title: Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example.
abstract: Emerging and re-emerging infections such as SARS (2003) and pandemic H1N1 (2009) have caused concern for public health researchers and policy makers due to the increased burden of these diseases on health care systems. This concern has prompted the use of mathematical models to evaluate strategies to control disease spread, making these models invaluable tools to identify optimal intervention strategies. A particularly important quantity in infectious disease epidemiology is the basic reproduction number, R0. Estimation of this quantity is crucial for effective control responses in the early phase of an epidemic. In our previous study, an approach for estimating the basic reproduction number in real time was developed. This approach uses case notification data and the structure of potential transmission contacts to accurately estimate R0 from the limited amount of information available at the early stage of an outbreak. Based on this approach, we extend the existing methodology; the most recent method features intra- and inter-age groups contact heterogeneity. Given the number of newly reported cases at the early stage of the outbreak, with parsimony assumptions on removal distribution and infectivity profile of the diseases, experiments to estimate real time R0 under different levels of intra- and inter-group contact heterogeneity using two age groups are presented. We show that the new method converges more quickly to the actual value of R0 than the previous one, in particular when there is high-level intra-group and inter-group contact heterogeneity. With the age specific contact patterns, number of newly reported cases, removal distribution, and information about the natural history of the 2009 pandemic influenza in Hong Kong, we also use the extended model to estimate R0 and age-specific R0.
authors.affiliation: School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, People's Republic of China.
authors.forename: Kin On
authors.initials: KO
authors.lastname: Kwok
authors.affiliation: Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada.
authors.forename: Bahman
authors.initials: B
authors.lastname: Davoudi
authors.affiliation: School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, People's Republic of China; MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College London, United Kingdom.
authors.forename: Steven
authors.initials: S
authors.lastname: Riley
authors.affiliation: Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada; School of Population & Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Babak
authors.initials: B
authors.lastname: Pourbohloul
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D050936:Basic Reproduction Number / Q000706:statistics & numerical data*; D002648:Child; D021821:Communicable Diseases, Emerging / Q000453:epidemiology*; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D015233:Models, Statistical; D058873:Pandemics / Q000706:statistics & numerical data*; D013997:Time Factors; D055815:Young Adult