A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases
Identifieur interne : 001F45 ( Main/Exploration ); précédent : 001F44; suivant : 001F46A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases
Auteurs : Yang Yang [États-Unis] ; Ira M. Longini Jr. [États-Unis] ; M. Elizabeth Halloran [États-Unis] ; Valerie Obenchain [États-Unis]Source :
- Biometrics [ 0006-341X ] ; 2012-12.
English descriptors
- Teeft :
- Algorithm, American journal, Asymptomatic, Asymptomatic cases, Asymptomatic infection, Asymptomatic infections, Baseline susceptibility, Binary indicators, Biometrics, Bootstrap, Bootstrap approach, Carlo, Casella, Casual contacts, Complete data, Computational, Computational burden, Contact groups, Convergence, Covariance matrix, Covariate, Daily probability, Data analysis, Data augmentation, Epidemic, Epidemic curves, Estimation, Exact approach, Exact boot, Exposure history, Halloran, High dimension, Household sizes, Hybird algorithm, Hybrid, Hybrid algorithm, Illness onsets, Immune status, Immunity level, Importance samples, Importance sampling, Importance weights, Index case, Infection, Infection outcomes, Infection status, Infection time, Infection times, Infectious disease, Infectious disease data, Infectious diseases, Infectious period, Infectiousness, Infectiousness level, Infectiousness onset, Infectiousness onset days, Infectiousness onset times, Information matrix, Initial estimates, Initial value, Iteration, Iterations time, Latent period, Latter approach, Levine, Longini, Markov chain monte carlo methods, Mcem, Mcem algorithm, Mcmc, Mcmc samples, Mcmc sampling time, Minimum value, Monte carlo, National institute, Natural history, Overall time, Parameter, Parameter estimates, Parameter expansion, Pathogenicity, Point estimation, Possible realizations, Preseason, Preseason immunity, Relative error, Relative infectivity, Sample size, Sampling range, Sampling stage, Seattle families, Simulation, Simulation studies, Simulation study, Special case, Standard deviation, Study households, Study population, Susceptible individuals, Symptom onset, Symptom onset time, Symptom outcomes, Symptomatic cases, Symptomatic infections, Symptomatic infective household member, Titer, Titer level, Titer levels, Titer values, Tolerance rate, Total number, Traditional mcem, Traditional mcem algorithm, Transmissibility, Transmission probabilities, Unknown source, Unobserved, Unobserved infection times, Variance, Variance calculation, Variance estimation, Virus infections, Whole population.
Abstract
Summary In epidemics of infectious diseases such as influenza, an individual may have one of four possible final states: prior immune, escaped from infection, infected with symptoms, and infected asymptomatically. The exact state is often not observed. In addition, the unobserved transmission times of asymptomatic infections further complicate analysis. Under the assumption of missing at random, data‐augmentation techniques can be used to integrate out such uncertainties. We adapt an importance‐sampling‐based Monte Carlo Expectation‐Maximization (MCEM) algorithm to the setting of an infectious disease transmitted in close contact groups. Assuming the independence between close contact groups, we propose a hybrid EM‐MCEM algorithm that applies the MCEM or the traditional EM algorithms to each close contact group depending on the dimension of missing data in that group, and discuss the variance estimation for this practice. In addition, we propose a bootstrap approach to assess the total Monte Carlo error and factor that error into the variance estimation. The proposed methods are evaluated using simulation studies. We use the hybrid EM‐MCEM algorithm to analyze two influenza epidemics in the late 1970s to assess the effects of age and preseason antibody levels on the transmissibility and pathogenicity of the viruses.
Url:
DOI: 10.1111/j.1541-0420.2012.01757.x
Affiliations:
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population</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Summary In epidemics of infectious diseases such as influenza, an individual may have one of four possible final states: prior immune, escaped from infection, infected with symptoms, and infected asymptomatically. The exact state is often not observed. In addition, the unobserved transmission times of asymptomatic infections further complicate analysis. Under the assumption of missing at random, data‐augmentation techniques can be used to integrate out such uncertainties. We adapt an importance‐sampling‐based Monte Carlo Expectation‐Maximization (MCEM) algorithm to the setting of an infectious disease transmitted in close contact groups. Assuming the independence between close contact groups, we propose a hybrid EM‐MCEM algorithm that applies the MCEM or the traditional EM algorithms to each close contact group depending on the dimension of missing data in that group, and discuss the variance estimation for this practice. In addition, we propose a bootstrap approach to assess the total Monte Carlo error and factor that error into the variance estimation. The proposed methods are evaluated using simulation studies. We use the hybrid EM‐MCEM algorithm to analyze two influenza epidemics in the late 1970s to assess the effects of age and preseason antibody levels on the transmissibility and pathogenicity of the viruses.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li></region></list><tree><country name="États-Unis"><region name="Floride"><name sortKey="Yang, Yang" sort="Yang, Yang" uniqKey="Yang Y" first="Yang" last="Yang">Yang Yang</name></region><name sortKey="Halloran, M Elizabeth" sort="Halloran, M Elizabeth" uniqKey="Halloran M" first="M. Elizabeth" last="Halloran">M. Elizabeth Halloran</name><name sortKey="Longini Jr, Ira M" sort="Longini Jr, Ira M" uniqKey="Longini Jr I" first="Ira M." last="Longini Jr.">Ira M. Longini Jr.</name><name sortKey="Obenchain, Valerie" sort="Obenchain, Valerie" uniqKey="Obenchain V" first="Valerie" last="Obenchain">Valerie Obenchain</name><name sortKey="Yang, Yang" sort="Yang, Yang" uniqKey="Yang Y" first="Yang" last="Yang">Yang Yang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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