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Updated: Feb 24, 2026

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

パンデミックのための早期段階のローカルモデルの開発：公衆衛生データへの適用：COVID-19パンデミックへの応用

Jiasheng Shi¹, Jeffrey S Morris², David Rubin³

¹School of Data Science, The Chinese University of Hong Kong, Shenzhen, China.

AMIA ... Annual Symposium proceedings. AMIA Symposium

|February 23, 2026

Abstract:

The emergence of novel infectious pathogens challenges early-phase modeling of disease transmission due to limited, low-quality data and an incomplete understanding of the pathogen. Additionally, regional variations in outbreaks necessitate models that incorporate local dynamics. We present an early-phase local model that leverages constrained public health data, primarily infection counts and aggregated regional characteristics, to study disease transmission dynamics. To address data limitations and potential model misspecifications, we incorporate a quasi-likelihood approach with a flexible error term. Furthermore, we introduce an online estimator that enables real-time data updates, supported by an iterative algorithm for parameter estimation. We applied this method to early COVID-19 data, analyzing infection counts and county-level risk factors from more than 800 U.S. counties to predict disease spread and assess the impact of social behavior, demographics, and vaccination coverage on disease transmission. This framework improves early outbreak analysis and informs local pandemic response under suboptimal data conditions.

キーワード:

感染症伝播モデリングパンデミック対応公衆衛生データ早期アウトブレイク検出ローカルモデリング

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