1
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Giovanni Maldonado Briones H, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Augusto Joaquim O, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol. PLoS One 2024; 19:e0301638. [PMID: 38913670 PMCID: PMC11195963 DOI: 10.1371/journal.pone.0301638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/15/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. METHODS To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. DISCUSSION Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
- Obianuju Genevieve Aguolu
- Division of Epidemiology, College of Public Heath, The Ohio State University, Columbus, Ohio, United States of America
| | - Moses Chapa Kiti
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Kristin Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic–Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | | | | | | | - Gifta John
- Christian Medical College Vellore, Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Sara Kim
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Holin Chen
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | - Inci Yildirim
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
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Zhang D, Britton T. An SEIR network epidemic model with manual and digital contact tracing allowing delays. Math Biosci 2024:109231. [PMID: 38914260 DOI: 10.1016/j.mbs.2024.109231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024]
Abstract
We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately notified and isolated. The early phase of the epidemic with manual and/or digital tracing is approximated by different multi-type branching processes, and three respective reproduction numbers are derived. The effectiveness of both contact tracing mechanisms is numerically quantified through the reduction of the reproduction number. This shows that app-using fraction plays an essential role in the overall effectiveness of contact tracing. The relative effectiveness of manual tracing compared to digital tracing increases if: more of the transmission occurs on the network, when the tracing delay is shortened, and when the network degree distribution is heavy-tailed. For realistic values, the combined tracing case can reduce R0 by 20%-30%, so other preventive measures are needed to reduce the reproduction number down to 1.2-1.4 for contact tracing to make it successful in avoiding big outbreaks.
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Affiliation(s)
- Dongni Zhang
- Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden.
| | - Tom Britton
- Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden
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3
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Duval A, Leclerc QJ, Guillemot D, Temime L, Opatowski L. An algorithm to build synthetic temporal contact networks based on close-proximity interactions data. PLoS Comput Biol 2024; 20:e1012227. [PMID: 38870216 PMCID: PMC11207132 DOI: 10.1371/journal.pcbi.1012227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 06/26/2024] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.
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Affiliation(s)
- Audrey Duval
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
| | - Quentin J. Leclerc
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
| | - Didier Guillemot
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- AP-HP, Paris Saclay, Department of Public Health, Medical Information, Clinical research, Garches, France
| | - Laura Temime
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
- Institut Pasteur, Conservatoire National des Arts et Métiers, Unité PACRI, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
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Kim E, Kim Y, Jin H, Lee Y, Lee H, Lee S. The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data. Front Public Health 2024; 12:1386495. [PMID: 38827618 PMCID: PMC11140122 DOI: 10.3389/fpubh.2024.1386495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.
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Affiliation(s)
- Eunmi Kim
- Institute of Mathematical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Yunhwan Kim
- College of General Education, Kookmin University, Seoul, Republic of Korea
| | - Hyeonseong Jin
- Department of Mathematics, Jeju National University, Jeju, Republic of Korea
| | - Yeonju Lee
- Division of Applied Mathematical Sciences, Korea University—Sejong, Sejong, Republic of Korea
| | - Hyosun Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Sunmi Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
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Hilliam K, Floerl O, Treml EA. Priorities for improving predictions of vessel-mediated marine invasions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171162. [PMID: 38401736 DOI: 10.1016/j.scitotenv.2024.171162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/22/2024] [Accepted: 02/19/2024] [Indexed: 02/26/2024]
Abstract
Nonindigenous marine species are impacting the integrity of marine ecosystems worldwide. The invasion rate is increasing, and vessel traffic, the most significant human-assisted transport pathway for marine organisms, is predicted to double by 2050. The ability to predict the transfer of marine species by international and domestic maritime traffic is needed to develop cost-effective proactive and reactive interventions that minimise introduction, establishment and spread of invasive species. However, despite several decades of research into vessel-mediated species transfers, some important knowledge gaps remain, leading to significant uncertainty in model predictions, often limiting their use in decision making and management planning. In this review, we discuss the sequential ecological process underlying human-assisted biological invasions and adapt it in a marine context. This process includes five successive stages: entrainment, transport, introduction, establishment, and the subsequent spread. We describe the factors that influence an organism's progression through these stages in the context of maritime vessel movements and identify key knowledge gaps that limit our ability to quantify the rate at which organisms successfully pass through these stages. We then highlight research priorities that will address these knowledge gaps and improve our capability to manage biosecurity risks at local, national and international scales. We identified four major data and knowledge gaps: (1) quantitative rates of entrainment of organisms by vessels; (2) the movement patterns of vessel types lacking maritime location devices; (3) quantifying the release (introduction) of organisms as a function of vessel behaviour (e.g. time spent at port); and (4) the influence of a species' life history on establishment success, for a given magnitude of propagule pressure. We discuss these four research priorities and how they can be addressed in collaboration with industry partners and stakeholders to improve our ability to predict and manage vessel-mediated biosecurity risks over the coming decades.
