1
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Gonzalez G, Carr M, Kelleher TM, O'Byrne E, Banka W, Keogan B, Bennett C, Franzoni G, Keane P, Kenna C, Meredith LW, Fletcher N, Urtasun-Elizari JM, Dean J, Browne C, Lyons F, Crowley B, Igoe D, Robinson E, Martin G, Connell J, De Gascun CF, Hare D. Multiple introductions of monkeypox virus to Ireland during the international mpox outbreak, May 2022 to October 2023. Euro Surveill 2024; 29:2300505. [PMID: 38639093 PMCID: PMC11027473 DOI: 10.2807/1560-7917.es.2024.29.16.2300505] [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: 09/18/2023] [Accepted: 02/05/2024] [Indexed: 04/20/2024] Open
Abstract
BackgroundMpox, caused by monkeypox virus (MPXV), was considered a rare zoonotic disease before May 2022, when a global epidemic of cases in non-endemic countries led to the declaration of a Public Health Emergency of International Concern. Cases of mpox in Ireland, a country without previous mpox reports, could reflect extended local transmission or multiple epidemiological introductions.AimTo elucidate the origins and molecular characteristics of MPXV circulating in Ireland between May 2022 and October 2023.MethodsWhole genome sequencing of MPXV from 75% of all Irish mpox cases (182/242) was performed and compared to sequences retrieved from public databases (n = 3,362). Bayesian approaches were used to infer divergence time between sequences from different subclades and evaluate putative importation events from other countries.ResultsOf 242 detected mpox cases, 99% were males (median age: 35 years; range: 15-60). All 182 analysed genomes were assigned to Clade IIb and, presence of 12 distinguishable subclades suggests multiple introductions into Ireland. Estimation of time to divergence of subclades further supports the hypothesis for multiple importation events from numerous countries, indicative of extended and sustained international spread of mpox. Further analysis of sequences revealed that 92% of nucleotide mutations were from cytosine to thymine (or from guanine to adenine), leading to a high number of non-synonymous mutations across subclades; mutations associated with tecovirimat resistance were not observed.ConclusionWe provide insights into the international transmission dynamics supporting multiple introductions of MPXV into Ireland. Such information supported the implementation of evidence-informed public health control measures.
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Affiliation(s)
- Gabriel Gonzalez
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Japan Initiative for World-leading Vaccine Research and Development Centers, Hokkaido University, Institute for Vaccine Research and Development, Sapporo, Japan
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Michael Carr
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Tomás M Kelleher
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Emer O'Byrne
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Weronika Banka
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Brian Keogan
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Charlene Bennett
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Geraldine Franzoni
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Patrice Keane
- Department of Virology, St. James's Hospital, Dublin, Ireland
| | - Cliona Kenna
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Luke W Meredith
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Nicola Fletcher
- Centre for Experimental Pathogen Host Research, University College Dublin, Dublin, Ireland
- Veterinary Sciences Centre, University College Dublin, Dublin, Ireland
| | | | - Jonathan Dean
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Ciaran Browne
- National MPOX Crisis Management Lead, Acute Operations, Health Service Executive, Dublin, Ireland
| | - Fiona Lyons
- Sexual Health and Crisis Pregnancy Programme, Health and Wellbeing, Strategy and Research, Healthcare Strategy, Health Service Executive, Dublin, Ireland
| | - Brendan Crowley
- Department of Virology, St. James's Hospital, Dublin, Ireland
| | - Derval Igoe
- Health Service Executive Public Health: National Health Protection, Ireland
| | - Eve Robinson
- Health Protection Surveillance Centre, Dublin, Ireland
| | - Greg Martin
- Health Protection Surveillance Centre, Dublin, Ireland
| | - Jeff Connell
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Cillian F De Gascun
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Daniel Hare
- UCD National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
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2
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Marinov TT, Marinova RS, Marinov RT, Shelby N. Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19. Viruses 2023; 15:1352. [PMID: 37376651 DOI: 10.3390/v15061352] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.
