SEMIPARAMETRIC MODELING OF SARS-COV-2 TRANSMISSION USING TESTS, CASES, DEATHS, AND SEROPREVALENCE DATA

被引：1

作者：

Bayer, Damon ^{[1
]}

Goldstein, Isaac H. ^{[1
]}

Fintzi, Jonathan ^{[2
]}

Lumbard, Keith ^{[3
]}

Ricotta, Emily ^{[4
]}

Warner, Sarah ^{[5
]}

Strich, Jeffrey R. ^{[5
]}

Chertow, Daniel S. ^{[5
]}

Busch, Lindsay M. ^{[6
]}

Parker, Daniel M. ^{[7
]}

Boden-Albala, Bernadette ^{[7
]}

Chhuon, Richard ^{[8
]}

Zahn, Matthew ^{[8
]}

Quick, Nichole ^{[9
]}

Dratch, Alissa ^{[10
]}

Minin, Volodymyr M. ^{[1
]}

机构：

[1] Univ Calif Irvine, Dept Stat, Irvine, CA 92697 USA

[2] NIAID, Biostat Res Branch, Rockville, MD USA

[3] Frederick Natl Lab Canc Res, Clin Monitoring Res Program Directorate, Frederick, MD USA

[4] NIAID, Epidemiol Unit, Rockville, MD USA

[5] NIH, Crit Care Med Dept, Clin Ctr, Bethesda, MD USA

[6] Emory Univ, Sch Med, Div Infect Dis, Atlanta, GA 30030 USA

[7] Univ Calif Irvine, Susan & Henry Samueli Coll Hlth Sci, Irvine, CA USA

[8] Orange Cty Hlth Care Agcy, Orange City, IA USA

[9] Los Angeles Cty Dept Publ Hlth, Los Angeles, CA USA

[10] Edwards Lifesci, Irvine, CA USA

来源：

ANNALS OF APPLIED STATISTICS | 2024年 / 18卷 / 03期

基金：

美国国家卫生研究院;

关键词：

Epidemic models; SARS-CoV-2; COVID-19;

D O I：

10.1214/24-AOAS1882

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Mechanistic models fit to streaming surveillance data are critical for understanding the transmission dynamics of an outbreak as it unfolds in realtime. However, transmission model parameter estimation can be imprecise, sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data as well as seroprevalence data from cross-sectional studies and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We apply our Bayesian data integration method to COVID19 surveillance data collected in Orange County, California, between March 2020 and February 2021 and find that 32-72% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest that the abrupt end of the winter surge in January 2021 was due to both behavioral changes and a high level of accumulated natural immunity.

引用

页码：2307 / 2325

页数：19

共 52 条

[1]

Abbott S., 2020, WELLCOME OPEN RES, V5, P112, DOI [10.12688/wellcomeopenres.16006.2, DOI 10.12688/WELLCOMEOPENRES.16006.2]

[2] Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing [J].

Anderson, Sean C. ;

Edwards, Andrew M. ;

Yerlanov, Madi ;

Mulberry, Nicola ;

Stockdale, Jessica E. ;

Iyaniwura, Sarafa A. ;

Falcao, Rebeca C. ;

Otterstatter, Michael C. ;

Irvine, Michael A. ;

Janjua, Naveed Z. ;

Coombs, Daniel ;

Colijn, Caroline .

PLOS COMPUTATIONAL BIOLOGY, 2020, 16 (12)

[3] Particle Markov chain Monte Carlo methods [J].

Andrieu, Christophe ;

Doucet, Arnaud ;

Holenstein, Roman .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2010, 72 :269-342

[4]

BAYER D., 2024, Supplement to "Semiparametric modeling of SARS-CoV-2 trans- mission using tests, cases, deaths, and seroprevalence data, DOI [10.1214/24-AOAS1882SUPPA, DOI 10.1214/24-AOAS1882SUPPA]

[5] Predictors for Severe COVID-19 Infection [J].