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Affiliation(s)
- Kyle Hilliam
- School of Life and Environmental Sciences, Centre for Marine Science, Deakin University, Geelong, Victoria 3220, Australia; Cawthron Institute, 98 Halifax Street East, Nelson 7010, New Zealand.
| | - O Floerl
- Cawthron Institute, 98 Halifax Street East, Nelson 7010, New Zealand; LWP Ltd, 212 Antigua Street, Christchurch 8011, New Zealand
| | - E A Treml
- School of Life and Environmental Sciences, Centre for Marine Science, Deakin University, Geelong, Victoria 3220, Australia; Australian Institute of Marine Science (AIMS) and UWA Oceans Institute, The University of Western Australia, MO96, 35 Stirling Highway, Crawley, WA 6009, Australia
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6
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McKee J, Dallas T. Structural network characteristics affect epidemic severity and prediction in social contact networks. Infect Dis Model 2024; 9:204-213. [PMID: 38293687 PMCID: PMC10824764 DOI: 10.1016/j.idm.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 11/14/2023] [Accepted: 12/27/2023] [Indexed: 02/01/2024] Open
Abstract
Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number (R0) have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators (R0 and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.
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Affiliation(s)
- Jae McKee
- Bioinnovation Program, Tulane University, New Orleans, LA, 70118, USA
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Tad Dallas
- Department of Biological Sciences, University of South Carolina, Columbia, SC, 29208, USA
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7
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Briones HGM, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Joaquim OA, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299472. [PMID: 38105989 PMCID: PMC10723497 DOI: 10.1101/2023.12.05.23299472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
| | | | - Kristin Nelson
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic – Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | | | | | | | - Gifta John
- Christian Medical College Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Georgia, USA
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Sara Kim
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Holin Chen
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | - Inci Yildirim
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
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8
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Onkhonova G, Gudymo A, Kosenko M, Marchenko V, Ryzhikov A. Quantitative measurement of influenza virus transmission in animal model: an overview of current state. Biophys Rev 2023; 15:1359-1366. [PMID: 37975001 PMCID: PMC10643727 DOI: 10.1007/s12551-023-01113-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
Influenza virus transmission is a crucial factor in understanding the spread of the virus within populations and developing effective control strategies. Studying the transmission patterns of influenza virus allows for better risk assessment and prediction of disease outbreaks. By monitoring the spread of the virus and identifying high-risk populations and geographic areas, it is possible to allocate resources more effectively, implement timely interventions, and provide targeted healthcare interventions to diminish the burden of influenza virus on vulnerable populations. Theoretical models of virus transmission are used to study and simulate of influenza virus spread within populations. These models aim to capture the complex dynamics of transmission, including factors such as population size, contact patterns, infectiousness, and susceptibility. Animal models serve as valuable tools for studying the dynamics of influenza virus transmission. This article presents a brief overview of existing research on the qualitative and quantitative study of influenza virus transmission in animal models. We discuss the methodologies employed, key insights gained from these studies, and their relevance.
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Affiliation(s)
- Galina Onkhonova
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Andrei Gudymo
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Maksim Kosenko
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Vasiliy Marchenko
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
| | - Alexander Ryzhikov
- Federal Budgetary Research Institution State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo, 630559 Russia
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9
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Pardo-Araujo M, García-García D, Alonso D, Bartumeus F. Epidemic thresholds and human mobility. Sci Rep 2023; 13:11409. [PMID: 37452118 PMCID: PMC10349094 DOI: 10.1038/s41598-023-38395-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
A comprehensive view of disease epidemics demands a deep understanding of the complex interplay between human behaviour and infectious diseases. Here, we propose a flexible modelling framework that brings conclusions about the influence of human mobility and disease transmission on early epidemic growth, with applicability in outbreak preparedness. We use random matrix theory to compute an epidemic threshold, equivalent to the basic reproduction number [Formula: see text], for a SIR metapopulation model. The model includes both systematic and random features of human mobility. Variations in disease transmission rates, mobility modes (i.e. commuting and migration), and connectivity strengths determine the threshold value and whether or not a disease may potentially establish in the population, as well as the local incidence distribution.
