Bayesian Spatiotemporal, Sample Survey, and Forecasting Methods for Analyzing COVID-19 Infections and Mortality
Bibliographic record
Abstract
For decades, mathematicians and statisticians have been modelling infectious diseases to forecast case/death counts, estimate important epidemiological quantities, and understand the dynamics of disease spread.This dissertation offers methodological insights into each of these three challenges using novel spatial, spatio-temporal, and Bayesian modelling methods, with applications to COVID-19 data. Alongside methodological contributions, this thesis also presents estimates of important epidemiological quantities which, subject to peer review, could be utilized by public health professionals and policy makers. There are four primary contributions of this work: 1) a subnational, single-wave COVID-19 mortality forecasting model that accounts for day-of-the-week effects, which was shown to outperform the most highly-cited model during the first viral wave; 2) a mobility-augmented spatial model for COVID-19 case counts, where cellphone-derived mobility data is shown to capture dependence between areal units better than physical proximity; 3) a novel, interpretable spatio-temporal infectious disease model where infectiousness is a function of mobility between areal units, resulting in estimates of the risk associated with travelling in two Spanish Communities; 4) a modular Bayesian framework based on mixture modelling of serological data and disaggregated deaths data to estimate COVID-19 incidence and infection fatality rates, resulting in estimates of these quantities across Canada for various strata. Although the applications in this thesis are to COVID-19 data, the proposed methodology can be applied to a wide spectrum of problems across infectious disease epidemiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".