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Pei H, Yang B, Liu J, Chang KCC. Active Surveillance via Group Sparse Bayesian Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:1133-1148. [PMID: 32915724 DOI: 10.1109/tpami.2020.3023092] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The key to the effective control of a diffusion system lies in how accurately we could predict its unfolding dynamics based on the observation of its current state. However, in the real-world applications, it is often infeasible to conduct a timely and yet comprehensive observation due to resource constraints. In view of such a practical challenge, the goal of this work is to develop a novel computational method for performing active observations, termed active surveillance, with limited resources. Specifically, we aim to predict the dynamics of a large spatio-temporal diffusion system based on the observations of some of its components. Towards this end, we introduce a novel measure, the γ value, that enables us to identify the key components by means of modeling a sentinel network with a row sparsity structure. Having obtained a theoretical understanding of the γ value, we design a backward-selection sentinel network mining algorithm (SNMA) for deriving the sentinel network via group sparse Bayesian learning. In order to be practically useful, we further address the issue of scalability in the computation of SNMA, and moreover, extend SNMA to the case of a non-linear dynamical system that could involve complex diffusion mechanisms. We show the effectiveness of SNMA by validating it using both synthetic datasets and five real-world datasets. The experimental results are appealing, which demonstrate that SNMA readily outperforms the state-of-the-art methods.
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Bourhy H, Nakouné E, Hall M, Nouvellet P, Lepelletier A, Talbi C, Watier L, Holmes EC, Cauchemez S, Lemey P, Donnelly CA, Rambaut A. Revealing the Micro-scale Signature of Endemic Zoonotic Disease Transmission in an African Urban Setting. PLoS Pathog 2016; 12:e1005525. [PMID: 27058957 PMCID: PMC4825935 DOI: 10.1371/journal.ppat.1005525] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 03/03/2016] [Indexed: 11/24/2022] Open
Abstract
The development of novel approaches that combine epidemiological and genomic data provides new opportunities to reveal the spatiotemporal dynamics of infectious diseases and determine the processes responsible for their spread and maintenance. Taking advantage of detailed epidemiological time series and viral sequence data from more than 20 years reported by the National Reference Centre for Rabies of Bangui, the capital city of Central African Republic, we used a combination of mathematical modeling and phylogenetic analysis to determine the spatiotemporal dynamics of rabies in domestic dogs as well as the frequency of extinction and introduction events in an African city. We show that although dog rabies virus (RABV) appears to be endemic in Bangui, its epidemiology is in fact shaped by the regular extinction of local chains of transmission coupled with the introduction of new lineages, generating successive waves of spread. Notably, the effective reproduction number during each wave was rarely above the critical value of 1, such that rabies is not self-sustaining in Bangui. In turn, this suggests that rabies at local geographic scales is driven by human-mediated dispersal of RABV among sparsely connected peri-urban and rural areas as opposed to dispersion in a relatively large homogenous urban dog population. This combined epidemiological and genomic approach enables development of a comprehensive framework for understanding disease persistence and informing control measures, indicating that control measures are probably best targeted towards areas neighbouring the city that appear as the source of frequent incursions seeding outbreaks in Bangui.
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Affiliation(s)
- Hervé Bourhy
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | | | - Matthew Hall
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, United Kingdom
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Pierre Nouvellet
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Anthony Lepelletier
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Chiraz Talbi
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Laurence Watier
- INSERM, UMR 1181 and Institut Pasteur, B2PHI, Paris, France
- Faculté de Médecine Paris Ile de France-Ouest, Université de Versailles–Saint-Quentin, Versailles, France
| | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases & Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, Sydney, Australia
| | - Simon Cauchemez
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France
| | | | - Christl A. Donnelly
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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Ferguson JM, Langebrake JB, Cannataro VL, Garcia AJ, Hamman EA, Martcheva M, Osenberg CW. Optimal sampling strategies for detecting zoonotic disease epidemics. PLoS Comput Biol 2014; 10:e1003668. [PMID: 24968100 PMCID: PMC4072525 DOI: 10.1371/journal.pcbi.1003668] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 04/28/2014] [Indexed: 11/18/2022] Open
Abstract
The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests. Outbreaks of zoonoses can have large costs to society through public health and agricultural impacts. Because many zoonoses co-occur in multiple animal populations simultaneously, detection of zoonotic outbreaks can be especially difficult. We evaluated how to design sampling strategies for the early detection of disease outbreaks of vector-borne diseases. We built a framework to integrate epidemiological dynamical models with a sampling process that accounts for budgetary constraints, such as those faced by many management agencies. We illustrate our approach using West Nile virus, a globally-spreading zoonotic arbovirus that has significantly affected North American bird populations. Our results suggest that simple formulas can often make robust predictions about the proper sampling procedure, though we also illustrate how computational methods can be used to extend our framework to more realistic modeling scenarios when these simple predictions break down.
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Affiliation(s)
- Jake M. Ferguson
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- * E-mail:
| | - Jessica B. Langebrake
- Department of Mathematics, University of Florida, Gainesville, Florida, United States of America
| | - Vincent L. Cannataro
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - Andres J. Garcia
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Department of Geography, University of Florida, Gainesville, Florida, United States of America
| | - Elizabeth A. Hamman
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - Maia Martcheva
- Department of Mathematics, University of Florida, Gainesville, Florida, United States of America
| | - Craig W. Osenberg
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
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Keller JP, Gerardo-Giorda L, Veneziani A. Numerical simulation of a susceptible-exposed-infectious space-continuous model for the spread of rabies in raccoons across a realistic landscape. JOURNAL OF BIOLOGICAL DYNAMICS 2013; 7 Suppl 1:31-46. [PMID: 23157180 PMCID: PMC3957468 DOI: 10.1080/17513758.2012.742578] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Revised: 10/04/2012] [Indexed: 05/19/2023]
Abstract
We introduce a numerical model for the spread of a lethal infectious disease in wildlife. The reference model is a Susceptible-Exposed-Infectious system where the spatial component of the dynamics is modelled by a diffusion process. The goal is to develop a model to be used for real geographical scenarios, so we do not rely upon simplifying assumptions on the shape of the region of interest. For this reason, space discretization is carried out with the finite element method on an unstructured triangulation. A diffusion term is designed to take into account landscape heterogeneities such as mountains and waterways. Numerical simulations are carried out for rabies epidemics among raccoons in New York state. A qualitative comparison of numerical results to available data from real-world epidemics is discussed.
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Affiliation(s)
- Joshua P. Keller
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Luca Gerardo-Giorda
- BCAM – Basque Center for Applied Mathematics, Bilbao, Spain
- Corresponding author.
| | - Alessandro Veneziani
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA
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