Statistical modelling of measles and influenza outbreaks
Identifieur interne : 001F00 ( Main/Exploration ); précédent : 001E99; suivant : 001F01Statistical modelling of measles and influenza outbreaks
Auteurs : Ad Cliff [Royaume-Uni] ; P. HaggettSource :
- Statistical Methods in Medical Research [ 0962-2802 ] ; 1993-03.
English descriptors
- Teeft :
- Absolute deviation, Accounting identities, Adequate contact, American journal, Asian influenza, Baroyan, Basic model, Cambridge university press, Chain binomial, Chain structure, Cliff, Cliff etal, Conventional view, Deterministic form, Different families, Dynamic analysis, Early part, Effective contact, Elveback model, Epidemic, Epidemic cycles, Epidemic peak, Epidemic peaks, Epidemic recurrences, Epidemic size, Epidemiological, Epidemiological modelling, Epidemiological research laboratory, Epidemiological research unit, Epidemiology, Etal, Exponential family, General practitioners, Graphs show, Haggett, Herd immunity, High probability, Historical geography, Hong kong, Icelandic data, Independent events, Infection, Infection rate, Infectious disease, Infectious diseases, Infectious period, Infectiousness, Infective, Infective population, Infectives, Influenza, Influenza data, Influenza epidemics, Influenza morbidity, Influenza outbreaks, Influenza peaks, Influenza spread, Influenza virus, Influenzal pneumonia, Large numbers, Latent period, Linear predictor, Link function, Main drawback, Mathematical biosciences, Mathematical statistics, Mathematical theory, Measles, Measles cases, Measles contacts, Measles epidemics, Measles epidemiology, Measles outbreaks, Measles virus, Medical district, Modelling, Morbidity, Open circles, Other areas, Outbreak, Parameter, Past levels, Population size, Process models, Random shocks, Reasonable projections, Recurrence, Recurrent epidemics, Reference region, Royal college, Schematic model, Serial interval, Shortest chain, Simple form, Simulation, Small epidemic, Soviet union, Spatial structure, Spatial systems, State models, Statistical modelling, Susceptible, Susceptible population, Susceptible populations, Susceptibles, Time period, Time periods, Time series, Time series models, Total community, Total number, Total population, Vaccination, Viral diseases, Viral infections, Virus, Virus isolations, World health organization.
Abstract
This paper reviews the application of statistical models to outbreaks of two common respiratory viral diseases, measles and influenza. For each disease, we look first at its epidemiological characteristics and assess the extent to which these either aid or hinder modelling. We then turn to the models that have been developed to simulate geographical spread. For measles, a distinction is drawn between process-based and time series models; for influenza, it is the scale of the communities (from small groups to global populations) which primarily determines modelling style. Applications are provided from work by the authors, largely using Icelandic data. Finally we consider the forecasting potential of the models described.
Url:
DOI: 10.1177/096228029300200104
Affiliations:
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Haggett</name><affiliation><wicri:noCountry code="subField">Bristol</wicri:noCountry></affiliation></author></analytic><monogr></monogr><series><title level="j">Statistical Methods in Medical Research</title><idno type="ISSN">0962-2802</idno><idno type="eISSN">1477-0334</idno><imprint><publisher>Sage Publications</publisher><pubPlace>Sage CA: Thousand Oaks, CA</pubPlace><date type="published" when="1993-03">1993-03</date><biblScope unit="volume">2</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="43">43</biblScope><biblScope unit="page" to="73">73</biblScope></imprint><idno type="ISSN">0962-2802</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0962-2802</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Absolute deviation</term><term>Accounting identities</term><term>Adequate contact</term><term>American journal</term><term>Asian influenza</term><term>Baroyan</term><term>Basic model</term><term>Cambridge university press</term><term>Chain binomial</term><term>Chain structure</term><term>Cliff</term><term>Cliff etal</term><term>Conventional view</term><term>Deterministic form</term><term>Different families</term><term>Dynamic analysis</term><term>Early part</term><term>Effective contact</term><term>Elveback model</term><term>Epidemic</term><term>Epidemic cycles</term><term>Epidemic peak</term><term>Epidemic peaks</term><term>Epidemic recurrences</term><term>Epidemic size</term><term>Epidemiological</term><term>Epidemiological modelling</term><term>Epidemiological research laboratory</term><term>Epidemiological research unit</term><term>Epidemiology</term><term>Etal</term><term>Exponential family</term><term>General practitioners</term><term>Graphs show</term><term>Haggett</term><term>Herd immunity</term><term>High probability</term><term>Historical geography</term><term>Hong kong</term><term>Icelandic data</term><term>Independent events</term><term>Infection</term><term>Infection rate</term><term>Infectious disease</term><term>Infectious diseases</term><term>Infectious period</term><term>Infectiousness</term><term>Infective</term><term>Infective population</term><term>Infectives</term><term>Influenza</term><term>Influenza data</term><term>Influenza epidemics</term><term>Influenza morbidity</term><term>Influenza outbreaks</term><term>Influenza peaks</term><term>Influenza spread</term><term>Influenza virus</term><term>Influenzal pneumonia</term><term>Large numbers</term><term>Latent period</term><term>Linear predictor</term><term>Link function</term><term>Main drawback</term><term>Mathematical biosciences</term><term>Mathematical statistics</term><term>Mathematical theory</term><term>Measles</term><term>Measles cases</term><term>Measles contacts</term><term>Measles epidemics</term><term>Measles epidemiology</term><term>Measles outbreaks</term><term>Measles virus</term><term>Medical district</term><term>Modelling</term><term>Morbidity</term><term>Open circles</term><term>Other areas</term><term>Outbreak</term><term>Parameter</term><term>Past levels</term><term>Population size</term><term>Process models</term><term>Random shocks</term><term>Reasonable projections</term><term>Recurrence</term><term>Recurrent epidemics</term><term>Reference region</term><term>Royal college</term><term>Schematic model</term><term>Serial interval</term><term>Shortest chain</term><term>Simple form</term><term>Simulation</term><term>Small epidemic</term><term>Soviet union</term><term>Spatial structure</term><term>Spatial systems</term><term>State models</term><term>Statistical modelling</term><term>Susceptible</term><term>Susceptible population</term><term>Susceptible populations</term><term>Susceptibles</term><term>Time period</term><term>Time periods</term><term>Time series</term><term>Time series models</term><term>Total community</term><term>Total number</term><term>Total population</term><term>Vaccination</term><term>Viral diseases</term><term>Viral infections</term><term>Virus</term><term>Virus isolations</term><term>World health organization</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper reviews the application of statistical models to outbreaks of two common respiratory viral diseases, measles and influenza. For each disease, we look first at its epidemiological characteristics and assess the extent to which these either aid or hinder modelling. We then turn to the models that have been developed to simulate geographical spread. For measles, a distinction is drawn between process-based and time series models; for influenza, it is the scale of the communities (from small groups to global populations) which primarily determines modelling style. Applications are provided from work by the authors, largely using Icelandic data. Finally we consider the forecasting potential of the models described.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Angleterre de l'Est</li></region><settlement><li>Cambridge</li></settlement><orgName><li>Université de Cambridge</li></orgName></list><tree><noCountry><name sortKey="Haggett, P" sort="Haggett, P" uniqKey="Haggett P" first="P." last="Haggett">P. Haggett</name></noCountry><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Cliff, Ad" sort="Cliff, Ad" uniqKey="Cliff A" first="Ad" last="Cliff">Ad Cliff</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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