The challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing in low-middle income countries and possible cost-effective measures in resource-limited settings
Bibliographic record
Abstract
Diagnostic testing for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains a challenge around the world, especially in low-middle-income countries (LMICs) with poor socio-economic backgrounds. From the beginning of the pandemic in December 2019 to August 2021, a total of approximately 3.4 billion tests were performed globally. The majority of these tests were restricted to high income countries. Reagents for diagnostic testing became a premium, LMICs either cannot afford or find manufacturers unwilling to supply them with expensive analytical reagents and equipment. From March to December 2020 obtaining testing kits for SARS-CoV-2 testing was a challenge. As the number of SARS-CoV-2 infection cases increases globally, large-scale testing still remains a challenge in LMICs. The aim of this review paper is to compare the total number and frequencies of SARS-CoV-2 testing in LMICs and high-income countries (HICs) using publicly available data from Worldometer COVID-19, as well as discussing possible interventions and cost-effective measures to increase testing capability in LMICs. In summary, HICs conducted more SARS-CoV-2 testing (USA: 192%, Australia: 146%, Switzerland: 124% and Canada: 113%) compared to middle-income countries (MICs) (Vietnam: 43%, South Africa: 29%, Brazil: 27% and Venezuela: 12%) and low-income countries (LICs) (Bangladesh: 6%, Uganda: 4% and Nigeria: 1%). Some of the cost-effective solutions to counteract the aforementioned problems includes using saliva instead of oropharyngeal or nasopharyngeal swabs, sample pooling, and testing high-priority groups to increase the number of mass testing in LMICs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".