U.S. state-level COVID-19 transmission insights from a mechanistic mobility-incidence model
Bibliographic record
Abstract
S ummary Background Throughout the COVID-19 pandemic, human mobility has played a central role in shaping disease transmission. In this study, we develop a mechanistic model to calculate disease incidence from commercially-available US mobility data over the course of 2020. We use it to study, at the US state level, the lag between infection and case report. We examine the evolution of per-contact transmission probability, and its dependence on mean air temperature. Finally, we evaluate the potential of the model to produce short-term incidence forecasts from mobility data. Methods We develop a mechanistic model that relates COVID-19 incidence to time series contact index (CCI) data collected by mobility data vendor Cuebiq. From this, we perform maximum-likelihood estimates of the transmission probability per CCI event. Finally, we retrospectively conduct forecasts from multiple dates in 2020 forward. Findings Across US states, we find a median lag of 19 days between transmission and case report. We find that the median transmission probability from May onward was about 20% lower than it was during March and April. We find a moderate, statistically significant negative correlation between mean state temperature and transmission probability, r = − .57, N = 49, p = 2 × 10 −5 . We conclude that for short-range forecasting, CCI data would likely have performed best overall during the first few months of the pandemic. Interpretation Our results are consistent with associations between colder temperatures and stronger COVID-19 burden reported in previous studies, and suggest that changes in the per-contact transmission probability play an important role. Our model displays good potential as a short-range (2 to 3 week) forecasting tool during the early stages of a future pandemic, before non-pharmaceutical interventions (NPIs) that modify per-contact transmission probability, principally face masks, come into widespread use. Hence, future development should also incorporate time series data of NPI use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".