How viruses spread across space and time: forecasting pandemic progression by modelling geographico-temporal interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has revealed severe flaws in the global healthcare systems ability to respond to unexpected health catastrophes. Much of the confusion and mishandling of the situation could be attributed to the failure in accurately predicting the spread of the virus across geographical locations. A global resource shortage in essential medical supplies and equipment, such as personal protective equipment (PPE) and ventilators, led to a compromised global supply chain. As a result, resources could not be allocated as needed to curb the spread of the pathogen in the most efficacious way. Although forecast models and machine learning algorithms have served as invaluable tools in devising effective response strategies, a large majority of these models were limited by their ability to describe the intricate interactions that underlie the spatio-temporal dynamics of viral proliferation. To address this issue, we employed a vector autoregression model to help capture the evolution of the disease across both the spatial and the temporal axes. Unlike traditional autoregression models, the present model is able to account for statistical regularities that exist both within a given region, and between geographical locations. Our results demonstrate that this approach accurately described the relationships across domestic and international localities throughout the evolution of the disease.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it