Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
Abstract
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.
- Authors:
-
[1];
[1];
[2];
[1]
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1874225
- Alternate Identifier(s):
- OSTI ID: 1874665
- Report Number(s):
- PNNL-SA-174364
Journal ID: ISSN 2045-2322
- Grant/Contract Number:
- AC05-76RL01830; AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 42 ENGINEERING
Citation Formats
Spannaus, Adam, Papamarkou, Theodore, Erwin, Samantha H., and Christian, J. Blair. Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling. United States: N. p., 2022.
Web. doi:10.1038/s41598-022-14979-0.
Spannaus, Adam, Papamarkou, Theodore, Erwin, Samantha H., & Christian, J. Blair. Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling. United States. https://doi.org/10.1038/s41598-022-14979-0
Spannaus, Adam, Papamarkou, Theodore, Erwin, Samantha H., and Christian, J. Blair. Fri .
"Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling". United States. https://doi.org/10.1038/s41598-022-14979-0. https://www.osti.gov/servlets/purl/1874225.
@article{osti_1874225,
title = {Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling},
author = {Spannaus, Adam and Papamarkou, Theodore and Erwin, Samantha H. and Christian, J. Blair},
abstractNote = {The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.},
doi = {10.1038/s41598-022-14979-0},
journal = {Scientific Reports},
number = 1,
volume = 12,
place = {United States},
year = {Fri Jun 24 00:00:00 EDT 2022},
month = {Fri Jun 24 00:00:00 EDT 2022}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Capturing the time-varying drivers of an epidemic using stochastic dynamical systems
journal, January 2013
- Dureau, J.; Kalogeropoulos, K.; Baguelin, M.
- Biostatistics, Vol. 14, Issue 3
Particle Markov chain Monte Carlo methods: Particle Markov Chain Monte Carlo Methods
journal, June 2010
- Andrieu, Christophe; Doucet, Arnaud; Holenstein, Roman
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72, Issue 3
An Introduction to Compartmental Modeling for the Budding Infectious Disease Modeler
journal, January 2018
- Blackwood, Julie; Childs, Lauren
- Letters in Biomathematics, Vol. 5, Issue 1
Dynamics of Shigellosis Epidemics: Estimating Individual-Level Transmission and Reporting Rates From National Epidemiologic Data Sets
journal, September 2013
- Joh, Richard I.; Hoekstra, Robert M.; Barzilay, Ezra J.
- American Journal of Epidemiology, Vol. 178, Issue 8
Artificial neural network scheme to solve the nonlinear influenza disease model
journal, May 2022
- Sabir, Zulqurnain; Botmart, Thongchai; Asif Zahoor Raja, Muhammad
- Biomedical Signal Processing and Control, Vol. 75
Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study
journal, January 2021
- Štorkánová, Hana; Oreská, Sabína; Špiritović, Maja
- Scientific Reports, Vol. 11, Issue 1
Modelling under-reporting in epidemics
journal, August 2013
- Gamado, Kokouvi M.; Streftaris, George; Zachary, Stan
- Journal of Mathematical Biology, Vol. 69, Issue 3
The pandemic’s true death toll: millions more than official counts
journal, January 2022
- Adam, David
- Nature, Vol. 601, Issue 7893
Temporal dynamics in viral shedding and transmissibility of COVID-19
journal, April 2020
- He, Xi; Lau, Eric H. Y.; Wu, Peng
- Nature Medicine, Vol. 26, Issue 5
Informing policy makers about future health spending: A comparative analysis of forecasting methods in OECD countries
journal, September 2012
- Astolfi, Roberto; Lorenzoni, Luca; Oderkirk, Jillian
- Health Policy, Vol. 107, Issue 1
The global community needs to swiftly ramp up the response to contain COVID-19
journal, April 2020
- Fisher, Dale; Wilder-Smith, Annelies
- The Lancet, Vol. 395, Issue 10230
The implications of silent transmission for the control of COVID-19 outbreaks
journal, July 2020
- Moghadas, Seyed M.; Fitzpatrick, Meagan C.; Sah, Pratha
- Proceedings of the National Academy of Sciences, Vol. 117, Issue 30
Sequential Monte Carlo with Highly Informative Observations
journal, January 2015
- Del Moral, Pierre; Murray, Lawrence M.
