Journal
PLOS ONE
Volume 10, Issue 9, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0137959
Keywords
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Categories
Funding
- Canadian Institutes of Health Research [PTL-97126]
- Michael Smith Foundation for Health Research [CI-SSH-00931(06-1)]
- British Columbia Ministry of Health (Pandemic Preparedness Modeling Project)
- Medical Research Council (UK) [MR/J008761/1]
- Wellcome Trust [093488/Z/10/Z]
- National Institute for Health Research (UK, Health Protection Research Unit)
- Fogarty International Centre (FluScape) [R01 TW008246-01]
- Research Fund for the Control of Infectious Disease, Food and Health Bureau, Government of the Hong Kong SARResearch Grants Council, Hong Kong [110642, PHE 21]
- Research Grants Council, Hong Kong [776810]
- Medical Research Council [MR/K010174/1, MR/J008761/1, MR/K010174/1B] Funding Source: researchfish
- National Institute for Health Research [HPRU-2012-10080] Funding Source: researchfish
- MRC [MR/J008761/1, MR/K010174/1] Funding Source: UKRI
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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, R-0. 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 R-0 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 R-0 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 R-0 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
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