Search for: All records

Creators/Authors contains: "Pidgeon, Brian"

« Prev Next »

Total Resources

2

Resource Type
Conference Paper

0

Conference Proceeding

0

Dataset

0

Journal Article

2

Workshop Report

0

Availability
Full Text / Resource Available

2

Citation Only

0

Save Results
Excel (limit 2000)
CSV (limit 5000)
XML (limit 5000)

Have feedback or suggestions for a way to improve these results?
!

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

On stable parameter estimation and short-term forecasting with quantified uncertainty with application to COVID-19 transmission

https://doi.org/10.1515/jiip-2021-0037

Smirnova, Alexandra ; Pidgeon, Brian ; Luo, Ruiyan ( April 2022 , Journal of Inverse and Ill-posed Problems)

Abstract A novel optimization algorithm for stable parameter estimation and forecasting from limited incidence data for an emerging outbreak is proposed.The algorithm combines a compartmental model of disease progression with iteratively regularized predictor-corrector numerical scheme aimed at the reconstruction of case reporting ratio, transmission rate, and effective reproduction number.The algorithm is illustrated with real data on COVID-19 pandemic in the states of Georgia and New York, USA.The techniques of functional data analysis are applied for uncertainty quantification in extracted parameters and in future projections of new cases.
more » « less
Full Text Available
The doubling time analysis for modified infectious disease Richards model with applications to COVID-19 pandemic

https://doi.org/10.3934/mbe.2022150

Smirnova, Alexandra ; Pidgeon, Brian ; Chowell, Gerardo ; Zhao, Yichuan ( January 2022 , Mathematical Biosciences and Engineering)

In the absence of reliable information about transmission mechanisms for emerging infectious diseases, simple phenomenological models could provide a starting point to assess the potential outcomes of unfolding public health emergencies, particularly when the epidemiological characteristics of the disease are poorly understood or subject to substantial uncertainty. In this study, we employ the modified Richards model to analyze the growth of an epidemic in terms of 1) the number of times cumulative cases double until the epidemic peaks and 2) the rate at which the intervals between consecutive doubling times increase during the early ascending stage of the outbreak. Our theoretical analysis of doubling times is combined with rigorous numerical simulations and uncertainty quantification using synthetic and real data for COVID-19 pandemic. The doubling-time approach allows to employ early epidemic data to differentiate between the most dangerous threats, which double in size many times over the intervals that are nearly invariant, and the least transmissible diseases, which double in size only a few times with doubling periods rapidly growing.

more » « less
Full Text Available