Robust estimation of the incubation period and the time of exposure using γ-divergence

Estimating the exposure time to single infectious pathogens and the associated incubation period, based on symptom onset data, is crucial for identifying infection sources and implementing public health interventions. However, data from rapid surveillance systems designed for early outbreak warning...

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Vydáno v:Journal of applied statistics Ročník 52; číslo 6; s. 1239 - 1257
Hlavní autoři: Yoneoka, Daisuke, Kawashima, Takayuki, Tanoue, Yuta, Nomura, Shuhei, Eguchi, Akifumi
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Taylor & Francis 26.04.2025
Taylor & Francis Ltd
Témata:
Algorithms
Confidence intervals
Divergence
Estimation
Exposure
exposure time
infectious disease
Optimization
Parameters
Public health
robust estimation
Robustness
Statistical analysis
Surveillance systems
three-parameter lognormal
γ-divergence
γ-divergence
three-parameter lognormal
infectious disease
robust estimation
92B15
exposure time
62F35
ISSN:0266-4763, 1360-0532
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Shrnutí:Estimating the exposure time to single infectious pathogens and the associated incubation period, based on symptom onset data, is crucial for identifying infection sources and implementing public health interventions. However, data from rapid surveillance systems designed for early outbreak warning often come with outliers originated from individuals who were not directly exposed to the initial source of infection (i.e. tertiary and subsequent infection cases), making the estimation of exposure time challenging. To address this issue, this study uses a three-parameter lognormal distribution and proposes a new γ-divergence-based robust approach for estimating the parameter corresponding to exposure time with a tailored optimization procedure using the majorization-minimization algorithm, which ensures the monotonic decreasing property of the objective function. Comprehensive numerical experiments and real data analyses suggest that our method is superior to conventional methods in terms of bias, mean squared error, and coverage probability of 95% confidence intervals.
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ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2024.2420221