On the Estimation of the Time-Dependent Transmission Rate in Epidemiological Models

Publication date: Dec 31, 2024

The COVID-19 pandemic highlighted the need to improve the modeling, estimation, and prediction of how infectious diseases spread. SEIR-like models have been particularly successful in providing accurate short-term predictions. This study fills a notable literature gap by exploring the following question: Is it possible to incorporate a nonparametric susceptible-exposed-infected-removed (SEIR) COVID-19 model into the inverse-problem regularization framework when the transmission coefficient varies over time? Our positive response considers varying degrees of disease severity, vaccination, and other time-dependent parameters. In addition, we demonstrate the continuity, differentiability, and injectivity of the operator that link the transmission parameter to the observed infection numbers. By employing Tikhonov-type regularization to the corresponding inverse problem, we establish the existence and stability of regularized solutions. Numerical examples using both synthetic and real data illustrate the model’s estimation accuracy and its ability to fit the data effectively.

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Concepts Keywords
Chicago Certified
Coronavirus Display
Hospitalizations Funder
Mathematics Granted
Max8mk International
License
Licenseit
Medrxiv
Parameter
Parameters
Peer
Perpetuity
Preprint
Transmission
Version

Semantics

Type Source Name
disease MESH COVID-19 pandemic
disease MESH infectious diseases
disease MESH infection
disease MESH Emergency
disease IDO host
disease IDO pathogen
disease IDO susceptible population
drug DRUGBANK Dacarbazine
disease MESH death
drug DRUGBANK Sodium lauryl sulfate
disease IDO process
disease IDO algorithm
drug DRUGBANK Pentaerythritol tetranitrate

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