Identification of time delays in COVID-19 data
نویسندگان
چکیده
Abstract Objective COVID-19 data released by public health authorities is subject to inherent time delays. Such delays have many causes, including in reporting and the natural incubation period of disease. We develop introduce a numerical procedure recover distribution these from data. Methods extend previously-introduced compartmental model with nonlinear, distributed-delay term general distribution, obtaining an integrodifferential equation. show this can be approximated weighted-sum constant time-delay terms, yielding linear problem for weights. Standard optimization then used weights, approximating demonstrate viability approach against Italy Austria. Results find that delay-distributions both Austria follow Gaussian-like profile, mean around 11 14 days. However, we note delay does not appear across all types, infection, recovery, mortality showing slightly different trends, suggesting presence independent each processes. also found recovered delay-distribution sensitive discretization resolution. Conclusions These results establish validity introduced identification time-delays Our methods are limited COVID-19, may applied other types epidemiological data, or indeed any dynamical system effects.
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ژورنال
عنوان ژورنال: Epidemiologic methods
سال: 2023
ISSN: ['2194-9263', '2161-962X']
DOI: https://doi.org/10.1515/em-2022-0117