Evidence of rapid rise in population immunity from SARS-CoV-2 subclinical infections through pre-vaccination serial serosurveys in Pakistan
Bibliographic record
Abstract
Background: Understanding factors associated with protective immunity against emerging viral infections is crucial for global health. Pakistan reported its first COVID-19 case on 26 February 2020, but experienced relatively low COVID-19-related morbidity and mortality between 2020 and 2022. The underlying reasons for this remain unclear, and our research aims to shed light on this crucial issue. Methods: We conducted a serial population-based serosurvey over 16 months (rounds 1-4, July 2020 to November 2021) across households in urban (Karachi) and rural (Matiari) Sindh, sampling 1100 households and 3900 individuals. We measured antibodies in sera and tested a subset of respiratory samples for COVID-19 using polymerase chain reaction (PCR) and antigen tests, also measuring haemoglobin (Hb), C-reactive protein (CRP), vitamin D, and zinc in round 1. Results: Participants showed 23% (95% confidence interval (CI) = 21.9-24.5) antibody seroprevalence in round 1, increasing across rounds 2-4 to 29% (95% CI = 27.4-30.6), 49% (95% CI = 47.2-50.9), and 79% (95% CI = 77.4-80.8), respectively. Urban residents had 2.6 times (95% CI = 1.9-3.6) higher odds of seropositivity than rural residents. Seropositivity did not differ between genders. Individuals aged 20-49 years had 7.5 (95% CI = 4.6-12.4) times higher odds of seropositivity compared to children aged 0-4 years. Most participants had no symptoms associated with COVID-19, with no reported mortality. Vitamin D deficiency was linked to seroprevalence. COVID-19 was confirmed in 1.8% of individuals tested via RT-PCR and antigen tests. Conclusions: The data suggests a steady increase in humoral immunity in Pakistan, likely due to increased transmission and associated asymptomatic disease. Overall, this reflects the longitudinal trend of protection against severe acute respiratory syndrome coronavirus 2, leading to the relatively low morbidity and mortality observed in the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".