Philippine immunization coverage and dengvaxia: An infodemiological study
Bibliographic record
Abstract
BACKGROUND: A dengue vaccine, dengvaxia, was licensed for the first time in 2015. It was approved for use in 11 countries where dengue infection is endemic, including the Philippines. In November 2017, controversy arose in the Philippines regarding the dengvaxia vaccine. We hypothesized that the dengvaxia controversy might be correlated with immunization coverage in the Philippines. METHODS: We performed an analytical and infodemiological study on web-based interest in dengvaxia, both globally and in 18 dengue endemic countries, from 2015 to 2020 using Google Trends™. Comparisons were made with search trends for the components of the National Immunization Program (NIP) and vaccine coverage by computing the Pearson product-moment correlation coefficient (r) between each variable. RESULTS: Among the 18 countries included, the Philippines had the highest search volume index for dengvaxia, with peaks in searches coinciding with that of worldwide search trends. There was no correlation between the relative search volume for dengvaxia with that of vaccines included in the NIP in the Philippines from 2015 to 2020. There was no significant correlation between web-based interest in dengvaxia and the estimated immunization coverage from 2015 to 2019. CONCLUSION: There was no significant correlation between web-based interest in dengvaxia, the vaccines in the NIP, and national immunization coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".