1
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Ward C, Brown GD, Oleson JJ. Incorporating infectious duration-dependent transmission into Bayesian epidemic models. Biom J 2023; 65:e2100401. [PMID: 36285663 DOI: 10.1002/bimj.202100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 09/02/2022] [Accepted: 09/13/2022] [Indexed: 11/11/2022]
Abstract
Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.
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Affiliation(s)
- Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA
| | - Grant D Brown
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA
| | - Jacob J Oleson
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA
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3
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Hatami F, Chen S, Paul R, Thill JC. Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model. Int J Environ Res Public Health 2022; 19:ijerph192315771. [PMID: 36497846 PMCID: PMC9736132 DOI: 10.3390/ijerph192315771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 05/09/2023]
Abstract
The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.
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Affiliation(s)
- Faizeh Hatami
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Shi Chen
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Correspondence:
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4
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Martinez K, Brown G, Pankavich S. Spatially-heterogeneous embedded stochastic SEIR models for the 2014–2016 Ebola outbreak in West Africa. Spat Spatiotemporal Epidemiol 2022; 41:100505. [DOI: 10.1016/j.sste.2022.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 12/03/2021] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
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5
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Potgieter A, Fabris-Rotelli IN, Kimmie Z, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, Thiede RN, Debba P, Manjoo-Docrat R, Abdelatif N, Khuluse-Makhanya S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Front Big Data 2021; 4:718351. [PMID: 34746771 PMCID: PMC8570263 DOI: 10.3389/fdata.2021.718351] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.
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Affiliation(s)
- A Potgieter
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - I N Fabris-Rotelli
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - Z Kimmie
- Foundation of Human Rights, Johannesburg, South Africa
| | - N Dudeni-Tlhone
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - J P Holloway
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - C Janse van Rensburg
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - R N Thiede
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - P Debba
- Inclusive Smart Settlements and Regions, Smart Places, Council for Scientific and Industrial Research, Pretoria, South Africa.,Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - R Manjoo-Docrat
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - N Abdelatif
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - S Khuluse-Makhanya
- IBM Research, Johannesburg, South Africa.,College of Graduate Studies, University of South Africa, Johannesburg, South Africa
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6
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Potgieter A, Fabris-Rotelli IN, Kimmie Z, Dudeni-Tlhone N, Holloway JP, Janse van Rensburg C, Thiede RN, Debba P, Manjoo-Docrat R, Abdelatif N, Khuluse-Makhanya S. Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Front Big Data 2021. [PMID: 34746771 DOI: 10.20944/preprints202106.0211.v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.
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Affiliation(s)
- A Potgieter
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - I N Fabris-Rotelli
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - Z Kimmie
- Foundation of Human Rights, Johannesburg, South Africa
| | - N Dudeni-Tlhone
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - J P Holloway
- Operational Intelligence, NextGen Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - C Janse van Rensburg
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - R N Thiede
- Department of Statistics, University of Pretoria, Pretoria, South Africa
| | - P Debba
- Inclusive Smart Settlements and Regions, Smart Places, Council for Scientific and Industrial Research, Pretoria, South Africa
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - R Manjoo-Docrat
- Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa
| | - N Abdelatif
- Biostatistics Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - S Khuluse-Makhanya
- IBM Research, Johannesburg, South Africa
- College of Graduate Studies, University of South Africa, Johannesburg, South Africa
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7
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Ozanne MV, Brown GD, Toepp AJ, Scorza BM, Oleson JJ, Wilson ME, Petersen CA. Bayesian compartmental models and associated reproductive numbers for an infection with multiple transmission modes. Biometrics 2020; 76:711-721. [PMID: 31785149 PMCID: PMC7673222 DOI: 10.1111/biom.13192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 11/08/2019] [Accepted: 11/14/2019] [Indexed: 01/01/2023]
Abstract
Zoonotic visceral leishmaniasis (ZVL) is a serious neglected tropical disease that is endemic in 98 countries. ZVL is primarily transmitted via a sand fly vector. In the United States, it is enzootic in some canine populations; it is transmitted from infectious mother to pup transplacentally, and vector-borne transmission is absent. This absence affords a unique opportunity to study (1) vertical transmission dynamics in dogs and (2) the importance of vertical transmission in maintaining an infectious reservoir in the presence of a vector. In this paper, we present Bayesian compartmental models and reproductive number formulations to examine (1) and (2), providing a mechanism to plan and evaluate interventions in regions where both transmission modes are present. First, we propose an individual-level susceptible, infectious, removed (SIR) model to study the effect of maternal infection status during pregnancy on pup infection progression. We provide evidence that pups born to diagnostically positive mothers during pregnancy are more likely to become diagnostically positive both earlier in life, and at some point during their lifetime, than those born to diagnostically negative mothers. Second, we propose a population-level SIR model to study the impact of a vertically maintained reservoir on propagating infection in a naive canine population through emergent vector transmission using simulation studies. We also present reproductive numbers to quantify contributions of vertically infected and vector-infected dogs to maintaining infection in the population. We show that a vertically maintained canine reservoir can propagate infection in a theoretical naive population in the presence of a vector.
