* Denotes a recommended reading
** Denotes a highly recommended reading

Week 1: Jan 8 - 12

Volz, E. M., Koelle, K., & Bedford, T. (2013). Viral phylodynamics. PLoS Computational Biology, 9(3), e1002947.
* Provides a bird’s-eye overview of phylodynamics

Week 2: Jan 15 - 19

Pereira, R., Oliveira, J., & Sousa, M. (2020). Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics. Journal of Clinical Medicine, 9(1), 132.

Week 3: Jan 22 - 26

Felsenstein , J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. J. Mol. Evol., 17(6) 368-376.
Introduces the Felsenstein pruning algorithm

Holder, M., & Lewis, P. O. (2003). Phylogeny estimation: traditional and Bayesian approaches. Nature Reviews Genetics, 4(4), 275.
* Reviews key aspects of Bayesian phylogenetic inference

Yang, Z. (2014). Molecular evolution: a statistical approach. Oxford University Press.
* Chapter 1 gives a great overview of the substitution models used in molecular evolution.

McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC
* Chapter 9 gives an excellent an intuitive introduction to MCMC.

Week 4: Jan 29 - Feb 2

Lemey, P., Rambaut, A., Drummond, A. J., & Suchard, M. A. (2009). Bayesian phylogeography finds its roots. PLoS Computational Biology, 5(9), e1000520.

Pybus, O. G., Suchard, M. A., Lemey, P., Bernardin, F. J., Rambaut, A., Crawford, F. W., … & Delwart, E. L. (2012). Unifying the spatial epidemiology and molecular evolution of emerging epidemics. PNAS, 109(37), 15066-15071.

Week 5: Feb 5 - 9

Rosenberg, N. A., & Nordborg, M. (2002). Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nature Reviews Genetics, 3(5), 380-390.
* Provides a great conceptual overview of coalescent theory

Drummond, A. J., Rambaut, A., Shapiro, B. E. T. H., & Pybus, O. G. (2005). Bayesian coalescent inference of past population dynamics from molecular sequences. Molecular Biology and Evolution, 22(5), 1185-1192.

De Maio, N., Wu, C. H., O’Reilly, K. M., & Wilson, D. (2015). New routes to phylogeography: a Bayesian structured coalescent approximation. PLoS Genetics, 11(8)
* Demonstrates how structured coalescent models can improve upon discrete trait phylogeographic analysis

Week 6: Feb 12 - 16

Jombart, T., Cori, A., Didelot, X., Cauchemez, S., Fraser, C., & Ferguson, N. (2014). Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Computational Biology, 10(1)

De Maio, N., Wu, C. H., & Wilson, D. J. (2016). SCOTTI: efficient reconstruction of transmission within outbreaks with the structured coalescent. PLoS Computational Biology, 12(9).

Wymant, C., Hall, M., Ratmann, O., Bonsall, D., Golubchik, T., de Cesare, M., … and The BEEHIVE Collaboration. (2018). PHYLOSCANNER: inferring transmission from within-and between-host pathogen genetic diversity. Molecular Biology and Evolution, 35(3), 719-733.

Week 7: Feb 19 - 23

Hein, J., Schierup, M., & Wiuf, C. (2004). Gene genealogies, variation and evolution: a primer in coalescent theory. Oxford University Press, USA.
** Chapter 5 presents an excellent overview of recombination and its effect on phylogenies.

Boni, M. F., Posada, D., & Feldman, M. W. (2007). An exact nonparametric method for inferring mosaic structure in sequence triplets. Genetics, 176(2), 1035-1047.

Week 8: Feb 26 - March 1

Shapiro, B. J. (2016). How clonal are bacteria over time?. Current Opinion in Microbiology, 31, 116-123. * Suggested reading based on in-class discussion of clonality.

Week 9: March 4 - 8

Stadler, T., & Bonhoeffer, S. (2013). Uncovering epidemiological dynamics in heterogeneous host populations using phylogenetic methods. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1614), 20120198.
* Describes the multi-type birth-death model for pathogen phylogenies.

Kühnert, D., Kouyos, R., Shirreff, G., Pečerska, J., Scherrer, A. U., Böni, J., … & Stadler, T. (2018). Quantifying the fitness cost of HIV-1 drug resistance mutations through phylodynamics. PLoS Pathogens, 14(2), e1006895.

Week 10: March 18 - 22

Keeling, M. J., & Rohani, P. (2011). Modeling infectious diseases in humans and animals. Princeton University Press.
** Chapters 2 and 3 give an amazing introduction to SIR-type models. Unfortunately not available online but well worth it if you can get your hands on a copy.

Ferrari, M. J., Grais, R. F., Bharti, N., Conlan, A. J., Bjørnstad, O. N., Wolfson, L. J., … & Grenfell, B. T. (2008). The dynamics of measles in sub-Saharan Africa. Nature, 451(7179), 679-684.

Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological models for invasion and persistence of pathogens. Annu. Rev. Phytopathol., 46, 385-418.
* Review exploring many different applications of epidemiological modeling to plant pathogens

Week 11: March 25 - 29

Keeling, M. J., & Rohani, P. (2011). Modeling infectious diseases in humans and animals. Princeton University Press.
** Chapter 6 gives a great overview of the types of stochastic models used in epidemiology.

Vaughan, T. G., & Drummond, A. J. (2013). A stochastic simulator of birth–death master equations with application to phylodynamics. Molecular Biology and Evolution, 30(6), 1480-1493.

Week 12: April 1 - 5

Volz, E. M., Pond, S. L. K., Ward, M. J., Brown, A. J. L., & Frost, S. D. (2009). Phylodynamics of infectious disease epidemics. Genetics, 183(4), 1421-1430.
* This paper first derived a coalescent model for SIR-type epidemiological models.

Rasmussen, D. A., Boni, M. F., & Koelle, K. (2014). Reconciling phylodynamics with epidemiology: the case of dengue virus in southern Vietnam. Molecular Biology and Evolution, 31(2), 258-271.

Volz, E. M., & Siveroni, I. (2018). Bayesian phylodynamic inference with complex models. PLoS Computational Biology, 14(11), e1006546.

Week 13: April 8 - 12

Didelot, X., & Parkhill, J. (2021). A scalable analytical approach from bacterial genomes to epidemiology. bioRxiv

Week 14: April 15 - 19

Łuksza, M., & Lässig, M. (2014). A predictive fitness model for influenza. Nature, 507(7490), 57-61.

Morris, D. H., Gostic, K. M., Pompei, S., Bedford, T., Łuksza, M., Neher, R. A., … & McCauley, J. W. (2018). Predictive modeling of influenza shows the promise of applied evolutionary biology. Trends in Microbiology, 26(2), 102-118.