Developing a Digital Solution for Dengue Through Epihack: Qualitative Evaluation Study of a Five-Day Health Hackathon in Sri Lanka
Bibliographic record
Abstract
BACKGROUND: Dengue is a mosquito-borne viral disease that has increasingly affected Sri Lanka in recent years. To address this issue, dengue surveillance through increasingly prevalent digital surveillance applications has been suggested for use by health authorities and the general public. Epihack Sri Lanka was a 5-day hackathon event organized to develop a digital dengue surveillance tool. OBJECTIVE: The goal of the research was to examine the effectiveness of a collaborative hackathon that brought together information technology (IT) and health experts from around the globe to develop a solution to the dengue pandemic in Sri Lanka. METHODS: Ethnographic observation and qualitative informal interviews were conducted with 58 attendees from 11 countries over the 5-day Epihack to identify the main factors that influence a collaborative hackathon. Interviews were transcribed and coded based on grounded theory. RESULTS: Three major themes were identified during the Epihack Sri Lanka event: engagement, communication, and current disease environment. Unlike other hackathons, Epihack had no winners or prizes and was collaborative rather than competitive, which worked well in formulating a variety of ideas and bringing together volunteers with a sense of civic duty to improve public health. Having health and IT experts work together concurrently was received positively and considered highly beneficial to the development of the product. Participants were overall very satisfied with the event, although they thought it could have been longer. Communication issues and cultural differences were observed but continued to decrease as the event progressed. This was found to be extremely important to the efficiency of the event, which highlighted the benefit of team-bonding exercises. Bringing expert knowledge and examples of systems from around the world benefited the creation of new ideas. However, developing a system that can adapt and cater to the local disease environment is important in successfully developing the concepts. CONCLUSIONS: Epihack Sri Lanka was successful in bringing together health and IT experts to develop a digital solution for dengue surveillance. The collaborative format achieved a variety of fruitful ideas and may lead to more hackathons working in this way in the future. Good communication, participant engagement, and stakeholder interest with adaptation of ideas to complement the current environment are vital to achieve the goals of the event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".