How feasible is it to mobilize $31 billion a year for pandemic preparedness and response? An economic growth modelling analysis
Bibliographic record
Abstract
Abstract Background Covid-19 has reinforced the strong health and economic case for investing in pandemic preparedness and response (PPR). The World Bank and World Health Organization (WHO) propose that low- and middle-income governments and donor countries should invest $31.1 billion each year for PPR. We analyse, based on the projected economic growth of countries between 2022 and 2027, how likely it is that low- and middle-income country governments and donors can mobilize the estimated funding. Methods We modelled trends in economic growth to project domestic health spending by low- and middle-income governments and official development assistance (ODA) by donors for years 2022 to 2027. We modelled two scenarios for countries and donors – a constant and an optimistic scenario. Under the constant scenario we assume that countries and donors continue to dedicate the same proportion of their health spending and ODA as a share of gross domestic product (GDP) and gross national income (GNI), respectively, as they did during baseline (the latest year for which data are available). In the optimistic scenario, we assume a yearly increase of 2.5% in health spending as a share of GDP for countries and ODA as a share of GNI for donors. Findings Our analysis shows that low-income countries would need to invest on average 37%, lower-middle income countries 9%, and upper-middle income countries 1%, of their total health spending on PPR each year under the constant scenario to meet the World Bank WHO targets. Donors would need to allocate on average 8% of their total ODA across all sectors to PPR each year to meet their target. Conclusions The World Bank WHO targets for PPR will not be met unless low- and middle-income governments and donors spend a much higher share of their funding on PPR. Even under optimistic growth scenarios, low-income and lower-middle income countries will require increased support from global health donors. The donor target cannot be met using the yearly increase in ODA under any scenario. If the country and donor targets are not met, the highest-impact health security measures need to be prioritized for funding. Alternative sources of PPR financing could include global taxation (e.g., on financial transactions, carbon, or airline flights), cancelling debt, and addressing illicit financial flows. There is also a need for continued work on estimating current PPR costs and funding requirements in order to arrive at more enduring and reliable estimates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".