Diverse local epidemics reveal the distinct effects of population\n density, demographics, climate, depletion of susceptibles, and intervention\n in the first wave of COVID-19 in the United States
Bibliographic record
Abstract
The SARS-CoV-2 pandemic has caused significant mortality and morbidity\nworldwide, sparing almost no community. As the disease will likely remain a\nthreat for years to come, an understanding of the precise influences of human\ndemographics and settlement, as well as the dynamic factors of climate,\nsusceptible depletion, and intervention, on the spread of localized epidemics\nwill be vital for mounting an effective response. We consider the entire set of\nlocal epidemics in the United States; a broad selection of demographic,\npopulation density, and climate factors; and local mobility data, tracking\nsocial distancing interventions, to determine the key factors driving the\nspread and containment of the virus. Assuming first a linear model for the rate\nof exponential growth (or decay) in cases/mortality, we find that\npopulation-weighted density, humidity, and median age dominate the dynamics of\ngrowth and decline, once interventions are accounted for. A focus on distinct\nmetropolitan areas suggests that some locales benefited from the timing of a\nnearly simultaneous nationwide shutdown, and/or the regional climate conditions\nin mid-March; while others suffered significant outbreaks prior to\nintervention. Using a first-principles model of the infection spread, we then\ndevelop predictions for the impact of the relaxation of social distancing and\nlocal climate conditions. A few regions, where a significant fraction of the\npopulation was infected, show evidence that the epidemic has partially resolved\nvia depletion of the susceptible population (i.e., "herd immunity"), while most\nregions in the United States remain overwhelmingly susceptible. These results\nwill be important for optimal management of intervention strategies, which can\nbe facilitated using our online dashboard.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".