A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report
Bibliographic record
Abstract
This paper presents a dynamic structural model to evaluate economic and public health effects of the diffusion of COVID-19, as well as the impact of factual and counterfactual public policies. Our framework combines a SIR epidemiological model of virus diffusion with a structural game of network production and social interactions. The economy comprises three types of geographic locations: homes, workplaces, and consumption places. Each individual has her own set of locations where she develops her life. The combination of these sets for all the individuals determines the economy's production and social network. Every day, individuals choose to work and consume either outside (with physical interaction with other people) or remotely (from home, without physical interactions). Working (and consuming) outside is more productive and generates stronger complementarities (positive externality). However, in the presence of a virus, working outside facilitates infection and the diffusion of the virus (negative externality). Individuals are forward-looking. We characterize an equilibrium of the dynamic network game and present an algorithm for its computation. We describe the estimation of the parameters of the model combining several sources of data on COVID-19 in Ontario, Canada: daily epidemiological data; hourly electricity consumption data; and daily cell phone data on individuals' mobility. We use the model to evaluate the health and economic impact of several counterfactual public policies: subsidies for working at home; testing policies; herd immunity; and changes in the network structure. These policies generate substantial differences in the propagation of the virus and its economic impact.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".