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Affiliation(s)
- Marie V Ozanne
- Department of Mathematics & Statistics, Mount Holyoke College, South Hadley, Massachusetts
- Department of Biostatistics, University of Iowa College of Public Health, Iowa City, Iowa
| | - Grant D Brown
- Department of Biostatistics, University of Iowa College of Public Health, Iowa City, Iowa
| | - Angela J Toepp
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa
| | - Breanna M Scorza
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa
| | - Jacob J Oleson
- Department of Biostatistics, University of Iowa College of Public Health, Iowa City, Iowa
| | - Mary E Wilson
- Department of Internal Medicine, University of Iowa Roy J and Lucille A Carver College of Medicine, Iowa City, Iowa
- Department of Microbiology, University of Iowa Roy J and Lucille A Carver College of Medicine, Iowa City, Iowa
- Iowa City VA Medical Center, Iowa City, Iowa
| | - Christine A Petersen
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa
- Center for Emerging Infectious Diseases, University of Iowa College of Public Health, Iowa City, Iowa
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8
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Hu Y, Xu L, Pan H, Shi X, Chen Y, Lynn H, Mao S, Zhang H, Cao H, Zhang J, Zhang J, Xiao S, Hu J, Li X, Yao S, Zhang Z, Zhao G. Transmission center and driving factors of hand, foot, and mouth disease in China: A combined analysis. PLoS Negl Trop Dis 2020; 14:e0008070. [PMID: 32150558 PMCID: PMC7062235 DOI: 10.1371/journal.pntd.0008070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 01/17/2020] [Indexed: 11/18/2022] Open
Abstract
Hand, foot, and mouth disease (HFMD) has become a major public health issue in China. The disease incidence varies substantially over time and across space. To understand the heterogeneity of HFMD transmission, we compare the spatiotemporal dynamics of HFMD in Qinghai and Shanghai by conducting combined analysis of epidemiological, wavelet time series, and mathematical methods to county-level data from 2009 to 2016. We observe hierarchical epidemic waves in Qinghai, emanating from Huangzhong and in Shanghai from Fengxian. Besides population, we also find that the traveling waves are significantly associated with socio-economic and geographical factors. The population mobility also varies between the two regions: long-distance movement in Qinghai and between-neighbor commuting in Shanghai. Our findings provide important evidence for characterizing the heterogeneity of HFMD transmission and for the design and implementation of interventions, such as deploying optimal vaccine and changing local driving factors in the transmission center, to prevent or limit disease spread in these areas.
