This repo hosts work on modeling how the composition of viral lineages, such as SARS-CoV-2 Pango lineages, changes over time.
The repo has the following structure:
linmod/, with a package for downloading lineage count data, and fitting and evaluating models with this data; andretrospective-forecasting/, with code that uses this package to automate the retrospective evaluation of models.
The model is provided with lightly-preprocessed data of variant sequences from humans in the USA, from Nextstrain (data dictionary).
An Apache Parquet is provided, with columns date, fd_offset, division, lineage, count.
Rows are uniquely identified by (date, division, lineage).
date and fd_offset can be computed from each other, given the forecast date.
Note that date is the sample collection date. fd refers to the forecast date. fd_offset is date - fd measured in days. Sequences are filtered to have a collection date no later than the forecast date.
| date | fd_offset | division | lineage | count |
|---|---|---|---|---|
| 2024-05-07 | -12 | Arizona | 24A | 1 |
| 2024-05-04 | -15 | Pennsylvania | 24A | 2 |
| ... | ... | ... | ... | ... |
The model must output samples of population-level lineage proportions.
An Apache Parquet should be provided, with columns fd_offset, division, lineage, sample_index, and phi (the population proportion), for fd_offset = -30, ..., 14.
Rows are uniquely identified by (fd_offset, division, lineage, sample_index).
| fd_offset | division | lineage | sample_index | phi |
|---|---|---|---|---|
| -30 | Alabama | 22B | 1 | 0.000014979599 |
| -30 | Alabama | 22B | 2 | 9.945703e-7 |
| ... | ... | ... | ... | ... |
- (1) Implement baseline and starter models
- Regression assuming spatial independence, no time covariate
- Regression assuming spatial independence and a time covariate
- (2) Design simulation study to verify model implementation
- (3) Implement a couple metrics to evaluate population-level lineage proportion forecasts in a retrospective setting
- (4) Conduct retrospective evaluations of our models with our metrics
- (5) Prepare for symposium & friends
- (6) Implement a metric to evaluate population-level lineage domination time predictions in a retrospective setting
- Answer two questions: will lineage X take off? Given that lineage X takes off, at what time point does it reach 50% phi?
- (7) Can we obtain lineage growth rates in a model-agnostic way, from only posterior samples of population-level lineage proportions?
- (8) Implement more advanced model and simulation study to verify
- Regression with information sharing over space
- (9) Study more on how to set priors on the logit scale to induce priors on the probability simplex
- (10) Does our ability to identify "good" models change if we evaluate daily vs weekly predictions?
| Sprint | Start Date | Target milestones | Notes |
|---|---|---|---|
| L | Jun 10 | 1 | |
| M | Jun 24 | 1, 2 | |
| N | Jul 08 | 2, 3 | |
| O | Jul 22 | 3 | |
| P | Aug 05 | 3, 4 | Thanasi at JSM for one week here |
| Q | Aug 19 | 4, 5, 8 | |
| R | Sep 2 | ||
| S | Sep 16 |
It seems useful to start at the bottom of the ladder, both for debugging purposes, but also to get a sense of the added predictive power of each step. You get more and more parameters to estimate; how does that balance against the improved accuracy?
- One human population/geography (e.g. state, HHS region, country), two pathogen populations (dominant variant vs. everything else), binomial dynamics
- i.e.
dominant% ~ invlogit[intercept + "slope" * time]
- i.e.
- One geography, multiple variants, multinomial
- Multiple geographies, multiple variants, multinomial, no correlations or partial pooling
- Statistically equivalent to #2, but requires some different computational implementation
- As above plus partial pooling of "slopes" by variant across geographies, but without correlations:
beta_1ij ~ N(mu_beta1i, sigma_beta1i)mu_beta1i ~ some prior,sigma_beta1i ~ some priori=variantj=geography
- As above plus partial pooling of intercepts by variant across geographies
- As above plus correlations between "slopes" by variant across counties
- i.e. if growth rates of variant A and B are correlated across countries X and Y, then we also expect variants C and D to have correlated growth rates across countries X and Y
beta_1*j ~ MVN(mu_beta1*, Sigma)Sigma ~ some LKJ prior
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