1
|
KhudaBukhsh WR, Rempała GA. How to correctly fit an SIR model to data from an SEIR model? Math Biosci 2024; 375:109265. [PMID: 39089573 DOI: 10.1016/j.mbs.2024.109265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/24/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases. This paper investigates stochastic versions of the SEIR and SIR frameworks and demonstrates that the SEIR model can be effectively approximated by a SIR model with time-dependent infection and recovery rates. The validity of this approximation is supported by the derivation of a large-population Functional Law of Large Numbers (FLLN) limit and a finite-population concentration inequality. To apply this approximation in practice, the paper introduces a parameter inference methodology based on the Dynamic Survival Analysis (DSA) survival analysis framework. This method enables the fitting of the SIR model to data simulated from the more complex SEIR dynamics, as illustrated through simulated experiments.
Collapse
Affiliation(s)
- Wasiur R KhudaBukhsh
- School of Mathematical Sciences, The University of Nottingham, University Park, Nottingham, NG7 2RD, Nottinghamshire, United Kingdom.
| | - Grzegorz A Rempała
- Division of Biostatistics, College of Public Health, The Ohio State University, 1841 Neil Avenue, Cunz Hall, Columbus, 43210, OH, United States of America.
| |
Collapse
|
2
|
Kiss IZ, Berthouze L, KhudaBukhsh WR. Towards Inferring Network Properties from Epidemic Data. Bull Math Biol 2023; 86:6. [PMID: 38063898 PMCID: PMC10709280 DOI: 10.1007/s11538-023-01235-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023]
Abstract
Epidemic propagation on networks represents an important departure from traditional mass-action models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the high-dimensionality becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. Data from an epidemics can be loosely categorised as being population level, e.g., daily new cases, or individual level, e.g., recovery times. To understand if and how network inference is influenced by the type of data, we employed the widely-used MLE approach for population-level data and dynamical survival analysis (DSA) for individual-level data. For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. In contrast, for real-world data, such as foot-and-mouth, H1N1 and COVID19, whereas the DSA method appears fairly robust to potential model mismatch and produces parameter estimates that are epidemiologically plausible, our results with the MLE method revealed several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network.
Collapse
Affiliation(s)
- Istvan Z Kiss
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
- Network Science Institute, Northeastern University London, London, E1W 1LP, UK.
| | - Luc Berthouze
- Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QH, UK
| | - Wasiur R KhudaBukhsh
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| |
Collapse
|
3
|
Kiss IZ, Kenah E, Rempała GA. Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks. J Math Biol 2023; 87:36. [PMID: 37532967 PMCID: PMC10397147 DOI: 10.1007/s00285-023-01967-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 05/09/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023]
Abstract
We prove that it is possible to obtain the exact closure of SIR pairwise epidemic equations on a configuration model network if and only if the degree distribution follows a Poisson, binomial, or negative binomial distribution. The proof relies on establishing the equivalence, for these specific degree distributions, between the closed pairwise model and a dynamical survival analysis (DSA) model that was previously shown to be exact. Specifically, we demonstrate that the DSA model is equivalent to the well-known edge-based Volz model. Using this result, we also provide reductions of the closed pairwise and Volz models to a single equation that involves only susceptibles. This equation has a useful statistical interpretation in terms of times to infection. We provide some numerical examples to illustrate our results.
Collapse
Affiliation(s)
- István Z Kiss
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
- Network Science Institute, Northeastern University London, London, E1W 1LP, UK.
| | - Eben Kenah
- Division of Biostatistics, College of Public Health and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA
| | - Grzegorz A Rempała
- Division of Biostatistics, College of Public Health and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
4
|
KhudaBukhsh WR, Khalsa SK, Kenah E, Rempała GA, Tien JH. COVID-19 dynamics in an Ohio prison. Front Public Health 2023; 11:1087698. [PMID: 37064663 PMCID: PMC10098107 DOI: 10.3389/fpubh.2023.1087698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 02/20/2023] [Indexed: 03/31/2023] Open
Abstract
Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number >14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons.
Collapse
Affiliation(s)
- Wasiur R. KhudaBukhsh
- School of Mathematical Sciences, The University of Nottingham, Nottingham, United Kingdom
| | - Sat Kartar Khalsa
- Wexner Medical Center, The Ohio State University, Columbus, OH, United States
| | - Eben Kenah
- Division of Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Gregorz A. Rempała
- Division of Biostatistics, Department of Mathematics, The Ohio State University, Columbus, OH, United States
| | - Joseph H. Tien
- Division of Epidemiology, Department of Mathematics, The Ohio State University, Columbus, OH, United States
- *Correspondence: Joseph H. Tien
| |
Collapse
|
5
|
KhudaBukhsh WR, Bastian CD, Wascher M, Klaus C, Sahai SY, Weir MH, Kenah E, Root E, Tien JH, Rempała GA. Projecting COVID-19 cases and hospital burden in Ohio. J Theor Biol 2023; 561:111404. [PMID: 36627078 PMCID: PMC9824941 DOI: 10.1016/j.jtbi.2022.111404] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/13/2022] [Accepted: 12/26/2022] [Indexed: 01/09/2023]
Abstract
As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
Collapse
Affiliation(s)
- Wasiur R KhudaBukhsh
- School of Mathematical Sciences, University of Nottingham, University Park Nottingham NG7 2RD, United Kingdom.
| | - Caleb Deen Bastian
- Program in Applied and Computational Mathematics, Princeton University, Fine Hall, Washington Road, Princeton, NJ 08544, USA.
