The epidemiology of the COVID-19 pandemic in the small, low-resource country of Timor-Leste, January 2020 – June 2022
Bibliographic record
Abstract
Abstract: Timor-Leste, a small, mountainous half-island nation which shares a land border with Indonesia and which is 550 km from Australia, has a population of 1.3 million and achieved independence for the second time in 2002. It is one of the poorest nations in Asia. In response to the global coronavirus disease 2019 (COVID-19) pandemic, the Timor-Leste Ministry of Health undertook surveillance and contact tracing activities on all notified COVID-19 cases. Between 1 January 2020 and 30 June 2022, there were 22,957 cases of COVID-19 notified which occurred in three waves, the first which was delayed until April 2021 (community transmission of B.1.466.2 variant following major flooding), followed by waves in August 2021 (B.1.617.2 Delta variant transmission) and February 2022 (B.1.1.529 Omicron variant transmission). There were 753 people hospitalised due to COVID-19 and 133 deaths. Of the 133 deaths, 122 (92%) were considered not fully vaccinated (< 2 COVID-19 vaccines) and none had received boosters. Timor-Leste implemented measures to control COVID-19, including: rapid closure of international borders; isolation of cases; quarantining of international arrivals and close contacts; restrictions on internal travel; social and physical distancing; and, finally, a country-wide vaccination program. The health system's capacity was never exceeded.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".