Estimating Hidden Population Size of COVID-19 using Respondent-DrivenSampling Method - A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Currently, the ongoing COVID-19 pandemic is posing a challenge to health systems worldwide. Unfortunately, the true number of infections is underestimated due to the existence of a vast number of asymptomatic infected individual's proportion. Detecting the actual number of COVID-19-affected patients is critical in order to treat and prevent it. Sampling of such populations, so-called hidden or hard-to-reach populations, is not possible using conventional sampling methods. The objective of this research is to estimate the hidden population size of COVID-19 by using respondent-driven sampling (RDS) methods. METHODS: This study is a systematic review. We have searched online databases of PubMed, Web of Science, Scopus, Embase, and Cochrane to identify English articles published from the beginning of December 2019 to December 2022 using purpose-related keywords. The complete texts of the final chosen articles were thoroughly reviewed, and the significant findings are condensed and presented in the table. RESULTS: Of the 7 included articles, all were conducted to estimate the actual extent of COVID-19 prevalence in their region and provide a mathematical model to estimate the asymptomatic and undetected cases of COVID-19 amid the pandemic. Two studies stated that the prevalence of COVID-19 in their sample population was 2.6% and 2.4% in Sierra Leone and Austria, respectively. In addition, four studies stated that the actual numbers of infected cases in their sample population were significantly higher, ranging from two to 50 times higher than the recorded reports. CONCLUSIONS: In general, our study illustrates the efficacy of RDS in the estimation of undetected asymptomatic cases with high cost-effectiveness due to its relatively trouble-free and low-cost methods of sampling the population. This method would be valuable in probable future epidemics.
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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 | 0.041 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".