Literature DB >> 2218197

Modelling forces of infection for measles, mumps and rubella.

C P Farrington1.   

Abstract

Serological data from 8870 persons collected prior to the introduction of measles, mumps and rubella (MMR) vaccine in the UK are used to describe the rate at which individuals acquire infection by these diseases at different ages. A parsimonious model is developed and fitted under various interpretations of the data, particularly concerning the probability of lifelong susceptibility to infection. It is shown that, while the force of infection curves are relatively robust in their general features, they exhibit considerable sensitivity in matters of important detail. This is true in particular of the values taken by the force of infection in older age groups. As a result, estimates of the average age at infection are highly sensitive to these interpretations. This in turn may limit the accuracy of predictions from mathematical models based on these parameters, in particular regarding the level of immunization required for eradication of disease.

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Year:  1990        PMID: 2218197     DOI: 10.1002/sim.4780090811

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  33 in total

1.  Age-structured effects and disease interference in childhood infections.

Authors:  Yunxin Huang; Pejman Rohani
Journal:  Proc Biol Sci       Date:  2006-05-22       Impact factor: 5.349

Review 2.  Rubella in the United Kingdom.

Authors:  E Miller
Journal:  Epidemiol Infect       Date:  1991-08       Impact factor: 2.451

3.  Force of infection of Helicobacter pylori in Mexico: evidence from a national survey using a hierarchical Bayesian model.

Authors:  F Alarid-Escudero; E A Enns; R F MacLehose; J Parsonnet; J Torres; K M Kuntz
Journal:  Epidemiol Infect       Date:  2018-04-16       Impact factor: 2.451

4.  Incidence of cytomegalovirus infection in Shanghai, China.

Authors:  Feng-Qin Fang; Qi-Shi Fan; Zhi-Jun Yang; Yi-Bing Peng; Li Zhang; Ke-Zi Mao; Yue Zhang; Yu-Hua Ji
Journal:  Clin Vaccine Immunol       Date:  2009-09-23

5.  Interpretation of serological surveillance data for measles using mathematical models: implications for vaccine strategy.

Authors:  N J Gay; L M Hesketh; P Morgan-Capner; E Miller
Journal:  Epidemiol Infect       Date:  1995-08       Impact factor: 2.451

6.  Predicting the impact of measles vaccination in England and Wales: model validation and analysis of policy options.

Authors:  H R Babad; D J Nokes; N J Gay; E Miller; P Morgan-Capner; R M Anderson
Journal:  Epidemiol Infect       Date:  1995-04       Impact factor: 2.451

7.  Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.

Authors:  Robin N Thompson; Ellen Brooks-Pollock
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-06-24       Impact factor: 6.237

8.  A mathematical model to study the effect of hepatitis B virus vaccine and antivirus treatment among the Canadian Inuit population.

Authors:  C O'Leary; Z Hong; F Zhang; M Dawood; G Smart; K Kaita; J Wu
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2009-11-12       Impact factor: 3.267

9.  Parvovirus B19 infection in five European countries: seroepidemiology, force of infection and maternal risk of infection.

Authors:  J Mossong; N Hens; V Friederichs; I Davidkin; M Broman; B Litwinska; J Siennicka; A Trzcinska; P VAN Damme; P Beutels; A Vyse; Z Shkedy; M Aerts; M Massari; G Gabutti
Journal:  Epidemiol Infect       Date:  2007-10-24       Impact factor: 2.451

10.  Rubella seroepidemiology in a non-immunized population of São Paulo State, Brazil.

Authors:  R S De Azevedo Neto; A S Silveira; D J Nokes; H M Yang; S D Passos; M R Cardoso; E Massad
Journal:  Epidemiol Infect       Date:  1994-08       Impact factor: 2.451

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