Mobile Apps Leveraged in the COVID-19 Pandemic in East and South-East Asia: Review and Content Analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic increased attention to digital tools to support governmental public health policies in East and South-East Asia. Mobile apps related to the COVID-19 pandemic continue to emerge and evolve with a wide variety of characteristics and functions. However, there is a paucity of studies evaluating such apps in this region, with most of the available studies conducted in the early days of the pandemic. OBJECTIVE: This study aimed to examine free apps developed or supported by governments in the East and South-East Asian region and highlight their key characteristics and functions. We also sought to interpret how the release dates of these apps were related to the commencement dates of other COVID-19 public health policies. METHODS: We systematically searched for apps in Apple App Store and Google Play Store and analyzed the contents of eligible apps. Mobile apps released or updated with COVID-19-related functions between March 1 and May 7, 2021, in Singapore, Taiwan, South Korea, China (mainland), Japan, Thailand, Hong Kong, Vietnam, Malaysia, Indonesia, and the Philippines were included. The CoronaNet Research Project database was also examined to determine the timeline of public health policy commencement dates in relation to the release dates of the included apps. We assessed each app's official website, media reports, and literature through content analysis. Descriptive statistics were used to summarize relevant information gathered from the mobile apps using RStudio. RESULTS: Of the 1943 mobile apps initially identified, 46 were eligible, with almost 70% of the apps being intended for the general public. Most apps were from Vietnam (n=9, 20%), followed by Malaysia, Singapore, and Thailand (n=6 each, 13%). Of note, most apps for quarantine monitoring (n=6, 13%) were mandatory for the target users or a population subset. The most common function was health monitoring (32/46, 70%), followed by raising public health awareness (19/46, 41%) through education and information dissemination. Other functions included monitoring quarantine (12/46, 26%), providing health resources (12/46, 26%). COVID-19 vaccination management functions began to appear in parallel with vaccine rollout (7/46, 15%). Regarding the timing of the introduction of mobile solutions, the majority of mobile apps emerged close to the commencement dates of other public health policies in the early stages of the pandemic between March and April 2020. CONCLUSIONS: In East and South-East Asia, most governments used mobile health apps as adjuncts to public health measures for tracking COVID-19 cases and delivering credible information. In addition, these apps have evolved by expanding their functions for COVID-19 vaccination.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".