tetano
Editor, Senior Moderator
Epidemics. 2019 Aug 26:100356. doi: 10.1016/j.epidem.2019.100356. [Epub ahead of print] [h=1]Improved inference of time-varying reproduction numbers during infectious disease outbreaks.[/h]
Thompson RN[SUP]1[/SUP], Stockwin JE[SUP]2[/SUP], van Gaalen RD[SUP]3[/SUP], Polonsky JA[SUP]4[/SUP], Kamvar ZN[SUP]5[/SUP], Demarsh PA[SUP]6[/SUP], Dahlqwist E[SUP]7[/SUP], Li S[SUP]7[/SUP], Miguel E[SUP]8[/SUP], Jombart T[SUP]9[/SUP], Lessler J[SUP]10[/SUP], Cauchemez S[SUP]11[/SUP], Cori A[SUP]5[/SUP].
[h=3]Author information[/h] 1 Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK; Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK; Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK. Electronic address: robin.thompson@chch.ox.ac.uk. 2 Lady Margaret Hall, University of Oxford, Norham Gardens, Oxford OX2 6QA, UK. 3 Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, the Netherlands. 4 World Health Organization, Avenue Appia, Geneva 1202, Switzerland; Faculty of Medicine, University of Geneva, 1 Rue Michel-Servet, Geneva 1211, Switzerland. 5 MRC Centre for Global Infectious Disease Analysis, Imperial College London, Faculty of Medicine, London W2 1PG, UK. 6 The Surveillance Lab, McGill University, 1140 Pine Avenue West, Montreal H3A 1A3, Canada; Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, 130 Colonnade Road, Ottawa, Ontario, K1A 0K9, Canada. 7 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden. 8 MIVEGEC, IRD, University of Montpellier, CNRS, Montpellier, France. 9 MRC Centre for Global Infectious Disease Analysis, Imperial College London, Faculty of Medicine, London W2 1PG, UK; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK. 10 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. 11 Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris 75015, France.
[h=3]Abstract[/h] Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.
Copyright ? 2019 The Authors. Published by Elsevier B.V. All rights reserved.
[h=4]KEYWORDS:[/h] Disease control; Infectious disease epidemiology; Mathematical modelling; Parameter inference; Reproduction number; Serial interval
PMID: 31624039 DOI: 10.1016/j.epidem.2019.100356
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Thompson RN[SUP]1[/SUP], Stockwin JE[SUP]2[/SUP], van Gaalen RD[SUP]3[/SUP], Polonsky JA[SUP]4[/SUP], Kamvar ZN[SUP]5[/SUP], Demarsh PA[SUP]6[/SUP], Dahlqwist E[SUP]7[/SUP], Li S[SUP]7[/SUP], Miguel E[SUP]8[/SUP], Jombart T[SUP]9[/SUP], Lessler J[SUP]10[/SUP], Cauchemez S[SUP]11[/SUP], Cori A[SUP]5[/SUP].
[h=3]Author information[/h] 1 Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK; Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK; Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK. Electronic address: robin.thompson@chch.ox.ac.uk. 2 Lady Margaret Hall, University of Oxford, Norham Gardens, Oxford OX2 6QA, UK. 3 Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, the Netherlands. 4 World Health Organization, Avenue Appia, Geneva 1202, Switzerland; Faculty of Medicine, University of Geneva, 1 Rue Michel-Servet, Geneva 1211, Switzerland. 5 MRC Centre for Global Infectious Disease Analysis, Imperial College London, Faculty of Medicine, London W2 1PG, UK. 6 The Surveillance Lab, McGill University, 1140 Pine Avenue West, Montreal H3A 1A3, Canada; Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, 130 Colonnade Road, Ottawa, Ontario, K1A 0K9, Canada. 7 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden. 8 MIVEGEC, IRD, University of Montpellier, CNRS, Montpellier, France. 9 MRC Centre for Global Infectious Disease Analysis, Imperial College London, Faculty of Medicine, London W2 1PG, UK; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK. 10 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. 11 Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris 75015, France.
[h=3]Abstract[/h] Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.
Copyright ? 2019 The Authors. Published by Elsevier B.V. All rights reserved.
[h=4]KEYWORDS:[/h] Disease control; Infectious disease epidemiology; Mathematical modelling; Parameter inference; Reproduction number; Serial interval
PMID: 31624039 DOI: 10.1016/j.epidem.2019.100356
Free full text