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Readings
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* 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.