Estimating incubation period distributions with coarse data
Identifieur interne : 002E09 ( Main/Exploration ); précédent : 002E08; suivant : 002E10Estimating incubation period distributions with coarse data
Auteurs : Nicholas G. Reich [États-Unis] ; Justin Lessler [États-Unis] ; Derek A. T. Cummings [États-Unis] ; Ron Brookmeyer [États-Unis]Source :
- Statistics in Medicine [ 0277-6715 ] ; 2009-09-30.
English descriptors
- Teeft :
- Accurate estimates, American journal, Calendar time, Cent interval coverage, Cent interval coverage rates, Cent intervals, Cent probability, Central tendency, Coarse data, Columns display results, Copyright, Coverage probabilities, Data reduction technique, Data sets, Disease onset, Dispersion, Diurnal exposure, Diurnal exposure distribution, Exposure distribution, Exposure interval, Exposure window, Exposure window size, Failure time model, Gure displays, Incubation, Incubation period, Incubation period data, Incubation period distribution, Incubation period distributions, Incubation periods, Infection, Infection time, Infection times, Infectious disease, Infectious diseases, Interval coverage, Intervalcensored, Intervalcensored analysis, John wiley sons, Johns hopkins bloomberg school, Large impact, Larger estimates, Larger sample sizes, Likelihood, Literature review, Median, Median incubation period, Model assumption, Normal distribution, Normal models, Outbreak, Outbreak investigations, Parametric, Percentile, Point estimates, Possible incubation periods, Possible values, Public health, Public health interventions, Respiratory syncytial virus, Respiratory syndrome, Same observation, Sample size, Second analysis, Sensitivity analysis, Simulation, Simulation parameters, Simulation study, Single analyses, Single analysis, Single coverage probabilities, Single data, Single interval, Single likelihood, Spike exposure distribution, Standard errors, Statist, Symptom onset, Symptom onset times, Symptom onset window size, Trapezoidal shape, True incubation periods, Uniform exposure distribution, Uniform infection times, Valuable contribution, Viral infections, Weibull, Weibull distribution, Weibull incubation periods, Weight function, Wide range, Wolfe street, World health organization.
Abstract
The incubation period, the time between infection and disease onset, is important in the surveillance and control of infectious diseases but is often coarsely observed. Coarse data arises because the time of infection, the time of disease onset or both are not known precisely. Accurate estimates of an incubation period distribution are useful in real‐time outbreak investigations and in modeling public health interventions. We compare two methods of estimating such distributions. The first method represents the data as doubly interval‐censored. The second introduces a data reduction technique that makes the computation more tractable. In a simulation study, the methods perform similarly when estimating the median, but the first method yields more reliable estimates of the distributional tails. We conduct a sensitivity analysis of the two methods to violations of model assumption and we apply these methods to historical incubation period data on influenza A and respiratory syncytial virus. The analysis of reduced data is less computationally intensive and performs well for estimating the median under a wide range of conditions. However for estimation of the tails of the distribution, the doubly interval‐censored analysis is the recommended procedure. Copyright © 2009 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/sim.3659
Affiliations:
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Coarse data arises because the time of infection, the time of disease onset or both are not known precisely. Accurate estimates of an incubation period distribution are useful in real‐time outbreak investigations and in modeling public health interventions. We compare two methods of estimating such distributions. The first method represents the data as doubly interval‐censored. The second introduces a data reduction technique that makes the computation more tractable. In a simulation study, the methods perform similarly when estimating the median, but the first method yields more reliable estimates of the distributional tails. We conduct a sensitivity analysis of the two methods to violations of model assumption and we apply these methods to historical incubation period data on influenza A and respiratory syncytial virus. The analysis of reduced data is less computationally intensive and performs well for estimating the median under a wide range of conditions. However for estimation of the tails of the distribution, the doubly interval‐censored analysis is the recommended procedure. Copyright © 2009 John Wiley & Sons, Ltd.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><country name="États-Unis"><region name="Maryland"><name sortKey="Reich, Nicholas G" sort="Reich, Nicholas G" uniqKey="Reich N" first="Nicholas G." last="Reich">Nicholas G. Reich</name></region><name sortKey="Brookmeyer, Ron" sort="Brookmeyer, Ron" uniqKey="Brookmeyer R" first="Ron" last="Brookmeyer">Ron Brookmeyer</name><name sortKey="Cummings, Derek A T" sort="Cummings, Derek A T" uniqKey="Cummings D" first="Derek A. T." last="Cummings">Derek A. T. Cummings</name><name sortKey="Lessler, Justin" sort="Lessler, Justin" uniqKey="Lessler J" first="Justin" last="Lessler">Justin Lessler</name><name sortKey="Reich, Nicholas G" sort="Reich, Nicholas G" uniqKey="Reich N" first="Nicholas G." last="Reich">Nicholas G. Reich</name><name sortKey="Reich, Nicholas G" sort="Reich, Nicholas G" uniqKey="Reich N" first="Nicholas G." last="Reich">Nicholas G. Reich</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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