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Affiliation(s)
- Yi Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Lili Xu
- Institute for Infectious Disease Control and Prevention, Qinghai Provincial Center for Disease Control and Prevention, Qinghai, China
| | - Hao Pan
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Yue Chen
- Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ontario, Canada
| | - Henry Lynn
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Shenghua Mao
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Huayi Zhang
- Institute for Infectious Disease Control and Prevention, Qinghai Provincial Center for Disease Control and Prevention, Qinghai, China
| | - Hailan Cao
- Institute for Infectious Disease Control and Prevention, Qinghai Provincial Center for Disease Control and Prevention, Qinghai, China
| | - Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Shuang Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Jian Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | - Xiande Li
- Department of Geography, Shanghai Normal University, Shanghai, China
| | - Shenjun Yao
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China
- School of Geographic Sciences, East China Normal University, Shanghai, China
| | - Zhijie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
- * E-mail:
| | - Genming Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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9
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Abstract
This paper discusses methods of estimating the reproductive power and the accompanying survival function of communicable events, e.g. infectious disease transmission. The early stage of an outbreak can be described by the infectiousness of the outbreak process, but in later stages of the outbreak, this is complicated by factors such as changing contact patterns and the impact of control measures. It is important to take these factors into account in order to get a good, if approximate, model for an outbreak process. This paper proposes a non-homogeneous birth process and regression model for the reproductive power function, similar to models in discrete survival analysis. A baseline reproductive power function gives a description of the outbreak when covariates are at their baseline values. As an illustration these methods are applied to an avian influenza (H5N1) outbreak among poultry in Thailand.
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Affiliation(s)
- Jan van den Broek
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
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10
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Fu X, Zhou Y, Wu J, Liu X, Ding C, Huang C, Deng M, Shi D, Wang C, Xu K, Ren J, Ruan B, Li L, Yang S. A Severe Seasonal Influenza Epidemic During 2017–2018 in China After the 2009 Pandemic Influenza: A Modeling Study. Infectious Microbes and Diseases 2019; 1:20-6. [DOI: 10.1097/im9.0000000000000006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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11
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Ozanne MV, Brown GD, Oleson JJ, Lima ID, Queiroz JW, Jeronimo SMB, Petersen CA, Wilson ME. Bayesian compartmental model for an infectious disease with dynamic states of infection. J Appl Stat 2018; 46:1043-1065. [PMID: 31537954 PMCID: PMC6752225 DOI: 10.1080/02664763.2018.1531979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 09/30/2018] [Indexed: 12/21/2022]
Abstract
Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.
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Affiliation(s)
- Marie V Ozanne
- Department of Biostatistics, University of Iowa College of Public Health, USA
| | - Grant D Brown
- Department of Biostatistics, University of Iowa College of Public Health, USA
| | - Jacob J Oleson
- Department of Biostatistics, University of Iowa College of Public Health, USA
| | - Iraci D Lima
- Department of Infectious Diseases, Universidade Federal do Rio Grande do Norte, Brazil
| | - Jose W Queiroz
- Department of Infectious Diseases, Universidade Federal do Rio Grande do Norte, Brazil
| | - Selma M B Jeronimo
- Institute of Tropical Medicine, Universidade Federal do Rio Grande do Norte, Brazil
- Department of Biochemistry, Universidade Federal do Rio Grande do Norte, Brazil
- National Institute of Science and Technology in Tropical Diseases, Bahia, Brazil
| | - Christine A Petersen
- Department of Epidemiology, University of Iowa College of Public Health, USA
- Center for Emerging Infectious Diseases, University of Iowa College of Public Health, USA
| | - Mary E Wilson
- Department of Epidemiology, University of Iowa College of Public Health, USA
- Department of Internal Medicine, University of Iowa Roy J and Lucille A Carver College of Medicine, USA
- Department of Microbiology, University of Iowa Roy J and Lucille A Carver College of Medicine, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
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12
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Ganyani T, Faes C, Chowell G, Hens N. Assessing inference of the basic reproduction number in an SIR model incorporating a growth-scaling parameter. Stat Med 2018; 37:4490-4506. [PMID: 30117184 DOI: 10.1002/sim.7935] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 06/08/2018] [Accepted: 07/16/2018] [Indexed: 11/12/2022]
Abstract
The standard mass action, which assumes that infectious disease transmission occurs in well-mixed populations, is popular for formulating compartmental epidemic models. Compartmental epidemic models often follow standard mass action for simplicity and to gain insight into transmission dynamics as it often performs well at reproducing disease dynamics in large populations. In this work, we formulate discrete time stochastic susceptible-infected-removed models with linear (standard) and nonlinear mass action structures to mimic varying mixing levels. Using simulations and real epidemic data, we demonstrate the sensitivity of the basic reproduction number to these mathematical structures of the force of infection. Our results suggest the need to consider nonlinear mass action in order to generate more accurate estimates of the basic reproduction number although its uncertainty increases due to the addition of one growth scaling parameter.
