Spatial diffusion of COVID-19: An econometric-based approach.
Bibliographic record
Abstract
An understanding of how infected-susceptible populations interact is critical to identify underlying causal factors and disease transmission patterns of infectious diseases. Disease transmission patterns are dynamic, non-linear, and spatially complex. This anisotropic characteristic of disease spread necessitates the ideal solution to be sensitive to the geographic context. A Spatial Diffusion Model (SDM) to predict interaction potential and COVID-19 risk probability is developed by adapting the Newtonian gravity model. This novel approach overcomes the limitations of existing epidemiological studies by characterizing the behavioral patterns of the infected population to model the spatiotemporal transmission of disease across the geographic space. The proposed model is robust as it couples a multicriteria behavioral pattern to enhance predictive capability. The model shows an 83.74% correlation with the observational COVID-19 case data. The highest risk patterns for COVID-19 are predicted in the neighborhoods of New York City (NYC), exhibiting clustered socioeconomic disparities along with racial and ethnic heterogeneity. Policymakers can use these results to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions. This study emphasizes the need for improved spatial epidemiological models including enhanced depictions of human activity patterns and the need to integrate spatial data with advanced mathematical models.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".