The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: Results from a statewide survey in Karnataka, India

Team Lead: Giridhara R. Babu; IISc contact: Rajesh Sundaresan
Email: epigiridhar@gmail.com, rajeshs@iisc.ac.in

Note: This is a pre-print study which has not been peer-reviewed yet.

Globally, the routinely used case-based reporting and IgG serosurveys underestimate the actual prevalence of COVID-19. Simultaneous estimation of IgG antibodies and active SARS-CoV-2 markers can provide a more accurate estimation. Methods: A cross-sectional survey of 16416 people covering all risk groups was done between 3-16 September 2020 using the infrastructure of Karnataka in 290 hospitals across all 30 districts. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT), and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active, and total prevalence, while multinomial regression identified predictors. Findings: The overall adjusted prevalence of COVID-19 in Karnataka was 27.3% (95% CI: 25.7-28.9), including IgG 16.4% (95% CI: 15.1 – 17.7) and active infection 12.7% (95% CI: 11.5-13.9). The case-to-infection ratio was 1:40, and the infection fatality rate was 0.05%. Influenza-like symptoms or contact with a COVID-19 positive patient are good predictors of active infection. The RAT kits had higher sensitivity (68%) in symptomatic participants compared to 47% in asymptomatic. Interpretation: This is the first comprehensive survey providing accurate estimates of the COVID-19 burden anywhere in the world. Further, our findings provide a reasonable approximation of population immunity threshold levels. Using the RAT kits and following the syndromic approach can be useful in screening and monitoring COVID-19. Leveraging existing surveillance platforms, coupled with appropriate methods and sampling framework, renders our model replicable in other settings.

Read the full paper at: https://www.medrxiv.org/content/10.1101/2020.12.04.20243949v1


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