CDCgov / cfa-viral-lineage-model

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cfa-viral-lineage-model

⚠️ The work in this repository is in progress and highly experimental. ⚠️

Overview

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:

Architecture

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
... ... ... ... ...

Milestones & timeline

Must-haves

Wishlist

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

A ladder of (multinomial logistic regression) models for consideration

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?

  1. 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]
  2. One geography, multiple variants, multinomial
  3. Multiple geographies, multiple variants, multinomial, no correlations or partial pooling
    • Statistically equivalent to #2, but requires some different computational implementation
  4. 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 prior
    • i=variant j=geography
  5. As above plus partial pooling of intercepts by variant across geographies
  6. 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

General Disclaimer

This repository was created for use by CDC programs to collaborate on public health related projects in support of the CDC mission. GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise.

Public Domain Standard Notice

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

License Standard Notice

This repository is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.

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The source code forked from other open source projects will inherit its license.

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