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Affiliation(s)
- Tapiwa Ganyani
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt (Hasselt University), Diepenbeek, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt (Hasselt University), Diepenbeek, Belgium
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, Georgia.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institute of Health, Bethesda, Maryland
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, UHasselt (Hasselt University), Diepenbeek, Belgium.,Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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13
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Brown GD, Porter AT, Oleson JJ, Hinman JA. Approximate Bayesian computation for spatial SEIR(S) epidemic models. Spat Spatiotemporal Epidemiol 2017; 24:27-37. [PMID: 29413712 DOI: 10.1016/j.sste.2017.11.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 10/06/2017] [Accepted: 11/14/2017] [Indexed: 11/25/2022]
Abstract
Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas.
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Affiliation(s)
- Grant D Brown
- Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242 USA.
| | - Aaron T Porter
- Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado 80401 USA
| | - Jacob J Oleson
- Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242 USA
| | - Jessica A Hinman
- Department of Epidemiology, University of Iowa, Iowa City, Iowa 52242 USA
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VanBuren J, Oleson JJ, Zamba GKD, Wall M. Integrating independent spatio-temporal replications to assess population trends in disease spread. Stat Med 2016; 35:5210-5221. [PMID: 27453437 DOI: 10.1002/sim.7056] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 06/06/2016] [Accepted: 07/03/2016] [Indexed: 11/06/2022]
Abstract
Glaucoma is the second leading cause of blindness in the USA. A visual field test (perimetry) is used to sample and quantitate visual field function in preselected regions in the eye. These regions can be considered a spatial field with replications across independently measured individuals. At return visits, a new set of visual field measurements is obtained producing a subject specific spatio-temporal dataset. We develop a Bayesian hierarchical modeling framework to analyze these spatio-temporal datasets both for individual level spread and as aggregate population level trends. Our model extends previous research utilizing a dimension reduction matrix and individual specific latent variables. Human characteristics are incorporated into the model to help explain glaucoma progression. One beneficial product of our model is smoothed estimates for individuals. We also specify how progression rates are computed for monitoring purposes so that clinicians can track changes and predict forward in time. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- John VanBuren
- Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A..
| | - Jacob J Oleson
- Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A
| | - Gideon K D Zamba
- Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A
| | - Michael Wall
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, U.S.A
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Abstract
Several West African countries - Liberia, Sierra Leone and Guinea - experienced significant morbidity and mortality during the largest Ebola epidemic to date, from late 2013 through 2015. The extent of the epidemic was fueled by outbreaks in large urban population centers as well as movement of the pathogen between populations. During the epidemic there was no known vaccine or drug, so effective disease control required coordinated efforts that include both standard medical and community practices such as hospitalization, quarantine and safe burials. Due to the high connectivity of the region, control of the epidemic not only depended on internal strategies but also was impacted by neighboring countries. In this paper, we use a deterministic framework to examine the role of movement between two populations in the overall success of practices designed to minimize the extent of Ebola epidemics. We find that it is possible for even small amounts of intermixing between populations to positively impact the control of an epidemic on a more global scale.
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Affiliation(s)
- J. C. Blackwood
- Department of Mathematics and Statistics Williams College, Williamstown, MA 01267, USA
| | - L. M. Childs
- Department of Mathematics and Statistics Williams College, Williamstown, MA 01267, USA
- Center for Communicable Disease Dynamics Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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