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Affiliation(s)
| | - David García-García
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
- Centro Nacional de Epidemiología (CNE-ISCIII), Madrid, Spain.
| | - David Alonso
- Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain
| | - Frederic Bartumeus
- Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Blanes, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Barcelona, Spain
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10
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Wilk-da-Silva R, Prist PR, Medeiros-Sousa AR, Laporta GZ, Mucci LF, Marrelli MT. The role of forest fragmentation in yellow fever virus dispersal. Acta Trop 2023:106983. [PMID: 37419378 DOI: 10.1016/j.actatropica.2023.106983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 07/09/2023]
Abstract
The intense process of deforestation in tropical forests poses serious challenges for the survival of biodiversity, as well as for the human species itself. This scenario is supported by the increase in the incidence of epidemics of zoonotic origin observed over the last few decades. In the specific case of sylvatic yellow fever (YF), it has already been shown that an increase in the transmission risk of the causative agent (yellow fever virus - YFV) is associated with areas with a high degree of forest fragmentation, which can facilitate the spread of the virus. In this study we tested the hypothesis that areas with more fragmented landscapes and a higher edge density (ED) but a high degree of connectivity between forest patches favor YFV spread. To this end, we used YF epizootics in non-human primates (NHPs) in the state of São Paulo to build direct networks, and used a multi-selection approach to analyze which landscape features could facilitate YFV spread. Our results showed that municipalities with the potential to spread the virus exhibited a higher amount of forest edge. Additionally, the models with greater empirical support showed a strong association between forest edge density and the risk of occurrence of epizootic diseases, as well as the need for a minimum threshold of native vegetation cover to restrict their transmission. These findings corroborate our hypothesis that more fragmented landscapes with a higher degree of connectivity favor the spread of YFV, while landscapes with fewer connections tend to act as dead zones for the circulation of the virus.
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Affiliation(s)
- Ramon Wilk-da-Silva
- Institute of Tropical Medicine, University of São Paulo, Av. Dr. Eneas Carvalho de Aguiar 470, São Paulo, SP, Brazil.
| | | | - Antônio Ralph Medeiros-Sousa
- Department of Epidemiology, School of Public Health, University of São Paulo, Av. Dr. Arnaldo 715, São Paulo, SP, Brazil
| | - Gabriel Zorello Laporta
- Graduate Studies, Research and Innovation Center, FMABC University Center, ABC Foundation, Av. Laure Gomes, 2000, Santo André, SP, Brazil
| | - Luis Filipe Mucci
- Institute Pasteur, São Paulo State Department of Health, PA. Cal. Victorian 23, Taubaté, SP, Brazil
| | - Mauro Toledo Marrelli
- Institute of Tropical Medicine, University of São Paulo, Av. Dr. Eneas Carvalho de Aguiar 470, São Paulo, SP, Brazil; Department of Epidemiology, School of Public Health, University of São Paulo, Av. Dr. Arnaldo 715, São Paulo, SP, Brazil
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11
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Akuno AO, Ramírez-Ramírez LL, Espinoza JF. Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico. ENTROPY (BASEL, SWITZERLAND) 2023; 25:968. [PMID: 37509915 PMCID: PMC10378648 DOI: 10.3390/e25070968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 07/30/2023]
Abstract
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.
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Affiliation(s)
- Albert Orwa Akuno
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - L Leticia Ramírez-Ramírez
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - Jesús F Espinoza
- Departamento de Matemáticas, Universidad de Sonora, Rosales y Boulevard Luis Encinas, Hermosillo C.P. 83000, Sonora, Mexico
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12
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Puspitarani GA, Fuchs R, Fuchs K, Ladinig A, Desvars-Larrive A. Network analysis of pig movement data as an epidemiological tool: an Austrian case study. Sci Rep 2023; 13:9623. [PMID: 37316653 PMCID: PMC10267221 DOI: 10.1038/s41598-023-36596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.
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Affiliation(s)
- Gavrila A Puspitarani
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria.
