SARS-CoV-2 Seroprevalence in 12 Cities of India from July-December 2020
Bibliographic record
Abstract
SUMMARY Objectives We sought to understand the spread of SARS-CoV-2 infection in urban India, which has surprisingly low COVID-19 deaths. Design Cross-sectional and trend analyses of seroprevalence in self-referred test populations, and of reported cases and COVID mortality data. Participants 448,518 self-referred individuals using a nationwide chain of private laboratories with central testing of SARS-CoV-2 antibodies and publicly available case and mortality data. Setting 12 populous cities with nearly 92 million total population. Main outcome measures Seropositivity trends and predictors (using a Bayesian geospatial model) and prevalence derived from mortality data and infection fatality rates (IFR). Results For the whole of India, 31% of the self-referred individuals undergoing antibody testing were seropositive for SARS-CoV-2 antibodies. Seropositivity was higher in females (35%) than in males (30%) overall and in nearly every age group. In these 12 cities, seroprevalence rose from about 18% in July to 41% by December, with steeper increases at ages <20 and 20-44 years than at older ages. The “M-shaped” age pattern is consistent with intergenerational transmission. Areas of higher childhood measles vaccination in earlier years had lower seropositivity. The patterns of increase in seropositivity and in peak cases and deaths varied substantially across cities. In Delhi, death rates and cases first peaked in June and again in November; Chennai had a single peak in July. Based local IFRs and COVID deaths (adjusted for undercounts), we estimate that 43%-65% of adults above age 20 had been infected (range of mid-estimates of 12%-77%) corresponding 26 to 36 million infected adults in these cities, or an average of 9-12 infected adults per confirmed case. Conclusion Even with relatively low death rates, the large cities of India had remarkably high levels of SARS-CoV-2 infection. Vaccination strategies need to consider widespread intergenerational transmission.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".