How previous epidemics enable timelier COVID-19 responses: an empirical study using organisational memory theory
Bibliographic record
Abstract
INTRODUCTION: There has been little systematic exploration into what affects timeliness of epidemic response, despite the potential for earlier responses to be more effective. Speculations have circulated that previous exposure to major epidemics helped health systems respond more quickly to COVID-19. This study leverages organisational memory theory to test whether health systems with any, more severe, or more recent exposure to major epidemics enacted timelier COVID-19 policy responses. METHODS: A data set was constructed cataloguing 846 policies across 178 health systems in total, 37 of which had major epidemics within the last 20 years. Hypothesis testing used OLS regressions with World Health Organization region fixed effects, controlling for several health system expenditure and political variables. RESULTS: Results show that exposure to any major epidemics was associated with providing earlier response in the following policy categories: all policies, surveillance/response, distancing, and international travel policies. The effect was about 6-10 days earlier response. The significance of this variable was largely nullified with the addition of the other two independent variables. Neither total cases nor years since previous epidemics showed no statistical significance. CONCLUSION: This study suggests that health systems may learn from past major epidemics. Policymakers ought to institutionalise lessons from COVID-19. Future studies can examine specific generalisable lessons and whether timelier responses correlated with lower health and economic impacts.
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 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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".