Assessing Global Pandemic Risks from Emerging Infectious Diseases and High Containment Laboratory Leaks: A Country Level Spatial Network SIR Model Analysis
Bibliographic record
Abstract
Abstract Future pandemics could arise from several sources, notably, Emerging Infectious Diseases (EID); and lab leaks from High Containment Biological Laboratories (HCBL). Recent advances in infectious disease, information technology and biotechnology provide building blocks to reduce pandemic risk if deployed intelligently. However, the global nature of infectious diseases, distribution of HCBLs, and increasing complexity of transmission dynamics due to travel networks, make it difficult to determine how to best deploy mitigation efforts. Increasing understanding of the risk landscape posed by EID and HCBL lab leaks could improve risk reduction efforts. The presented paper develops a country level spatial network Susceptible Infected Removed (SIR) model based on global travel network data and relative risk measures of potential origin sources, EID and lab leaks from Biological Safety Level 3+ and 4 labs, to explore expected infections over the first 30 days of a pandemic. Model outputs indicate that for EID and lab leaks India, the US and China are most impacted at day 30. For EID, expected infections shift from high EID origin potential countries at day 10 to the US, India and China, while for lab leaks the US and India start with high lab leak potential. With respect to model uncertainties and limitations, results indicate several large wealthy countries are influential to pandemic risk from both EID and lab leaks indicating high leverage points for mitigation efforts.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".