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Use of Risk Assessment and Likelihood Estimation to Analyze Spatial Distribution Pattern of Respiratory Infection Cases
Author(s) -
SzeTo Gin Nam,
Chao Christopher Y. H.
Publication year - 2011
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2010.01525.x
Subject(s) - estimation , outbreak , infectious disease (medical specialty) , statistics , risk assessment , epidemiology , maximum likelihood , computer science , disease , mathematics , biology , medicine , virology , pathology , engineering , systems engineering , computer security
Obvious spatial infection patterns are often observed in cases associated with airborne transmissible diseases. Existing quantitative infection risk assessment models analyze the observed cases by assuming a homogeneous infectious particle concentration and ignore the spatial infection pattern, which may cause errors. This study aims at developing an approach to analyze spatial infection patterns associated with infectious respiratory diseases or other airborne transmissible diseases using infection risk assessment and likelihood estimation. Mathematical likelihood, based on binomial probability, was used to formulate the retrospective component with some additional mathematical treatments. Together with an infection risk assessment model that can address spatial heterogeneity, the method can be used to analyze the spatial infection pattern and retrospectively estimate the influencing parameters causing the cases, such as the infectious source strength of the pathogen. A Varicella outbreak was selected to demonstrate the use of the new approach. The infectious source strength estimated by the Wells‐Riley concept using the likelihood estimation was compared with the estimation using the existing method. It was found that the maximum likelihood estimation matches the epidemiological observation of the outbreak case much better than the estimation under the assumption of homogeneous infectious particle concentration. Influencing parameters retrospectively estimated using the new approach can be used as input parameters in quantitative infection risk assessment of the disease under other scenarios. The approach developed in this study can also serve as an epidemiological tool in outbreak investigation. Limitations and further developments are also discussed.