Bibliographic record
Abstract
Introduction: Digital epidemiology is an emerging branch of epidemiology that is enabled by digital tools and data sources. The extent to which digital epidemiology promoted health systems resilience during the COVID-19 pandemic remains an open question. The objective of this dissertation was to study the role of digital epidemiology in promoting resilient pandemicresponse using both analytic approaches to demonstrate specific use cases and a qualitative approach to study implementation. Methods: The first objective was a qualitative study of public-private partnerships between Canadian technology companies and health organizations to develop and deploy novel digital tools. The second objective was a spatial epidemiological study that created a social vulnerability index (SVI) using Census data in Canada and demonstrated its application in Ontario. The third objective was another spatial epidemiological study that assessed the suitability of smartphone-based mobility data (SBMD) to study urban greenspace use in Toronto. Finally, the fourth objective was a predictive modelling study that examined the stability of population-level prediction models designed to predict adverse COVID-19 outcomes (i.e., hospitalization, intensive care unit admission, and death) to dataset shift during the pandemic. Results: The first study explored the barriers and enablers of effective partnerships across three distinct digital applications. The second study demonstrated a positive association between the SVI and COVID-19 outcomes and a negative association between the SVI and COVID-19 vaccine uptake both across Ontario and the Greater Toronto Area. The third study illustrated how sampling bias, spatial inconsistencies, temporal variation, and the unavailability of disaggregated measures can hinder analyses with pre-aggregated SBMD. Finally, the fourth study characterized the impact of different sources of dataset shift such as the initial spread of SARS-CoV-2 through demographically diverse communities, the rise of stark health disparities in infection and disease severity, and the timing and eligibility of vaccination. Conclusions: This dissertation explored how digital epidemiology can facilitate resilient health system response to pandemics. It generated new knowledge about predictive models and data sources as well as how to guide partnerships to design and deploy digital tools. The findings within have considerable implications to guide future research, policy, and practice.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".