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Slater JJ, Brown PE, Rosenthal JS, Mateu J. Capturing spatial dependence of COVID-19 case counts with cellphone mobility data. SPATIAL STATISTICS 2022; 49:100540. [PMID: 34603946 PMCID: PMC8479517 DOI: 10.1016/j.spasta.2021.100540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/26/2021] [Accepted: 09/06/2021] [Indexed: 05/07/2023]
Abstract
Spatial dependence is usually introduced into spatial models using some measure of physical proximity. When analysing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.
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Affiliation(s)
- Justin J Slater
- Department of Statistical Sciences, University of Toronto, Canada
- Centre for Global Health Research, St. Michael's Hospital, Canada
| | - Patrick E Brown
- Department of Statistical Sciences, University of Toronto, Canada
- Centre for Global Health Research, St. Michael's Hospital, Canada
| | | | - Jorge Mateu
- Department of Mathematics, University Jaume I of Castellon, Spain
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Abstract
This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements.
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De Rooij MMT, Van Leuken JPG, Swart A, Kretzschmar MEE, Nielen M, De Koeijer AA, Janse I, Wouters IM, Heederik DJJ. A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics. Zoonoses Public Health 2018; 66:14-25. [PMID: 30402920 PMCID: PMC7379662 DOI: 10.1111/zph.12534] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 10/08/2018] [Indexed: 01/07/2023]
Abstract
From 2007 through 2010, the Netherlands experienced the largest Q fever epidemic ever reported. This study integrates the outcomes of a multidisciplinary research programme on spatial airborne transmission of Coxiella burnetii and reflects these outcomes in relation to other scientific Q fever studies worldwide. We have identified lessons learned and remaining knowledge gaps. This synthesis was structured according to the four steps of quantitative microbial risk assessment (QMRA): (a) Rapid source identification was improved by newly developed techniques using mathematical disease modelling; (b) source characterization efforts improved knowledge but did not provide accurate C. burnetii emission patterns; (c) ambient air sampling, dispersion and spatial modelling promoted exposure assessment; and (d) risk characterization was enabled by applying refined dose–response analyses. The results may support proper and timely risk assessment and risk management during future outbreaks, provided that accurate and structured data are available and exchanged readily between responsible actors.
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Affiliation(s)
- Myrna M T De Rooij
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Jeroen P G Van Leuken
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Arno Swart
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Mirjam E E Kretzschmar
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.,Julius Centre, University Medical Centre Utrecht (UMCU), Utrecht, The Netherlands
| | - Mirjam Nielen
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Aline A De Koeijer
- Central Veterinary Institute, Wageningen University and Research Centre, Lelystad, The Netherlands
| | - Ingmar Janse
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Inge M Wouters
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Dick J J Heederik
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
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Qiao P, Mølck C, Ferrari D, Hollande F. A Spatio-Temporal Model and Inference Tools for Longitudinal Count Data on Multicolor Cell Growth. Int J Biostat 2018; 14:/j/ijb.ahead-of-print/ijb-2018-0008/ijb-2018-0008.xml. [PMID: 29981281 DOI: 10.1515/ijb-2018-0008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 06/19/2018] [Indexed: 11/15/2022]
Abstract
Multicolor cell spatio-temporal image data have become important to investigate organ development and regeneration, malignant growth or immune responses by tracking different cell types both in vivo and in vitro. Statistical modeling of image data from common longitudinal cell experiments poses significant challenges due to the presence of complex spatio-temporal interactions between different cell types and difficulties related to measurement of single cell trajectories. Current analysis methods focus mainly on univariate cases, often not considering the spatio-temporal effects affecting cell growth between different cell populations. In this paper, we propose a conditional spatial autoregressive model to describe multivariate count cell data on the lattice, and develop inference tools. The proposed methodology is computationally tractable and enables researchers to estimate a complete statistical model of multicolor cell growth. Our methodology is applied on real experimental data where we investigate how interactions between cancer cells and fibroblasts affect their growth, which are normally present in the tumor microenvironment. We also compare the performance of our methodology to the multivariate conditional autoregressive (MCAR) model in both simulations and real data applications.