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Affiliation(s)
- Tchavdar T Marinov
- Department of Natural Sciences, Southern University at New Orleans, 6801 Press Drive, New Orleans, LA 70126, USA
| | - Rossitza S Marinova
- Department of Mathematical & Physical Sciences, Concordia University of Edmonton, 7128 Ada Boulevard, Edmonton, AB T5B 4E4, Canada
- Department Computer Science, Varna Free University, 9007 Varna, Bulgaria
| | - Radoslav T Marinov
- Rescale, 33 New Montgomery Street, Suite 950, San Francisco, CA 94105, USA
| | - Nicci Shelby
- Department of Natural Sciences, Southern University at New Orleans, 6801 Press Drive, New Orleans, LA 70126, USA
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3
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Tkachenko AV, Maslov S, Wang T, Elbana A, Wong GN, Goldenfeld N. Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus, and an endemic state. eLife 2021; 10:68341. [PMID: 34747698 PMCID: PMC8670744 DOI: 10.7554/elife.68341] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 03/12/2021] [Accepted: 11/04/2021] [Indexed: 12/23/2022] Open
Abstract
It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, that is, constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models, we demonstrate the emergence of a new long timescale governing the epidemic, in broad agreement with empirical data. Our stochastic social activity model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of a long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to an endemic state.
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Affiliation(s)
- Alexei V Tkachenko
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, United States
| | - Sergei Maslov
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, United States
| | - Tong Wang
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Ahmed Elbana
- Department of Civil Engineering, University of Illinois at Urbana-Champaign, Urbana, United States
| | - George N Wong
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, United States
| | - Nigel Goldenfeld
- University of Illinois at Urbana-Champaign, Urbana, United States
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4
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Franco D, Gonzalez C, Abrego LE, Carrera JP, Diaz Y, Caicedo Y, Moreno A, Chavarria O, Gondola J, Castillo M, Valdespino E, Gaitán M, Martínez-Mandiche J, Hayer L, Gonzalez P, Lange C, Molto Y, Mojica D, Ramos R, Mastelari M, Cerezo L, Moreno L, Donnelly CA, Pascale JM, Faria NR, Lopez-Verges S, Martinez AA. Early Transmission Dynamics, Spread, and Genomic Characterization of SARS-CoV-2 in Panama. Emerg Infect Dis 2021; 27:612-615. [PMID: 33496228 PMCID: PMC7853578 DOI: 10.3201/eid2702.203767] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
We report an epidemiologic analysis of 4,210 cases of infection with severe acute respiratory syndrome coronavirus 2 and genetic analysis of 313 new near-complete virus genomes in Panama during March 9-April 16, 2020. Although containment measures reduced R0 and Rt, they did not interrupt virus spread in the country.
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5
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Kahn R, Kennedy-Shaffer L, Grad YH, Robins JM, Lipsitch M. Potential Biases Arising From Epidemic Dynamics in Observational Seroprotection Studies. Am J Epidemiol 2021; 190:328-335. [PMID: 32870977 PMCID: PMC7499481 DOI: 10.1093/aje/kwaa188] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 08/20/2020] [Accepted: 08/26/2020] [Indexed: 11/23/2022] Open
Abstract
The extent and duration of immunity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods for alleviating biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serological studies in the context of an uncontrolled or controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytical approaches to analyze the simulated data. We find that in studies assessing whether seropositivity confers protection against future infection, comparing seropositive persons with seronegative persons with similar time-dependent patterns of exposure to infection by stratifying or matching on geographic location and time of enrollment is essential in order to prevent bias.
