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Peitsch J, Pokharel G, Hossain S. Ensemble learning methods of inference for spatially stratified infectious disease systems. Int J Biostat 2024; 0:ijb-2023-0102. [PMID: 38590142 DOI: 10.1515/ijb-2023-0102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/13/2024] [Indexed: 04/10/2024]
Abstract
Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.
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Affiliation(s)
- Jeffrey Peitsch
- Department of Mathematics and Statistics, 2129 University of Calgary , Calgary, AB, Canada
| | - Gyanendra Pokharel
- Department of Mathematics and Statistics, 8665 University of Winnipeg , Winnipeg, MB, Canada
| | - Shakhawat Hossain
- Department of Mathematics and Statistics, 8665 University of Winnipeg , Winnipeg, MB, Canada
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Ward C, Deardon R, Schmidt AM. Bayesian modeling of dynamic behavioral change during an epidemic. Infect Dis Model 2023; 8:947-963. [PMID: 37608881 PMCID: PMC10440573 DOI: 10.1016/j.idm.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/20/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023] Open
Abstract
For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.
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Affiliation(s)
- Caitlin Ward
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Rob Deardon
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada
| | - Alexandra M. Schmidt
- Department of Epidemiology, Biostatistics, and Occupational Health, Montreal, QC, Canada
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Amiri L, Torabi M, Deardon R. Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach. SPATIAL STATISTICS 2023; 55:100729. [PMID: 37089455 PMCID: PMC10103593 DOI: 10.1016/j.spasta.2023.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/29/2023] [Accepted: 02/14/2023] [Indexed: 05/03/2023]
Abstract
The basic homogeneous SEIR (susceptible-exposed-infected-removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the population of study is homogeneous and, one cannot incorporate individual-level information (e.g., location of infected people, distance between susceptible and infected individuals, vaccination status) which may be important in predicting new disease cases. Recently, a geographically-dependent individual-level model (GD-ILM) within an SEIR framework was developed for when both regional and individual-level spatial data are available. In this paper, we propose to use an SEIR GD-ILM for each health region of Manitoba (central Canadian province) population to analyse the COVID-19 data. As different health regions of the population under study may act differently, we assume that each health region has its own corresponding parameters determined by a homogeneous SEIR model (such as contact rate, latent period, infectious period). A Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters we predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas. Performance of the proposed approach is also evaluated through simulation studies.
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Affiliation(s)
- Leila Amiri
- Departments of Community Health Sciences & Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Mahmoud Torabi
- Departments of Community Health Sciences & Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Rob Deardon
- Department of Mathematics and Statistics & Faculty of Veterinary Medicine, University of Calgary, Calgary, Canada
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Mahmood M, Mateu J, Hernández-Orallo E. Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 36:893-917. [PMID: 34720737 PMCID: PMC8547309 DOI: 10.1007/s00477-021-02065-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/18/2021] [Indexed: 05/28/2023]
Abstract
The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.
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Kirkeby C, Brookes VJ, Ward MP, Dürr S, Halasa T. A Practical Introduction to Mechanistic Modeling of Disease Transmission in Veterinary Science. Front Vet Sci 2021; 7:546651. [PMID: 33575275 PMCID: PMC7870987 DOI: 10.3389/fvets.2020.546651] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Computer-based disease spread models are frequently used in veterinary science to simulate disease spread. They are used to predict the impacts of the disease, plan and assess surveillance, or control strategies, and provide insights about disease causation by comparing model outputs with real life data. There are many types of disease spread models, and here we present and describe the implementation of a particular type: individual-based models. Our aim is to provide a practical introduction to building individual-based disease spread models. We also introduce code examples with the goal to make these techniques more accessible to those who are new to the field. We describe the important steps in building such models before, during and after the programming stage, including model verification (to ensure that the model does what was intended), validation (to investigate whether the model results reflect the modeled system), and convergence analysis (to ensure models of endemic diseases are stable before outputs are collected). We also describe how sensitivity analysis can be used to assess the potential impact of uncertainty about model parameters. Finally, we provide an overview of some interesting recent developments in the field of disease spread models.
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Affiliation(s)
- Carsten Kirkeby
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark,*Correspondence: Carsten Kirkeby
| | - Victoria J. Brookes
- School of Animal and Veterinary Sciences, Faculty of Science, Charles Sturt University, Wagga, NSW, Australia,Graham Centre for Agricultural Innovation (Charles Sturt University and NSW Department of Primary Industries), Wagga, NSW, Australia
| | - Michael P. Ward
- Faculty of Veterinary Science, Sydney School of Veterinary Science, University of Sydney, Sydney, NSW, Australia
| | - Salome Dürr
- Department of Clinical Research and Public Health, Veterinary Public Health Institute, University of Bern, Bern, Switzerland
| | - Tariq Halasa
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
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Amiri L, Torabi M, Deardon R, Pickles M. Spatial modeling of individual-level infectious disease transmission: Tuberculosis data in Manitoba, Canada. Stat Med 2021; 40:1678-1704. [PMID: 33469942 DOI: 10.1002/sim.8863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 10/28/2020] [Accepted: 12/10/2020] [Indexed: 11/10/2022]
Abstract
Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates.
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Affiliation(s)
- Leila Amiri
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Mahmoud Torabi
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Department of Statistics, Faculty of Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Rob Deardon
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Michael Pickles
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
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