A random effects model for diseases with heterogeneous rates of infection |
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| Institution: | 1. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK;1. School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, People''s Republic of China;2. Univ. Bordeaux, France CNRS, IMB, UMR 5251, F-33400, Talence, France;3. Département Tronc Commun, École Polytechnique de Thiés, Senegal;4. Mathematics Department, Vanderbilt University, Nashville, TN, USA;1. University of Transport Technology, Hanoi 100000, Viet Nam;2. Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran;3. Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran;4. School of Resources and Safety Engineering, Central South University, Changsha, 410083 Hunan Province, People''s Republic of China;5. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;6. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;7. Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 18 Hoang Quoc Viet, Viet Nam;8. Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam;9. Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, Viet Nam;10. Department of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam;11. Institute of Marine Geology and Geophysics, VAST, Hanoi, Viet Nam;1. The Interdisplinary Research Center for Mathematics and Life Sciences, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China;2. Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada;3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China;4. School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710119, People’s Republic of China;5. Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Ontario, M3J 1P3, Canada |
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| Abstract: | One form of data collected in the study of infectious diseases is on the transmission of a disease within households. We consider a model which allows the rate of disease transmission to vary between households. A Bayesian hierarchical approach to fitting the model is proposed and is implemented by the Metropolis–Hastings algorithm, a standard Markov chain Monte Carlo (MCMC) method. Results are presented for both simulated epidemic chain data and the Providence measles data, illustrating the potential that MCMC methods have to dealing with heterogeneity in infectious disease transmission. |
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