### Evolution of HIV

- Chimp → human transmission around 1900 gave rise to HIV-1 group M
- ~100 million infected people since
- subtypes differ at 10-20% of their genome
- HIV-1 evolves ~0.1% per year

image: Sharp and Hahn, CSH Persp. Med.
### HIV infection

- $10^8$ cells are infected every day
- the virus repeatedly escapes immune recognition
- integrates into T-cells as latent provirus

image: wikipedia
### HIV-1 evolution within one individual

silouhette: clipartfest.com, Zanini at al, 2015. Collaboration with Jan Albert and his group
### Population sequencing to track all mutations above 1%

- diverge at 0.1-1% per year
- almost full genomes coverage in 10 patients
- full data set at hiv.tuebingen.mpg.de

Zanini et al, eLife, 2015
### Diversity and rates of change

- envelope changes fastest, enzymes slowest
- identical rate of synonymous evolution
- diversity saturates where evolution is fast
- synonymous mutations stay at low frequency

Zanini et al, eLife, 2015
### Sequences record the spread of pathogens

##### The resolution is limited by the number of mutations!

images by Trevor Bedford
### Human seasonal influenza viruses

slide by Trevor Bedford
- Influenza virus evolves to avoid human immunity
- Vaccines need frequent updates

## Beyond tracking: can we predict?

### Competition between different viral variants

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derride; Kessler & Levine
### Predicting evolution

### Given the branching pattern:

- can we predict fitness?
- pick the closest relative of the future?

RN, Russell, Shraiman, eLife, 2014
### Fitness inference from trees

$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$

RN, Russell, Shraiman, eLife, 2014
### Prediction of the dominating H3N2 influenza strain

- no influenza specific input
- how can the model be improved? (see model by Luksza & Laessig)
- what other context might this apply?

RN, Russell, Shraiman, eLife, 2014
## nextstrain.org

- integrate data from many different sources
- analyze those data in near real time
- disseminate results in an intuitive yet informative way
- provide actionable insights

### Acknowledgments

- Fabio Zanini
- Jan Albert
- Johanna Brodin
- Christa Lanz
- GĂ¶ran Bratt
- Lina Thebo
- Vadim Puller

### Influenza and Theory acknowledgments

- Boris Shraiman
- Colin Russell
- Trevor Bedford
- Oskar Hallatschek

### nextstrain.org

- Trevor Bedford
- Colin Megill
- Pavel Sagulenko
- Sidney Bell
- James Hadfield
- Wei Ding