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Affiliation(s)
- PuXue Qiao
- The University of Melbourne, Melbourne, Australia
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Bakka H, Rue H, Fuglstad G, Riebler A, Bolin D, Illian J, Krainski E, Simpson D, Lindgren F. Spatial modeling with R‐INLA: A review. ACTA ACUST UNITED AC 2018. [DOI: 10.1002/wics.1443] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Haakon Bakka
- CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia
| | - Håvard Rue
- CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia
| | - Geir‐Arne Fuglstad
- Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
| | - Andrea Riebler
- Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway
| | - David Bolin
- Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg Gothenburg Sweden
| | - Janine Illian
- CREEM, School of Mathematics and Statistics University of St Andrews St. Andrews UK
| | - Elias Krainski
- Departamento de Estatística Universidade Federal do Paraná Paraná Brazil
| | - Daniel Simpson
- Department of Statistical Sciences University of Toronto Toronto Canada
| | - Finn Lindgren
- School of Mathematics University of Edinburgh Edinburgh UK
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Denis M, Cochard B, Syahputra I, de Franqueville H, Tisné S. Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm. Spat Spatiotemporal Epidemiol 2018; 24:63-74. [PMID: 29413715 DOI: 10.1016/j.sste.2017.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/17/2017] [Accepted: 12/22/2017] [Indexed: 10/18/2022]
Abstract
In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software.
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Meyer S, Held L. Incorporating social contact data in spatio-temporal models for infectious disease spread. Biostatistics 2017; 18:338-351. [PMID: 28025182 PMCID: PMC5379927 DOI: 10.1093/biostatistics/kxw051] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Accepted: 11/06/2016] [Indexed: 01/10/2023] Open
Abstract
Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases-possibly stratified by region and/or age group. We investigate how an age-structured social contact matrix can be incorporated into a spatio-temporal endemic-epidemic model for infectious disease counts. To illustrate the approach, we analyze the spread of norovirus gastroenteritis over six age groups within the 12 districts of Berlin, 2011-2015, using contact data from the POLYMOD study. The proposed age-structured model outperforms alternative scenarios with homogeneous or no mixing between age groups. An extended contact model suggests a power transformation of the survey-based contact matrix toward more within-group transmission.
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Affiliation(s)
- Sebastian Meyer
- Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg,Waldstraße 6, DE-91054 Erlangen,
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland
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Amirpour Haredasht S, Polson D, Main R, Lee K, Holtkamp D, Martínez-López B. Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices. BMC Vet Res 2017; 13:163. [PMID: 28592317 PMCID: PMC5463409 DOI: 10.1186/s12917-017-1076-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 05/25/2017] [Indexed: 11/21/2022] Open
Abstract
Background Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PTji) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight. Results The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124). Conclusion The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.
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Affiliation(s)
- Sara Amirpour Haredasht
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School Veterinary Medicine, University of California, 2108 Tupper Hall, one Shields Avenue, Davis, California, 95616, USA
| | - Dale Polson
- Boehringer-Ingelheim Vetmedica Inc, Saint Joseph, Missouri, USA
| | - Rodger Main
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, USA
| | - Kyuyoung Lee
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School Veterinary Medicine, University of California, 2108 Tupper Hall, one Shields Avenue, Davis, California, 95616, USA
| | - Derald Holtkamp
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, USA
| | - Beatriz Martínez-López
- Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School Veterinary Medicine, University of California, 2108 Tupper Hall, one Shields Avenue, Davis, California, 95616, USA.
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Charu V, Zeger S, Gog J, Bjørnstad ON, Kissler S, Simonsen L, Grenfell BT, Viboud C. Human mobility and the spatial transmission of influenza in the United States. PLoS Comput Biol 2017; 13:e1005382. [PMID: 28187123 PMCID: PMC5349690 DOI: 10.1371/journal.pcbi.1005382] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 03/14/2017] [Accepted: 01/26/2017] [Indexed: 11/18/2022] Open
Abstract
Seasonal influenza epidemics offer unique opportunities to study the invasion and re-invasion waves of a pathogen in a partially immune population. Detailed patterns of spread remain elusive, however, due to lack of granular disease data. Here we model high-volume city-level medical claims data and human mobility proxies to explore the drivers of influenza spread in the US during 2002–2010. Although the speed and pathways of spread varied across seasons, seven of eight epidemics likely originated in the Southern US. Each epidemic was associated with 1–5 early long-range transmission events, half of which sparked onward transmission. Gravity model estimates indicate a sharp decay in influenza transmission with the distance between infectious and susceptible cities, consistent with spread dominated by work commutes rather than air traffic. Two early-onset seasons associated with antigenic novelty had particularly localized modes of spread, suggesting that novel strains may spread in a more localized fashion than previously anticipated. The underlying mechanisms dictating the spatial spread of seasonal influenza remain poorly understood, in part because of the lack of spatially resolved disease data to quantify patterns of spread. In this paper, we address this issue by analyzing fine-grain insurance claims data on influenza-like-illnesses over eight seasons in ~300 locations throughout the United States. Using statistical methods, we found that seven of eight epidemics likely originated in the Southern US, that influenza spatial transmission is dominated by local traffic between cities, and that seasons marked by novel influenza virus circulation had a particularly radial, localized spatial structure. These findings are in stark contrast to prevailing theories of influenza spatial transmission that suggest that transmission is favored in low humidity environments and that spread is a dominated by air traffic between populous hubs.
