Revisiting classical SIR modelling in light of the COVID-19 pandemic
Background: Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and under...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-03-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042722001087 |
| _version_ | 1797866910687166464 |
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| author | Leonid Kalachev Erin L. Landguth Jon Graham |
| author_facet | Leonid Kalachev Erin L. Landguth Jon Graham |
| author_sort | Leonid Kalachev |
| collection | DOAJ |
| description | Background: Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms. Objective: We aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment. Methods: We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA. Results: We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected. Conclusions: We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the “right” data in the “wrong” model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements. |
| first_indexed | 2024-04-09T23:32:58Z |
| format | Article |
| id | doaj.art-71df519677914332bb404c1ddbff2aa3 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-09T23:32:58Z |
| publishDate | 2023-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-71df519677914332bb404c1ddbff2aa32023-03-21T04:16:57ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272023-03-01817283Revisiting classical SIR modelling in light of the COVID-19 pandemicLeonid Kalachev0Erin L. Landguth1Jon Graham2Mathematical Sciences, University of Montana, Missoula, USA; Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USACenter for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USA; Corresponding author. University of Montana, 32 Campus Drive, Missoula, MT, 59812, USA.Mathematical Sciences, University of Montana, Missoula, USA; Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USABackground: Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms. Objective: We aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment. Methods: We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA. Results: We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected. Conclusions: We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the “right” data in the “wrong” model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.http://www.sciencedirect.com/science/article/pii/S2468042722001087Basic disease reproduction numberCommunicable disease controlCoronavirusCOVID-19Disease transmissionEpidemics |
| spellingShingle | Leonid Kalachev Erin L. Landguth Jon Graham Revisiting classical SIR modelling in light of the COVID-19 pandemic Infectious Disease Modelling Basic disease reproduction number Communicable disease control Coronavirus COVID-19 Disease transmission Epidemics |
| title | Revisiting classical SIR modelling in light of the COVID-19 pandemic |
| title_full | Revisiting classical SIR modelling in light of the COVID-19 pandemic |
| title_fullStr | Revisiting classical SIR modelling in light of the COVID-19 pandemic |
| title_full_unstemmed | Revisiting classical SIR modelling in light of the COVID-19 pandemic |
| title_short | Revisiting classical SIR modelling in light of the COVID-19 pandemic |
| title_sort | revisiting classical sir modelling in light of the covid 19 pandemic |
| topic | Basic disease reproduction number Communicable disease control Coronavirus COVID-19 Disease transmission Epidemics |
| url | http://www.sciencedirect.com/science/article/pii/S2468042722001087 |
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