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A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts


Bracher, Johannes; Held, Leonhard (2020). A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts. Biometrics:Epub ahead of print.

Abstract

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic‐epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model‐based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time‐varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.

Abstract

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic‐epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model‐based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time‐varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Immunology and Microbiology
Life Sciences > General Agricultural and Biological Sciences
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:General Biochemistry, Genetics and Molecular Biology, Statistics and Probability, General Immunology and Microbiology, Applied Mathematics, General Agricultural and Biological Sciences, General Medicine
Language:English
Date:28 September 2020
Deposited On:13 Jan 2021 12:39
Last Modified:14 Jan 2021 21:01
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0006-341X
OA Status:Green
Publisher DOI:https://doi.org/10.1111/biom.13371
PubMed ID:32920842

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