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Ecological inference for infectious disease data, with application to vaccination strategies
Author(s) -
Fisher Leigh H.,
Wakefield Jon
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8390
Subject(s) - inference , infectious disease (medical specialty) , measles , computer science , outbreak , vaccination , ecology , econometrics , disease , biology , medicine , mathematics , artificial intelligence , virology , pathology
Disease surveillance systems provide a rich source of data regarding infectious diseases, aggregated across geographical regions. The analysis of such ecological data is fraught with difficulties, and, unless care and suitable data summaries are available, will lead to biased estimates of individual‐level parameters. We consider using surveillance data to study the impacts of vaccination. To catalog the problems of ecological inference, we start with an individual‐level model, which contains familiar parameters, and derive an ecologically consistent model for infectious diseases in partially vaccinated populations. We compare with other popular model classes and highlight deficiencies. We explore the properties of the new model through simulation and demonstrate that, under standard assumptions, the ecological model provides less biased estimates. We then fit the new model to data collected on measles outbreaks in Germany from 2005‐2007.