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Affiliation(s)
- Vivek Charu
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- * E-mail:
| | - Scott Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Julia Gog
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Ottar N. Bjørnstad
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Entomology, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Stephen Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Lone Simonsen
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Public Health, Copenhagen University, Copenhagen, Denmark
| | - Bryan T. Grenfell
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America
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Pirard P, Goria S, Nguengang Wakap S, Galey C, Motreff Y, Guillet A, Le Tertre A, Corso M, Beaudeau P. No increase in drug dispensing for acute gastroenteritis after Storm Klaus, France 2009. JOURNAL OF WATER AND HEALTH 2015; 13:737-745. [PMID: 26322759 DOI: 10.2166/wh.2015.188] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
During the night of 23-24 January 2009, Storm Klaus hit south-western France and caused power outages affecting 1,700,000 homes and stopping numerous pumping and drinking water disinfection systems. In France, medicalized acute gastroenteritis (MAGE) outbreaks are monitored by analysing the daily amount of reimbursements of medical prescriptions, registered in the French National Health Insurance database, at the 'commune' administrative level. As AGE is suspected to be associated with perturbations to water supply systems as well as power outages, Storm Klaus provided an opportunity to test its influence on the incidence of MAGE in the communes of three affected French departments (administrative areas larger than communes). The geographical exposure indicator was built by using the mapping of the water distribution zones, the reported distribution/production stoppages and their duration. Irrespective of exposure class, a relative risk of MAGE of 0.86 (95% confidence 0.84-0.88) was estimated compared with the 'unexposed' reference level. Although these results must be considered with caution because of a potential marked decrease in global medical consultation probably due to impassable roads, they do not suggest a major public health impact of Klaus in terms of increased MAGE incidence.
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Affiliation(s)
- P Pirard
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - S Goria
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - S Nguengang Wakap
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - C Galey
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - Y Motreff
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - A Guillet
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - A Le Tertre
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - M Corso
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
| | - P Beaudeau
- Département santé environnement, Institut de Veille Sanitaire, 12 rue du Val d'Osne, Saint-Maurice Cedex 94415, France E-mail
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Geilhufe M, Held L, Skrøvseth SO, Simonsen GS, Godtliebsen F. Power law approximations of movement network data for modeling infectious disease spread. Biom J 2014; 56:363-82. [PMID: 24843881 DOI: 10.1002/bimj.201200262] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Globalization and increased mobility of individuals enable person-to-person transmitted infectious diseases to spread faster to distant places around the world, making good models for the spread increasingly important. We study the spatiotemporal pattern of spread in the remotely located and sparsely populated region of North Norway in various models with fixed, seasonal, and random effects. The models are applied to influenza A counts using data from positive microbiology laboratory tests as proxy for the underlying disease incidence. Human travel patterns with local air, road, and sea traffic data are incorporated as well as power law approximations thereof, both with quasi-Poisson regression and based on the adjacency structure of the relevant municipalities. We investigate model extensions using information about the proportion of positive laboratory tests, data on immigration from outside North Norway and by connecting population to the movement network. Furthermore, we perform two separate analyses for nonadults and adults as children are an important driver for influenza A. Comparisons of one-step-ahead predictions generally yield better or comparable results using power law approximations.
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Corberán-Vallet A, Lawson AB. Prospective analysis of infectious disease surveillance data using syndromic information. Stat Methods Med Res 2014; 23:572-90. [PMID: 24659490 DOI: 10.1177/0962280214527385] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease counts due to the presence of an epidemic. A novelty of our model formulation is that the parameters describing the spread of epidemics are allowed to vary in both space and time. We also show how syndromic information can be incorporated into the model to provide a better description of the data and more accurate one-step-ahead forecasts. These real-time forecasts can be used to identify high-risk areas for outbreaks and, consequently, to develop efficient targeted surveillance. We apply the methodology to weekly emergency room discharges for acute bronchitis in South Carolina.
