README

Lineage Frequency Dynamics and Growth-Advantage Estimation from Genomic Surveillance Counts

An R package for modeling pathogen lineage frequencies, estimating growth advantages, and forecasting variant replacement dynamics from genomic surveillance counts.

Why lineagefreq?

Three lines of code transform raw surveillance counts into publication-ready model fits, growth advantage estimates, and probabilistic forecasts — with built-in backtesting for honest accuracy evaluation.

Installation

Without lineagefreq	With lineagefreq
Raw point estimates, no model	MLR / hierarchical MLR / Piantham engines
No uncertainty quantification	95% prediction intervals (parameter + sampling)
No forecasting	Probabilistic 2–6 week frequency forecasts
No evaluation framework	Rolling-origin backtest + MAE/WIS/coverage
Ad hoc scripts per analysis	Reproducible `lfq_data` → `fit_model` → `forecast` pipeline
Not on CRAN	CRAN-distributable, tested on 4 platforms

# install.packages("pak")
pak::pak("CuiweiG/lineagefreq")

# Or with devtools:
# devtools::install_github("CuiweiG/lineagefreq")

Quick example

library(lineagefreq)
library(ggplot2)

data(cdc_sarscov2_jn1)
x <- lfq_data(cdc_sarscov2_jn1,
              lineage = lineage, date = date, count = count)

fit <- fit_model(x, engine = "mlr")
growth_advantage(fit, type = "relative_Rt", generation_time = 5)

fc <- forecast(fit, horizon = 28)
autoplot(fc)

Real-Data Case Studies

Figures below use real U.S. CDC surveillance data (data.cdc.gov/jr58-6ysp, public domain). Two independent epidemic waves illustrate model behavior across distinct replacement settings.

Data accessed 2026-03-28. Lineages below 5% peak frequency collapsed to “Other.” Reproducible scripts: data-raw/prepare_cdc_data.R and data-raw/prepare_ba2_data.R.

Variant Replacement Dynamics

JN.1 emergence (Oct 2023 – Mar 2024): MLR recovers the observed replacement trajectory from <1% to >80%.

BA.1 → BA.2 period (Dec 2021 – Jun 2022): A well-characterized Omicron replacement wave with four sequential subvariant sweeps.

Growth Advantage Estimation

Relative Rt estimates are consistent with published values: BA.2 = 1.34× vs BA.1 (Lyngse et al. 2022, published 1.3–1.5×); KP.3 = 1.36× vs JN.1. Generation times: 3.2 days for Omicron BA.* subvariants (Du et al. 2022); 5.0 days for JN/KP lineages.

Frequency Forecast

Six-week projection with 95% marginal prediction intervals (pointwise, not simultaneous). Uncertainty reflects parameter estimation error (MVN from Fisher information) and multinomial sampling noise (n_eff = 100 sequences/period). See figure caption for full methodological notes.

Forecast Accuracy

Rolling-origin out-of-sample evaluation on the BA.2 period: approximately 4% MAE at 2-week and 8% at 4-week horizon.

Features

Model fitting - fit_model() with engines "mlr", "hier_mlr", "piantham", "fga", "garw" (Bayesian engines require ‘CmdStan’)

Inference - Growth advantage in four scales: growth rate, relative Rt, selection coefficient, doubling time

Forecasting - Probabilistic frequency forecasts with parametric simulation and configurable sampling noise

Evaluation - Rolling-origin backtesting via backtest() with standardized scoring (MAE, RMSE, coverage, WIS) via score_forecasts()

Surveillance utilities - summarize_emerging(): binomial GLM trend tests per lineage - sequencing_power(): minimum sample size for detection - collapse_lineages(), filter_sparse(): preprocessing

Visualization - autoplot() methods for fits, forecasts, and backtest summaries - Publication-quality output with colorblind-safe palettes

Interoperability - broom-compatible: tidy(), glance(), augment() - as_lfq_data() generic for extensible data import - read_lineage_counts() for CSV input

Supported pathogens

Any pathogen with variant/lineage-resolved sequencing count data: SARS-CoV-2, influenza, RSV, mpox, and others.

Citation

citation("lineagefreq")

lineagefreq