| | - Matthew Wascher
- Department of Mathematics, University of Dayton, 300 College Park, Dayton, OH 45469, USA.
| | - Colin Klaus
- Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Avenue, Columbus, OH 43210, USA; College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA; Infectious Diseases Institute, 208 Bricker Hall, 190 North Oval Mall, Columbus, OH 43210-1358, USA.
| | - Saumya Yashmohini Sahai
- Department of Computer Science and Engineering, The Ohio State University, 395 Dreese Laboratories, 2015 Neil Avenue, Columbus, OH 43210, USA.
| | - Mark H Weir
- College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA; Infectious Diseases Institute, 208 Bricker Hall, 190 North Oval Mall, Columbus, OH 43210-1358, USA; The Sustainability Institute, The Ohio State University, 74 W. 18th Avenue, Columbus, OH 43210, USA.
| | - Eben Kenah
- College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA; Infectious Diseases Institute, 208 Bricker Hall, 190 North Oval Mall, Columbus, OH 43210-1358, USA.
| | - Elisabeth Root
- Institute for Disease Modeling, The Bill & Melinda Gates Foundation, Seattle, WA, USA.
| | - Joseph H Tien
- Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Avenue, Columbus, OH 43210, USA; College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA; Infectious Diseases Institute, 208 Bricker Hall, 190 North Oval Mall, Columbus, OH 43210-1358, USA; Department of Mathematics, The Ohio State University, 231 West 18th Avenue, Columbus, OH 43210-1174, USA.
| | - Grzegorz A Rempała
- Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Avenue, Columbus, OH 43210, USA; College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA; Infectious Diseases Institute, 208 Bricker Hall, 190 North Oval Mall, Columbus, OH 43210-1358, USA; Department of Mathematics, The Ohio State University, 231 West 18th Avenue, Columbus, OH 43210-1174, USA.
| |
Collapse
|
6
|
Klaus C, Wascher M, KhudaBukhsh WR, Rempała GA. Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4103-4127. [PMID: 36899619 DOI: 10.3934/mbe.2023192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the Dynamical Survival Analysis (DSA) method has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of Dynamical Survival Analysis (DSA) is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations. In this work we describe how a complex non-Markovian Dynamical Survival Analysis (DSA) model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.
Collapse
Affiliation(s)
- Colin Klaus
- Mathematical Biosciences Institute and the Division of Biostatistics, College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA
| | - Matthew Wascher
- Department of Mathematics, University of Dayton, 300 College Park Dayton, Ohio 45469, USA
| | - Wasiur R KhudaBukhsh
- School of Mathematical Sciences, The University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Grzegorz A Rempała
- Mathematical Biosciences Institute and the Division of Biostatistics, College of Public Health, The Ohio State University, Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA
| |
Collapse
|
7
|
Smith T, Holm RH, Keith RJ, Amraotkar AR, Alvarado CR, Banecki K, Choi B, Santisteban IC, Bushau-Sprinkle AM, Kitterman KT, Fuqua J, Hamorsky KT, Palmer KE, Brick JM, Rempala GA, Bhatnagar A. Quantifying the relationship between sub-population wastewater samples and community-wide SARS-CoV-2 seroprevalence. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 853:158567. [PMID: 36084773 PMCID: PMC9444845 DOI: 10.1016/j.scitotenv.2022.158567] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/07/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
Robust epidemiological models relating wastewater to community disease prevalence are lacking. Assessments of SARS-CoV-2 infection rates have relied primarily on convenience sampling, which does not provide reliable estimates of community disease prevalence due to inherent biases. This study conducted serial stratified randomized samplings to estimate the prevalence of SARS-CoV-2 antibodies in 3717 participants, and obtained weekly samples of community wastewater for SARS-CoV-2 concentrations in Jefferson County, KY (USA) from August 2020 to February 2021. Using an expanded Susceptible-Infected-Recovered model, the longitudinal estimates of the disease prevalence were obtained and compared with the wastewater concentrations using regression analysis. The model analysis revealed significant temporal differences in epidemic peaks. The results showed that in some areas, the average incidence rate, based on serological sampling, was 50 % higher than the health department rate, which was based on convenience sampling. The model-estimated average prevalence rates correlated well with the wastewater (correlation = 0.63, CI (0.31,0.83)). In the regression analysis, a one copy per ml-unit increase in weekly average wastewater concentration of SARS-CoV-2 corresponded to an average increase of 1-1.3 cases of SARS-CoV-2 infection per 100,000 residents. The analysis indicates that wastewater may provide robust estimates of community spread of infection, in line with the modeled prevalence estimates obtained from stratified randomized sampling, and is therefore superior to publicly available health data.
Collapse
Affiliation(s)
- Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Rachel J Keith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Alok R Amraotkar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Chance R Alvarado
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
| | - Krzysztof Banecki
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Boseung Choi
- Division of Big Data Science, Korea University, Sejong, South Korea; Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | - Ian C Santisteban
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA
| | - Adrienne M Bushau-Sprinkle
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Kathleen T Kitterman
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA
| | - Joshua Fuqua
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA; Department of Pharmacology and Toxicology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Krystal T Hamorsky
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | - Kenneth E Palmer
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY 40202, USA; Department of Pharmacology and Toxicology, School of Medicine, University of Louisville, Louisville, KY 40202, USA
| | | | - Grzegorz A Rempala
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
| | - Aruni Bhatnagar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY 40202, USA.
| |
Collapse
|