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Affiliation(s)
- Ana Corberán-Vallet
- Department of Statistics and Operations Research, University of Valencia, Burjassot, Spain
| | - Andrew B Lawson
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, U.S.A
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Yu HL, Angulo JM, Cheng MH, Wu J, Christakos G. An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan). Biom J 2014; 56:428-40. [PMID: 24615833 DOI: 10.1002/bimj.201200270] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Revised: 12/17/2013] [Accepted: 12/18/2013] [Indexed: 11/10/2022]
Abstract
The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time.
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Affiliation(s)
- Hwa-Lung Yu
- Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan
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Ensoy C, Aerts M, Welby S, Van der Stede Y, Faes C. A dynamic spatio-temporal model to investigate the effect of cattle movements on the spread of bluetongue BTV-8 in Belgium. PLoS One 2013; 8:e78591. [PMID: 24244324 PMCID: PMC3823847 DOI: 10.1371/journal.pone.0078591] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 09/21/2013] [Indexed: 11/18/2022] Open
Abstract
When Bluetongue Virus Serotype 8 (BTV-8) was first detected in Northern Europe in 2006, several guidelines were immediately put into place with the goal to protect farms and stop the spreading of the disease. This however did not prevent further rapid spread of BTV-8 across Northern Europe. Using information on the 2006 Bluetongue outbreak in cattle farms in Belgium, a spatio-temporal transmission model was formulated. The model quantifies the local transmission of the disease between farms within a municipality, the short-distance transmission between farms across neighbouring municipalities and the transmission as a result of cattle movement. Different municipality-level covariates such as farm density, land composition variables, temperature and precipitation, were assessed as possibly influencing each component of the transmission process. Results showed a significant influence of the different covariates in each model component, particularly the significant effect of temperature and precipitation values in the number of infected farms. The model which allowed us to predict the dynamic spreading of BTV for different movement restriction scenarios, also affirmed the significant impact of cattle movement in the 2006 BTV outbreak pattern. Simulation results further showed the importance of considering the size of restriction zones in the formulation of guidelines for animal infectious diseases.
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Affiliation(s)
- Chellafe Ensoy
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Universiteit Hasselt, Hasselt, Belgium
- * E-mail:
| | - Marc Aerts
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Universiteit Hasselt, Hasselt, Belgium
| | - Sarah Welby
- Veterinary and Agrochemical Research Centre, Brussels, Belgium
| | - Yves Van der Stede
- Veterinary and Agrochemical Research Centre, Brussels, Belgium
- University of Ghent, Laboratory of Vet. Immunology, Merelbeke, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Universiteit Hasselt, Hasselt, Belgium
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Angulo J, Yu HL, Langousis A, Kolovos A, Wang J, Madrid AE, Christakos G. Spatiotemporal infectious disease modeling: a BME-SIR approach. PLoS One 2013; 8:e72168. [PMID: 24086257 PMCID: PMC3785461 DOI: 10.1371/journal.pone.0072168] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 07/11/2013] [Indexed: 11/18/2022] Open
Abstract
This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.
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Affiliation(s)
- Jose Angulo
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - Hwa-Lung Yu
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
- * E-mail:
| | - Andrea Langousis
- Department of Civil Engineering, University of Patras, Patras, Greece
| | - Alexander Kolovos
- SpaceTimeWorks, LLC, San Diego, California, United States of America
| | - Jinfeng Wang
- Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Ana Esther Madrid
- University Centre of Defence at the Spanish Air Force Academy, MDE-UPCT, Santiago de la Ribera, Murcia, Spain
| | - George Christakos
- Department of Geography, San Diego State University, San Diego, California, United States of America
- Department of Environment & Natural Resources, Zhejiang University, Hangzhou, China
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Held L, Paul M. Modeling seasonality in space-time infectious disease surveillance data. Biom J 2012; 54:824-43. [PMID: 23034894 DOI: 10.1002/bimj.201200037] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2012] [Revised: 07/09/2012] [Accepted: 07/20/2012] [Indexed: 11/10/2022]
Abstract
Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one-step-ahead predictions if all three components allow for seasonal variation.
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Affiliation(s)
- Leonhard Held
- Division of Biostatistics, Institute of Social and Preventive Medicine, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.
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