Archive of discussed papers

Listed below are the papers and books discussed in previous meetings, with the most recently discussed listed first.

• P. Trapman et al., Inferring R0 emerging empidemics - the effect of common population structure is small. ArXiv (2016).
• A.J. Kucharski et al., Transmission dynamics of Zika virus in island populations: a modelling analysis of the 2013-14 French Polynesia outbreak, bioRxiv (2016).
• S. Yang et al., Accurate estimation of influenza epidemics using Google search data via ARGO, PNAS (2015).
• Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases (Editors: Piero Manfredi, Alberto D'Onofrio).
• M.J. Keeling, The effects of local spatial structure on epidemiological invasions, Proceedings B 266(1421): 859-867 (1999).
  
• K.T.D. Eames and M.J. Keeling, Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases, PNAS 99(20): 13330–13335 (2002).
• A.L. Lloyd, Estimating variability in models for recurrent epidemics: assessing the use of moment closure techniques, Theoretical Population Biology 65: 49–65 (2004).
• M.J. Keeling, Multiplicative moments and measures of persistence in Ecology, Journal Theoretical Biology 205: 269-281 (2000).
• M.J. Keeling, J.V. Ross, Optimal prophylactic vaccination in segregated populations: When can we improve on the equalising strategy? 11: 7-13 (2015).
  
• A. J. Adler et al. Incidence and risk factors for influenza-like-illness in the UK: online surveillance using Flusurvey, BMC Infectious Diseases 14:232 (2014). 
• J. M. Fonville et al., Antibody landscapes after influenza virus infection or vaccination, Science 346(6212): 996-1000 (2014). 
• C. Tomasetti and B. Vogelstein, Variation in cancer risk among tissues can be explained by the number of stem cell divisions, Science 347(6217): 78-81 (2015). 
• P. C. Johnson et al., Multiple-Challenge Study of Host Susceptibility to Norwalk Gastroenteritis in US Adults, Journal of Infectious Diseases 161(1): 18-21 (1990). 
• K. Shea et al., Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology, PLoS Biology 12(10): e1001970 (2014). 
• J. M. Drake, Limits to forecasting precision for outbreaks of directly transmitted diseases, PLoS Medicine, 3(1):e3 (2006). 
• T. House, Epidemiological dynamics of Ebola outbreaks, eLife 3: e03908 (2014). 
  
• B.Cazelles et al., Wavelet analysis of ecological time series, Oecologia 156(2): 287-304 (2008). 
• S. Gandon, M. E. Hochberg, R. D. Holt & T. Day, What limits the evolutionary emergence of pathogens? Phil. Trans. R. Soc. B 368(1610): 20120086 (2013). 
• T.J. McKinley, J.V. Ross, R. Deardon & A.R. Cook, Simulation-based Bayesian inference for epidemic models. Computational Statistics & Data Analysis 71: 434-447 (2014).
• K.M. Gamado, G. Streftaris & S. Zachary, Modelling under-reporting in epidemics. Journal of Mathematical Biology 69(3): 737-765 (2014).
• P. Rohani, X. Zhong & A. A. King, Contact Network Structure Explains the Changing Epidemiology of Pertussis, Science 330(6006): 982-985 (2010).
• N. Ringa & C.T. Bauch, Dynamics and control of foot-and-mouth disease in endemic countries: A pair approximation model, Journal of Theoretical Biology 357: 150-159 (2014).
• T. W. Berngruber, R.Froissart, M. Choisy & S. Gandon,Evolution of Virulence in Emerging Epidemics, PLOS Pathogens 9(3): e1003209 (2013).
• A new method for estimating the effort required to control an infectious disease, M. G. Roberts and J. A. P. Heesterbeek
• J. Alexsen, R. Yaari, B.T. Grenfell & L. Stone, Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers. PNAS 111(26): 9538-9542 (2014).
• M. Girolami, Bayesian inference for differential equations. Theoretical Computer Science 408: 4–16 (2008).
• J.M. Read & M.J. Keeling, Disease evolution across a range of spatio-temporal scales, Theoretical Population Biology 70(2): 201-213 (2006).
• P.J. Neal & G.O. Roberts, Statistical inference and model selection for the 1861 Hagelloch measles epidemic, Biostatistics 5(2): 249-261 (2004).
• J. Leander, T. Lundh & M. Jirstrand, Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements, Mathematical Biosciences 251: 54-62 (2014).
• B.T. Grenfell, O.N. Bjornstad & J. Kappey, Travelling waves and spatial hierarchies in measles epidemics, Nature 414: 716-723 (2001).
• D.M. Walker et al., Parameter inference in small world network disease models with approximate Bayesian Computational methods, Physica A 389(3): 540–548 (2010).