Epidemic preparedness and response capacity against infectious disease outbreaks in 186 countries, 2018–2022
Bibliographic record
Abstract
OBJECTIVES: Disruptive public health risks and events, including infectious disease outbreaks, are inevitable, but their effects can be mitigated by investing in prevention and preparedness. We assessed the epidemic preparedness and response capacities of health systems in 186 countries from 2018 to 2022. METHODS: We utilized data from the International Health Regulations (IHR) State Party Self-Assessment Annual Reporting (SPAR) submissions to assess health systems' IHR capacities to (1) prevent, (2) detect, (3) respond, (4) enable resources and coordinate, and (5) ensure operational readiness from 2018 to 2022. We categorized the IHR capacities into five levels, with level 1 denoting the lowest level of national capacity and level 5 the highest. We calculated each index's capacity level as the arithmetic mean of its related indicators and analyzed changes over time using the Mann-Kendall nonparametric trend test. RESULTS: SPAR reporting marginally improved from 92.9% (182 of 196 countries) in 2018 to 94.9% (186 of 196 countries) in 2022, with considerable improvement in all five capacity domains over this period: prevention (58.4 in 2018 to 66.5 in 2022), detection (74.7 to 78.3), response (56.5 to 67.8), enabling resources and coordination (63.0 to 68.3), and ensuring operational readiness (62.8 to 69.9). From the 2022 submissions, 116 (62%) countries reported functional (Level 4 or 5) prevention capacity, 162 (87%) had functional detection capacity, 118 (63%) had functional response capacity, 121 (65%) had functional enabling resources and coordination capacity, and 133 (72%) had functional operational readiness against public health events. Across all the indexes, the WHO African Region reported the fewest countries with functional capacity in these domains. CONCLUSIONS: There was an overall increase in functional capacity across all five domains at both global and regional levels; and a high percentage of countries achieved functional capacity across all domains in 2022. However, a significant number of countries, particularly in the Global South, have yet to achieve functional competence in these capacities, leaving the world vulnerable to the persistent risk of epidemics and infectious biohazards. Strengthening IHR competencies through local, national, and global engagements must be urgently prioritized to achieve global health security against infectious diseases.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".