Abstract
We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a stay-at-home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov chain Monte Carlo methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its subregions in order to account for the wide disparities in population size and density. Without prior information on nonpharmaceutical interventions, the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. Forward predictions of the model provide robust estimates of the peak position and severity and also enable forecasting the regional-dependent results of releasing stay-at-home orders. The resulting highly constrained narrative of the epidemic is able to provide estimates of its unseen progression and inform scenarios for sustainable monitoring and control of the epidemic.
- Received 15 June 2020
- Revised 13 August 2020
- Accepted 22 October 2020
DOI:https://doi.org/10.1103/PhysRevX.10.041033
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Viewpoint
ViewpointThe Uncertain Future in How a Virus Spreads
Published 16 November 2020
A new model helps clarify the limits of pandemic predictions, which are notoriously difficult for the near future and impossible for longer timescales.
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Popular Summary
To combat the spread of COVID-19, the infectious disease caused by the novel coronavirus SARS-CoV-2, the governor of Illinois issued a stay-at-home order for the entire state on March 21, 2020. Roughly two months later, the order was lifted. Recently, parts of the state have begun to see hints of a resurgence of the disease. Any responsible return to normalcy must be informed by realistic epidemiological modeling not only of the resulting increased death toll but also of the stress placed upon the healthcare system. Here, we report on such an analysis, performed in real time during the progression of the epidemic.
We provide an accurate quantitative description of the COVID-19 epidemic dynamics capturing both government-imposed mitigation and scenarios for its eventual release. The novelty of our approach is that our model is calibrated against multiple time-dependent data streams, and we provide a detailed analysis of the predictive power of epidemiological modeling. We use Markov chain Monte Carlo statistical analysis techniques to take into account epidemiological estimates of model parameters such as delays between times of infection, hospitalization, and death.
Our analysis shows that the stay-at-home order and social distancing were crucial in “flattening the curve,” reducing hospital occupancy and deaths by at least an order of magnitude. We also find that abruptly removing the stay-at-home order would have likely led to a significant second wave of infection, whereas a gradual return would delay the second wave and reduce its peak.
The importance to society of this timely interdisciplinary topic, approached using state-of-the-art computational physics techniques combined with epidemiological modeling concepts, should make our study of interest to the broad scientific community as it works to combat the ongoing pandemic.
