Infectious disease pandemic planning and response: Incorporating decision analysis

About the Authors: Freya M. Shearer Affiliation: Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia Robert Moss Affiliation: Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia ORCID logo http://orcid.org/0000-0002-4568-2012 Jodie McVernon Affiliations Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia, Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Australia, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia ORCID logo http://orcid.org/0000-0001-9774-1961 Joshua V. Ross Affiliation: School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia James M. McCaw * E-mail: jamesm@unimelb.edu.au Affiliations Modelling and Simulation Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia, Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Australia, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia ORCID logo http://orcid.org/0000-0002-2452-3098 Introduction Planning is critical to mitigating the sudden and potentially catastrophic impact of an infectious disease pandemic on society, but it is far from straightforward [1]. In recent decades, other global infectious disease events, including the epidemics of severe acute respiratory syndrome (SARS, 2002–2003), the emergence of highly pathogenic avian influenza (HPAI) virus H5N1 (2003), and the west African Ebola virus disease epidemic (2013–2016), have also stimulated advances in pandemic preparedness and response capabilities [11, 18, 19]. Every year during the influenza season, modelers in many parts of the world, sometimes in collaboration with public health practitioners, make weekly forecasts of epidemic characteristics, such as peak size and timing [35–37]. Since 2013, the United States Centers for Disease Control and Prevention (CDC) have