Publications

(Google Scholar, ORCiD)

Full List

Journal papers + preprints

  1. Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies.
    Aaron J. Stern, Leo Speidel, Noah A. Zaitlen, Rasmus Nielsen.
    Preprint: bioRxiv:2020.05.07.083402
    • This paper introduces a full-likelihood method to quantify polygenic adaptation. This method can disentangle selection acting on correlated traits.
    Code: https://github.com/35ajstern/palm

  2. A method for genome-wide genealogy estimation for thousands of samples.
    Leo Speidel, Marie Forest, Sinan Shi, Simon R. Myers.
    Nature Genetics 51, 1321-1329 (2019)
    Preprint: bioRxiv:550558
    • This paper describes the Relate method for estimating genealogies for thousands of samples and its application to 2478 modern humans.
    Code: https://myersgroup.github.io/relate/
    Data: https://zenodo.org/record/3234689
    Featured in:
  3. Topological data analysis of continuum percolation with disks.
    Leo Speidel, Heather A. Harrington, S. Jonathan Chapman, Mason A. Porter.
    Physical Review E 98, 012318 (2018)
    Preprint: arXiv:1804.07733
    • We studied percolation of disks which are dropped at random onto a plane leading to clusters of overlapping disks, the size of which can undergo sudden phase transitions. Here, we characterize topological properties of such clusters.
    Code: https://github.com/leospeidel/TDA_of_continuum_percolation_with_disks

  4. Asynchronous rumor spreading on random graphs.
    Konstantinos Panagiotou, Leo Speidel (alphabetical order).
    Algorithmica 78, 968-989 (2017)
    Preprint: arXiv:1608.01766
    • For a simple protocol for disseminating information in a network, we derive tight bounds on the time until all nodes are informed, which we show to be robust to the density of connections in the network.

  5. Temporal interactions facilitate endemicity in the susceptible-infected-susceptible epidemic model.
    Leo Speidel, Konstantin Klemm, Victor M. Eguiluz, Naoki Masuda.
    New Journal of Physics 18, 073013 (2016) [open access]
    Preprint: arXiv:1602.00859
    • The structure of networks describing human interactions directly impact how easily epidemics can spread. Many networks additionally change through time and we show that rapidly changing networks are always more susceptible to an epidemic compared to the corresponding static ones.

  6. Community detection in directed acyclic graphs.
    Leo Speidel*, Taro Takaguchi*, Naoki Masuda (*contributed equally).
    European Physical Journal B 88, 203 (2015) [open access]
    Preprint: arXiv:1503.05641
    • Empirical networks commonly exhibit communities, which are densely connected submodules. Detecting such communities can substantially enhance the understanding of a network. We extend an existing approach to a subclass of networks that have directed links and no cycles, including e.g., citation networks and genealogies.
    Code: https://github.com/leospeidel/dag_community_paper
    Featured in:
  7. Steady state and mean recurrence time for random walks on stochastic temporal networks.
    Leo Speidel, Renaud Lambiotte, Kazuyuki Aihara, Naoki Masuda.
    Physical Review E 91, 012806 (2015) [open access]
    Preprint: arXiv:1407.4582
    • We characterise a random walk on a temporally changing network. This random walk is a simplified model for e.g., an epidemic spreading through physical contacts among agents.

Book chapters/thesis

  1. Genealogy estimation for thousands of samples.
    Leo Speidel.
    DPhil thesis, University of Oxford, 2019.

  2. Epidemic threshold in temporally-switching networks.
    Leo Speidel, Konstantin Klemm, Victor M. Eguiluz, Naoki Masuda.
    Temporal Network Epidemiology (Springer, Singapore, 2017), pp. 161-177.

Misc

  1. What Our DNA Can Tell Us About the History of Humans.
    Leo Speidel, Clare Bycroft.
    Front. Young Minds. 8:106 (2020) [open access]
    • We wrote this article for young scientists (aged 8-15); it describes how we can use maths, stats, and computers to find out about our genetic history.