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Affiliation(s)
| | | | | | | | - Marc Lipsitch
- Correspondence to Dr. Marc Lipsitch, Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115 (e-mail: )
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6
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Tat Dat T, Frédéric P, Hang NTT, Jules M, Duc Thang N, Piffault C, Willy R, Susely F, Lê HV, Tuschmann W, Tien Zung N. Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19. Biology (Basel) 2020; 9:E477. [PMID: 33353045 PMCID: PMC7767158 DOI: 10.3390/biology9120477] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/13/2020] [Accepted: 12/15/2020] [Indexed: 01/27/2023]
Abstract
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
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Affiliation(s)
- Tô Tat Dat
- Centre de Mathématiques Laurent-Schwartz, École Polytechnique Cour Vaneau, 91120 Palaiseau, France
| | - Protin Frédéric
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen T. T. Hang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Martel Jules
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen Duc Thang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Charles Piffault
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Rodríguez Willy
- Ecole Nationale de l’Aviation Civile, 7 Avenue Edouard Belin, 31400 Toulouse, France;
| | - Figueroa Susely
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Hông Vân Lê
- Institute of Mathematics of the Czech Academy of Sciences, Zitna 25, 11567 Praha 1, Czech Republic;
| | - Wilderich Tuschmann
- Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), Englerstr. 2, D-76131 Karlsruhe, Germany;
| | - Nguyen Tien Zung
- Institut de Mathematiques de Toulouse, Université Toulouse 3, 18 Route de Narbonne, 31400 Toulouse, France;
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7
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Singh BB, Lowerison M, Lewinson RT, Vallerand IA, Deardon R, Gill JPS, Singh B, Barkema HW. Public health interventions slowed but did not halt the spread of COVID-19 in India. Transbound Emerg Dis 2020; 68:2171-2187. [PMID: 33012088 PMCID: PMC7675717 DOI: 10.1111/tbed.13868] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [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: 06/20/2020] [Revised: 09/08/2020] [Accepted: 09/26/2020] [Indexed: 12/27/2022]
Abstract
The government of India implemented social distancing interventions to contain the COVID‐19 epidemic. However, effects of these interventions on epidemic dynamics are yet to be understood. Rates of laboratory‐confirmed COVID‐19 infections per day and effective reproduction number (Rt) were estimated for 7 periods (Pre‐lockdown, Lockdown Phases 1 to 4 and Unlock 1–2) according to nationally implemented interventions with phased relaxation. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID‐19 are presented. Daily case rates ranged from 0.03 to 285.60/10 million people across 7 discrete periods in India. From 18 May to 31 July 2020, the NCT of Delhi had the highest case rate (999/10 million people/day), whereas Madhya Pradesh had the lowest (49/10 million/day). Average Rt was 1.99 (95% CI 1.93–2.06) and 1.39 (95% CI 1.38–1.40) for the entirety of India during the period from 22 March 2020 to 17 May 2020 and from 18 May 2020 to 31 July 2020, respectively. Median mobility in India decreased in all contact domains during the period from 22 March 2020 to 17 May 2020, with the lowest being 21% in retail/recreation, except home which increased to 129% compared to the 100% baseline value. Median mobility in the ‘Grocery and Pharmacy’ returned to levels observed before 22 March 2020 in Unlock 1 and 2, and the enhanced mobility in the Pharmacy sector needs to be investigated. The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID‐19 epidemic progression in India. The identified daily COVID‐19 case rates and Rt will aid national and state governments in formulating ongoing COVID‐19 containment plans. Furthermore, these findings may inform COVID‐19 public health policy in developing countries with similar settings to India.
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Affiliation(s)
- Balbir B Singh
- School of Public Health & Zoonoses, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India.,Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.,Department of Veterinary Microbiology, University of Saskatchewan, Canada
| | - Mark Lowerison
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,One Health at UCalgary, University of Calgary, Calgary, Canada
| | - Ryan T Lewinson
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Isabelle A Vallerand
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Rob Deardon
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.,Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada
| | - Játinder P S Gill
- School of Public Health & Zoonoses, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India
| | - Baljit Singh
- One Health at UCalgary, University of Calgary, Calgary, Canada.,Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Herman W Barkema
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,One Health at UCalgary, University of Calgary, Calgary, Canada.,Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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8
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Abstract
Real epidemic spreading networks are often composed of several kinds of complex networks interconnected with each other, such as Lyme disease, and the interrelated networks may have different topologies and epidemic dynamics. Moreover, most human infectious diseases are derived from animals, and zoonotic infections always spread on directed interconnected networks. So, in this article, we consider the epidemic dynamics of zoonotic infections on a unidirectional circular-coupled network. Here, we construct two unidirectional three-layer circular interactive networks, one model has direct contact between interactive networks, the other model describes diseases transmitted through vectors between interactive networks, which are established by introducing the heterogeneous mean-field approach method. Then we obtain the basic reproduction numbers and stability of equilibria of the two models. Through mathematical analysis and numerical simulations, it is found that basic reproduction numbers of the models depend on the infection rates, infection periods, average degrees, and degree ratios. Numerical simulations illustrate and expand these theoretical results very well.
