CovidSIMVL – Agent-Based Modeling of Localized Transmission within a Heterogeneous Array of Locations – Motivation, Configuration and Calibration
Bibliographic record
Abstract
ABSTRACT CovidSIMVL is an agent-based infectious disease modeling tool that is designed specifically to simulate localized spread of infectious disease. It is intended to support tactical decision-making around localized/staged re-institution of pre-pandemic levels and patterns of social/economic/health service delivery activity, following an initial stage of pan-societal closures of social/economic institutions and broad-based reductions in services. By design, CovidSIMVL supports the generation of dynamic models that reflect heterogeneity within and between a network of interacting localized contexts. This heterogeneity is embodied in a hierarchically organized set of rules. Primary rules reflect the pathophysiology of transmission. Secondary rules (“HazardRadius” and “Mingle Factor” in CovidSIMVL) relate transmission to proximity and movement within physically demarcated and relatively contained spaces (“Universes”). Tertiary rules (“Schedules”) relate probabilities of transmission to movement of people between a network of localized contexts (a CovidSIMVL “Multiverse”). This report focuses mainly on calibration of secondary rules. To calibrate the HazardRadius and MingleFactor parameters, growth curves were generated with CovidSIMVL by setting different configurations of values on those two proximal determinants of viral transmission. These were compared to the characteristic shapes of curves generated by equation-based compartmental models (e.g., SEIR models) that fit different real-world datasets embodying different reproduction numbers (R 0 ). By operating with parameter values in CovidSIMVL that generate “real-world” growth curves, the tool can be used to produce plausible simulations of localized chains of transmission. These include transmission among different groups of persons (e.g., staff, patients) who are co-located within a single setting such as a long-term care facility. The Multiverse version of CovidSIMVL can be used to simulate localized cross-over transmission among arrays consisting of both unaffected and impacted contexts and associated sub-populations, via agents who interact within and across arrays of contexts such as schools, multigenerational families, recreational facilities, places of work, emergency shelters for homeless persons, or other settings in which people are in close physical proximity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".