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      Representative Contact Diaries for Modeling the Spread of Infectious Diseases in Taiwan

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      1 , 2 , 3 , 4
      PLoS ONE
      Public Library of Science

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          Abstract

          Recent studies of infectious diseases have attempted to construct more realistic parameters of interpersonal contact patterns from diary-approach surveys. To ensure that such diary-based contact patterns provide accurate baseline data for policy implementation in densely populated Taiwan, we collected contact diaries from a national sample, using 3-stage systematic probability sampling and rigorous in-person interviews. A representative sample of 1,943 contact diaries recorded a total of 24,265 wide-range, face-to-face interpersonal contacts during a 24-hour period. Nearly 70% of the contacts occurred outside of respondents' households. The most active age group was schoolchildren (ages 5–14), who averaged around 16–18 daily contacts, about 2–3 times as many as the least active age groups. We show how such parameters of contact patterns help modify a sophisticated national simulation system that has been used for years to model the spread of pandemic diseases in Taiwan. Based on such actual and representative data that enable researchers to infer findings to the whole population, our analyses aim to facilitate implementing more appropriate and effective strategies for controlling an emerging or pandemic disease infection.

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          Most cited references22

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          Mitigation strategies for pandemic influenza in the United States.

          Recent human deaths due to infection by highly pathogenic (H5N1) avian influenza A virus have raised the specter of a devastating pandemic like that of 1917-1918, should this avian virus evolve to become readily transmissible among humans. We introduce and use a large-scale stochastic simulation model to investigate the spread of a pandemic strain of influenza virus through the U.S. population of 281 million individuals for R(0) (the basic reproductive number) from 1.6 to 2.4. We model the impact that a variety of levels and combinations of influenza antiviral agents, vaccines, and modified social mobility (including school closure and travel restrictions) have on the timing and magnitude of this spread. Our simulations demonstrate that, in a highly mobile population, restricting travel after an outbreak is detected is likely to delay slightly the time course of the outbreak without impacting the eventual number ill. For R(0) < 1.9, our model suggests that the rapid production and distribution of vaccines, even if poorly matched to circulating strains, could significantly slow disease spread and limit the number ill to <10% of the population, particularly if children are preferentially vaccinated. Alternatively, the aggressive deployment of several million courses of influenza antiviral agents in a targeted prophylaxis strategy may contain a nascent outbreak with low R(0), provided adequate contact tracing and distribution capacities exist. For higher R(0), we predict that multiple strategies in combination (involving both social and medical interventions) will be required to achieve similar limits on illness rates.
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            Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

            The estimation of transmission parameters has been problematic for diseases that rely predominantly on transmission of pathogens from person to person through small infectious droplets. Age-specific transmission parameters determine how such respiratory agents will spread among different age groups in a human population. Estimating the values of these parameters is essential in planning an effective response to potentially devastating pandemics of smallpox or influenza and in designing control strategies for diseases such as measles or mumps. In this study, the authors estimated age-specific transmission parameters by augmenting infectious disease data with auxiliary data on self-reported numbers of conversational partners per person. They show that models that use transmission parameters based on these self-reported social contacts are better able to capture the observed patterns of infection of endemically circulating mumps, as well as observed patterns of spread of pandemic influenza. The estimated age-specific transmission parameters suggested that school-aged children and young adults will experience the highest incidence of infection and will contribute most to further spread of infections during the initial phase of an emerging respiratory-spread epidemic in a completely susceptible population. These findings have important implications for controlling future outbreaks of novel respiratory-spread infectious agents.
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              The impact of the SARS epidemic on the utilization of medical services: SARS and the fear of SARS.

              Using interrupted time-series analysis and National Health Insurance data between January 2000 and August 2003, this study assessed the impacts of the severe acute respiratory syndrome (SARS) epidemic on medical service utilization in Taiwan. At the peak of the SARS epidemic, significant reductions in ambulatory care (23.9%), inpatient care (35.2%), and dental care (16.7%) were observed. People's fears of SARS appear to have had strong impacts on access to care. Adverse health outcomes resulting from accessibility barriers posed by the fear of SARS should not be overlooked.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                3 October 2012
                : 7
                : 10
                : e45113
                Affiliations
                [1 ]Institute of Sociology, Academia Sinica, Taipei, Taiwan, Republic of China
                [2 ]Institute of Information Science, Academia Sinica, Taipei, Taiwan, Republic of China
                [3 ]Epidemic Intelligence Center, Centers for Disease Control, Taipei, Taiwan, Republic of China
                [4 ]Institute of Biomedical Informatics & Institute of Public Health, National Yang-Ming University, Taipei, Taiwan, Republic of China
                Umeå University, Sweden
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: YCF DWW JHC. Performed the experiments: YCF. Analyzed the data: YCF DWW. Contributed reagents/materials/analysis tools: YCF DWW. Wrote the paper: YCF DWW. Restructure paper contents and formats: JHC.

                Article
                PONE-D-12-20867
                10.1371/journal.pone.0045113
                3463600
                23056193
                e1e0b144-aef0-4588-a094-f7d2fd2ba168
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 July 2012
                : 13 August 2012
                Page count
                Pages: 7
                Funding
                This study was sponsored by the Department of Health, Taiwan, Republic of China (grant no. DOH99-DC-1004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer Science
                Computer Modeling
                Medicine
                Epidemiology
                Infectious Disease Epidemiology
                Social Epidemiology
                Survey Methods
                Infectious Diseases
                Viral Diseases
                Influenza
                Infectious Disease Modeling
                Public Health
                Behavioral and Social Aspects of Health
                Social and Behavioral Sciences
                Sociology

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                Uncategorized

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