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Affiliation(s)
- Zhongpu Xu
- Department of Mathematics, Shanghai University, Shanghai, 200444 China
| | - Xinchu Fu
- Department of Mathematics, Shanghai University, Shanghai, 200444 China
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9
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Cauchemez S, Hoze N, Cousien A, Nikolay B, Ten Bosch Q. How Modelling Can Enhance the Analysis of Imperfect Epidemic Data. Trends Parasitol 2019; 35:369-379. [PMID: 30738632 PMCID: PMC7106457 DOI: 10.1016/j.pt.2019.01.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [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: 12/11/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 01/02/2023]
Abstract
Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact. Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves. An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors. In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection. Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures. Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.
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Affiliation(s)
- Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions.
| | - Nathanaël Hoze
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Anthony Cousien
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Birgit Nikolay
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
| | - Quirine Ten Bosch
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions
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10
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Abstract
In a highly interconnected world, immunizing infections are a transboundary problem, and their control and elimination require international cooperation and coordination. In the absence of a global or regional body that can impose a universal vaccination strategy, each individual country sets its own strategy. Mobility of populations across borders can promote free-riding, because a country can benefit from the vaccination efforts of its neighbours, which can result in vaccination coverage lower than the global optimum. Here we explore whether voluntary coalitions that reward countries that join by cooperatively increasing vaccination coverage can solve this problem. We use dynamic epidemiological models embedded in a game-theoretic framework in order to identify conditions in which coalitions are self-enforcing and therefore stable, and thus successful at promoting a cooperative vaccination strategy. We find that countries can achieve significantly greater vaccination coverage at a lower cost by forming coalitions than when acting independently, provided a coalition has the tools to deter free-riding. Furthermore, when economically or epidemiologically asymmetric countries form coalitions, realized coverage is regionally more consistent than in the absence of coalitions.
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Affiliation(s)
- Petra Klepac
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Itamar Megiddo
- Center for Disease Dynamics, Economics and Policy, Washington, DC 20036, USA
| | - Bryan T Grenfell
- Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ramanan Laxminarayan
- Center for Disease Dynamics, Economics and Policy, Washington, DC 20036, USA Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA Public Health Foundation of India, New Delhi 110070, India
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11
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Cauchemez S, Nouvellet P, Cori A, Jombart T, Garske T, Clapham H, Moore S, Mills HL, Salje H, Collins C, Rodriquez-Barraquer I, Riley S, Truelove S, Algarni H, Alhakeem R, AlHarbi K, Turkistani A, Aguas RJ, Cummings DA, Van Kerkhove MD, Donnelly CA, Lessler J, Fraser C, Al-Barrak A, Ferguson NM. Unraveling the drivers of MERS-CoV transmission. Proc Natl Acad Sci U S A 2016; 113:9081-6. [PMID: 27457935 DOI: 10.1073/pnas.1519235113] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.
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12
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Abstract
Recent and potential outbreaks of infectious diseases are triggering interest in predicting epidemic dynamics on a national scale and testing the efficacies of different combinations of public health policies. Network-based simulations are proving their worth as tools for addressing epidemiology and public health issues considered too complex for field investigations and questionnaire analyses. Universities and research centres are therefore using network-based simulations as teaching tools for epidemiology and public health education students, but instructors are discovering that constructing appropriate network models and epidemic simulations are difficult tasks in terms of individual movement and contact patterns. In this paper we will describe (a) a four-category framework (based on demographic and geographic properties) to discuss ways of applying network-based simulation approaches to undergraduate students and novice researchers; (b) our experiences simulating the transmission dynamics of two infectious disease scenarios in Taiwan (HIV and influenza); (c) evaluation results indicating significant improvement in student knowledge of epidemic transmission dynamics and the efficacies of various public health policy suites; and (d) a geospatial modelling approach that integrates a national commuting network as well as multi-scale contact structures.
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Affiliation(s)
- C-Y Huang
- Chang Gung University, Taoyuan, Taiwan
| | - Y-S Tsai
- National Chiao Tung University, Hsinchu, Taiwan
| | - T-H Wen
- National Taiwan University, Taipei, Taiwan
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