Data rich phylodynamics

Trevor Bedford

Fred Hutchinson Cancer Center / Howard Hughes Medical Institute
2 Feb 2024
CCDD ID Epi Seminar Series
Harvard School of Public Health
 
Slides at: bedford.io/talks

Genomic epidemiology during the COVID-19 pandemic

Over 16M SARS-CoV-2 genomes shared to GISAID and evolution tracked in real-time at nextstrain.org

Richard Neher,   Ivan Aksamentov,   Kim Andrews,   Jennifer Chang   James Hadfield,   Emma Hodcroft,   John Huddleston,   Jover Lee,   Victor Lin,   Cornelius Roemer,   Thomas Sibley

Three key insights that genomic epi provided during pandemic

1. Rapid human-to-human spread in Wuhan beyond initial market outbreak
2. Extensive local transmission while testing was rare
3. Identification of variants of concern and mapping of increased transmission rates

Jan 11: First five genomes showed a novel SARS-like coronavirus

Initially thought clustering due to epi investigation of linked cases at Huanan seafood market

Jan 19: First 12 genomes from Wuhan (blue) and Bangkok (red) showed lack of genetic diversity

Jan 23: Introduction into the human population between Nov 15 and Dec 15 and subsequent rapid human-to-human spread

Rapid global epidemic spread from China

Epidemic in the USA was introduced from China in late Jan and from Europe during Feb

Early sequencing provided best estimate of extent of local outbreak

After initial wave, with mitigation
efforts and decreased travel,
regional clades emerge

Emergence and spread of initial VOC viruses

Alpha

Gamma

Continued evolution post-Omicron

Future of genomic epidemiology

The COVID-19 pandemic has pushed the field perhaps ~5 years into the future

Genomic outbreak investigation

Phylogeography infers migration matrix via phylogeny

Paredes et al. 2022. medRxiv.

Phylogeography infers migration matrix via phylogeny

However, this approach faces significant issues with scalability and sampling bias

Paredes et al. 2022. medRxiv.

Model emergence, spread and dissolution of clusters of identical sequences

Tran Kiem et al.

Identical sequences generally closely linked

One mutation every ~13 days vs duration of infection of ~5 days

Tran Kiem et al.

Spatial and social determinants of transmission from clusters of identical sequences

114k SARS-CoV-2 genomes from Washington State sentinel surveillance annotated with
geographic location and age

Tran Kiem et al.

Pairs of identical sequences 5.1 times more likely to come from same county

Tran Kiem et al.

Relative risk score robust to sampling intensity

Tran Kiem et al.

Between county enrichment corresponds well with geography

Tran Kiem et al.

Between age group enrichment shows social mixing patterns

Tran Kiem et al.

Sequence-derived mixing patterns correlate well with expectations from journaling studies

Tran Kiem et al.

Signal for differential local vs long-distance age mixing

Tran Kiem et al.

Evolutionary Forecasting

Currently 2857 Pango lineages and samples can be rapidly assigned lineages in Nextclade or UShER

Variants that are just 1-2 mutations different will get a label

Population genetic expectation of variant frequency under selection

$x' = \frac{x \, (1+s)}{x \, (1+s) + (1-x)}$ for frequency $x$ over one generation with selective advantage $s$

$x(t) = \frac{x_0 \, (1+s)^t}{x_0 \, (1+s)^t + (1-x_0)}$ for initial frequency $x_0$ over $t$ generations

Trajectories are linear once logit transformed via $\mathrm{log}(\frac{x}{1 - x})$

Consistent frequency dynamics in logit space (BA.2 Mar 2022)

Consistent frequency dynamics in logit space (BA.5 Jul 2022)

Consistent frequency dynamics in logit space (JN.1 Dec 2023)

Multinomial logistic regression

Multinomial logistic regression across $n$ variants models the probability of a virus sampled at time $t$ belonging to variant $i$ as

$$\mathrm{Pr}(X = i) = x_i(t) = \frac{p_i \, \mathrm{exp}(f_i \, t)}{\sum_{1 \le j \le n} p_j \, \mathrm{exp}(f_j \, t) }$$

with $2n$ parameters consisting of $p_i$ the frequency of variant $i$ at initial timepoint and $f_i$ the growth rate or fitness of variant $i$.

Original VOC viruses had substantially increased transmissibility

Model from Figgins and Bedford. 2022. medRxiv.

Variant frequencies across countries from Feb 2022 to present

We find that recent variants are ~250% fitter than original Omicron BA.1

Assessing MLR models for short-term frequency forecasting

Retrospective projections twice monthly during 2022

Abousamra, Figgins and Bedford. 2023. medRxiv.

+30 day short-term forecasts across different countries

Abousamra, Figgins and Bedford. 2023. medRxiv.

MLR models generate accurate short-term forecasts

30 days out, countries range from 6 to 10% mean absolute error

Abousamra, Figgins and Bedford. 2023. medRxiv.

Clade and lineage forecasts continuously updated at nextstrain.org

The global sweep of JN.1 is now largely complete

Figgins et al. nextstrain.org

Growth advantage readily identifiable while lineage is still rare

Growth advantage readily identifiable while lineage is still rare

Acknowledgements

SARS-CoV-2 genomic epi: Data producers from all over the world and GISAID

Nextstrain: Richard Neher, Ivan Aksamentov, Kim Andrews, Jennifer Chang, James Hadfield, Emma Hodcroft, John Huddleston, Jover Lee, Victor Lin, Cornelius Roemer, Thomas Sibley

Determinants of transmission: Cécile Tran Kiem, Amanda Perofsky, Miguel Paredes, Lauren Frisbie, Allison Black, Cécile Viboud

Evolutionary forecasting: Marlin Figgins, Jover Lee, James Hadfield, John Huddleston, Cornelius Roemer, Richard Neher

Bedford Lab: John Huddleston,   James Hadfield,   Katie Kistler,   Thomas Sibley,   Jover Lee,   Cassia Wagner,   Miguel Paredes,   Nicola Müller,   Marlin Figgins,   Victor Lin,   Jennifer Chang,   Eslam Abousamra,   Nashwa Ahmed,   Cécile Tran Kiem,   Kim Andrews