S
Sara L. Loo
Researcher at University of Sydney
Publications - 4
Citations - 30
Sara L. Loo is an academic researcher from University of Sydney. The author has contributed to research in topics: Computer science & Mating. The author has an hindex of 2, co-authored 2 publications receiving 23 citations.
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Journal ArticleDOI
Evolution of male strategies with sex-ratio-dependent pay-offs: connecting pair bonds with grandmothering.
TL;DR: Using a difference equation model, the relative pay-offs for three competing male strategies (dependant care, multiple mating, mate guarding) in response to changing adult sex ratios are explored.
Journal ArticleDOI
Further Mathematical Modelling of Mating Sex Ratios & Male Strategies with Special Relevance to Human Life History.
TL;DR: An ordinary differential equation model of mutually exclusive strategies (dependant care, multiple mating, and mate guarding), calculate steady-state frequencies and perform bifurcation analysis on parameters of care and guarding efficiency is presented.
Journal ArticleDOI
Gut mutualists can persist in host populations despite low fidelity of vertical transmission
TL;DR: A mathematical model is developed to identify the conditions under which the mutualist can persist in a population where vertical transmission is imperfect and shows that several factors compensate for imperfect vertical transmission, namely, a selective advantage to the host conferred by the Mutualist, horizontal transmission of the mutualists through an environmental reservoir and transmission of a cultural practice that promotes microbial transmission.
Posted ContentDOI
Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub
Emily Howerton,Lucie Contamin,Luke C. Mullany,Michelle Qin,Nicholas G. Reich,Samantha Bents,Rebecca K. Borchering,Sung-mok Jung,Sara L. Loo,Claire Smith,John Levander,Jessica Kerr,J. Espino,Willem G. van Panhuis,H. William Hochheiser,Marta Galanti,Teresa K. Yamana,Sen Pei,Jeffrey Shaman,Kaitlin Rainwater-Lovett,Matthew Kinsey,K. Tallaksen,Shelby Wilson,Lauren Shin,Joseph C. Lemaitre,Joshua Kaminsky,Juan Dent Hulse,Elizabeth C. Lee,Alison K. Hill,D. Karlen,Matteo Chinazzi,Jessica T. Davis,Kunpeng Mu,Xinyue Xiong,Ana Pastore y Piontti,Alessandro Vespignani,Erik Rosenstrom,Julie S. Ivy,Maria E. Mayorga,Julie Swann,Guido España,Sean M. Cavany,Sean M. Moore,Alex Perkins,Thomas J. Hladish,Alexander N Pillai,Kok Ben Toh,Ira M. Longini,Shih Ching Chen,Rajib Paul,Daniel Janies,Jean-Claude Thill,Anass Bouchnita,Kaiming Bi,Michael Lachmann,Spencer J. Fox,Lauren Ancel Meyers,A. Srivastava,Przemyslaw J. Porebski,Srinivasan Venkatramanan,Aniruddha Adiga,Bryan Lewis,Brian D. Klahn,J.L. Outten,Benjamin Hurt,Jiangzhuo Chen,Henning S. Mortveit,Amanda Wilson,Madhav V. Marathe,Stefan Hoops,Parantapa Bhattacharya,Dustin Machi,Betsy L. Cadwell,Jessica M. Healy,Rachel B. Slayton,Michael A. Johansson,Matthew Biggerstaff,Shaun A. Truelove,Michael C. Runge,Katriona Shea,Cécile Viboud,Justin Lessler +81 more
TL;DR: The COVID-19 Scenario Modeling Hub (SMH) as discussed by the authors has been used to forecast epidemics more than a few weeks into the future, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures.