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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of applied statistics Jg. 52; H. 6; S. 1239 - 1257
Hauptverfasser: Yoneoka, Daisuke, Kawashima, Takayuki, Tanoue, Yuta, Nomura, Shuhei, Eguchi, Akifumi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Taylor & Francis 26.04.2025
Taylor & Francis Ltd
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2024.2420221