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria.
| | - Reinhard Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
- Institute of Systems Sciences, Innovation and Sustainability Research, University of Graz, Merangasse 18/1, 8010, Graz, Austria
| | - Klemens Fuchs
- Department for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Zinzendorfgasse 27/1, 8010, Graz, Austria
| | - Andrea Ladinig
- University Clinic for Swine, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
| | - Amélie Desvars-Larrive
- Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria
- Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria
- VetFarm, University of Veterinary Medicine Vienna, Kremesberg 13, 2563, Pottenstein, Austria
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13
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Berec L, Diviák T, Kuběna A, Levínský R, Neruda R, Suchopárová G, Šlerka J, Šmíd M, Trnka J, Tuček V, Vidnerová P, Zajíček M. On the contact tracing for COVID-19: A simulation study ☆. Epidemics 2023; 43:100677. [PMID: 36989916 PMCID: PMC10019035 DOI: 10.1016/j.epidem.2023.100677] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
Background: Contact tracing is one of the most effective non-pharmaceutical interventions in the COVID-19 pandemic. This study uses a multi-agent model to investigate the impact of four types of contact tracing strategies to prevent the spread of COVID-19. Methods: In order to analyse individual contact tracing in a reasonably realistic setup, we construct an agent-based model of a small municipality with about 60.000 inhabitants (nodes) and about 2.8 million social contacts (edges) in 30 different layers. Those layers reflect demographic, geographic, sociological and other patterns of the TTWA (Travel-to-work-area) Hodonín in Czechia. Various data sources such as census, land register, transport data or data reflecting the shopping behaviour, were employed to meet this purpose. On this multi-graph structure we run a modified SEIR model of the COVID-19 dynamics. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in the period March to June 2020. The simplest type of contact tracing follows just the family, the second tracing version tracks the family and all the work contacts, the third type finds all contacts with the family, work contacts and friends (leisure activities). The last one is a complete (digital) tracing capable of recalling any and all contacts. We evaluate the performance of these contact tracing strategies in four different environments. First, we consider an environment without any contact restrictions (benchmark); second with strict contact restriction (replicating the stringent non-pharmaceutical interventions employed in Czechia in the spring 2020); third environment, where the measures were substantially relaxed, and, finally an environment with weak contact restrictions and superspreader events (replicating the situation in Czechia in the summer 2020). Findings: There are four main findings in our paper. 1. In general, local closures are more effective than any type of tracing. 2. In an environment with strict contact restrictions there are only small differences among the four contact tracing strategies. 3. In an environment with relaxed contact restrictions the effectiveness of the tracing strategies differs substantially. 4. In the presence of superspreader events only complete contact tracing can stop the epidemic. Interpretation: In situations, where many other non-pharmaceutical interventions are in place, the specific extent of contact tracing may not have a large influence on their effectiveness. In a more relaxed setting with few contact restrictions and larger events the effectiveness of contact tracing depends heavily on their extent.
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Affiliation(s)
- Luděk Berec
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Centre for Mathematical Biology, Institute of Mathematics, Faculty of Science, University of South Bohemia, Branišovská 1760, 37005 České Budějovice, Czech Republic,Czech Academy of Sciences, Biology Centre, Institute of Entomology, Department of Ecology, Branišovská 31, 37005, České Budějovice, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Criminology and Mitchell Centre for Social Network Analysis, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Aleš Kuběna
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,CERGE-EI, Politických vězňů 7, 11121 Praha 1, Czech Republic,New Media Studies, Faculty of Arts, Charles University, Na Pří kopě 29, 110 00 Praha 1, Czech Republic
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic,Corresponding author at: The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Gabriela Suchopárová
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
| | - Martin Šmíd
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,New Media Studies, Faculty of Arts, Charles University, Na Pří kopě 29, 110 00 Praha 1, Czech Republic
| | - Jan Trnka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00 Praha 10, Czech Republic
| | - Vít Tuček
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Mathematics, University of Zagreb, Bijenička 30, 10000 Zagreb, Croatia
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Milan Zajíček
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
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14
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Michalska-Smith M, Enns EA, White LA, Gilbertson MLJ, Craft ME. The illusion of personal health decisions for infectious disease management: disease spread in social contact networks. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221122. [PMID: 36998767 PMCID: PMC10049757 DOI: 10.1098/rsos.221122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.
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Affiliation(s)
- Matthew Michalska-Smith
- Department of Ecology, Evolution and behavior, University of Minnesota, