transmissibility is high, the ICU bed requirement would increase to 1,994,682. CONCLUSIONS: The social distancing measures seem to have been working for India in absence of treatment in sight for COVID-19. Although with the government's response strategy of social distancing, the peak of the epidemic is extended giving more months for preparedness to the country; however, the sustainability of these measures is uncertain.