- SIAM/ASA Journal on Uncertainty Quantification, Vol. 3, Issue 1
Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity
journal, February 2021
- Subramanian, Rahul; He, Qixin; Pascual, Mercedes
- Proceedings of the National Academy of Sciences, Vol. 118, Issue 9
Capturing the time-varying drivers of an epidemic using stochastic dynamical systems
preprint, January 2012
- Dureau, Joseph; Kalogeropoulos, Konstantinos; Baguelin, Marc
- arXiv
Accounting for Underreporting in Mathematical Modeling of Transmission and Control of COVID-19 in Iran
journal, July 2020
- Saberi, Meead; Hamedmoghadam, Homayoun; Madani, Kaveh
- Frontiers in Physics, Vol. 8
Evidence for Limited Early Spread of COVID-19 Within the United States, January–February 2020
journal, June 2020
- Jorden, Michelle A.; Rudman, Sarah L.; Villarino, Elsa
- MMWR. Morbidity and Mortality Weekly Report, Vol. 69, Issue 22
Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases
journal, February 2007
- Lloyd-Smith, James O.
- PLoS ONE, Vol. 2, Issue 2
On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control
journal, January 2022
- Sulaiman, Ibrahim Mohammed; Malik, Maulana; Awwal, Aliyu Muhammed
- Advances in Continuous and Discrete Models, Vol. 2022, Issue 1
Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany
journal, March 2020
- Rothe, Camilla; Schunk, Mirjam; Sothmann, Peter
- New England Journal of Medicine, Vol. 382, Issue 10
Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters
journal, March 2021
- Lau, H.; Khosrawipour, T.; Kocbach, P.
- Pulmonology, Vol. 27, Issue 2
COVID-19 underreporting and its impact on vaccination strategies
journal, October 2021
- Albani, Vinicius; Loria, Jennifer; Massad, Eduardo
- BMC Infectious Diseases, Vol. 21, Issue 1
The pseudo-marginal approach for efficient Monte Carlo computations
journal, April 2009
- Andrieu, Christophe; Roberts, Gareth O.
- The Annals of Statistics, Vol. 37, Issue 2
Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters
journal, March 2021
- Lau, H.; Khosrawipour, T.; Kocbach, P.
- Pulmonology, Vol. 27, Issue 2
Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America
journal, December 2017
- Shutt, Deborah P.; Manore, Carrie A.; Pankavich, Stephen
- Epidemics, Vol. 21
Practical considerations for measuring the effective reproductive number, Rt
journal, December 2020
- Gostic, Katelyn M.; McGough, Lauren; Baskerville, Edward B.
- PLOS Computational Biology, Vol. 16, Issue 12
Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model
journal, March 2018
- Funk, Sebastian; Camacho, Anton; Kucharski, Adam J.
- Epidemics, Vol. 22
Sequential Monte Carlo samplers
journal, June 2006
- Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 68, Issue 3
Estimating the incidence reporting rates of new influenza pandemics at an early stage using travel data from the source country
journal, October 2013
- Chong, K. C.; Fong, H. F.; Zee, C. Y.
- Epidemiology and Infection, Vol. 142, Issue 5
Evidence Synthesis for Stochastic Epidemic Models
journal, February 2018
- Birrell, Paul J.; De Angelis, Daniela; Presanis, Anne M.
- Statistical Science, Vol. 33, Issue 1
Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling
journal, August 2021
- Mishra, Swapnil; Scott, James A.; Laydon, Daniel J.
- Scientific Reports, Vol. 11, Issue 1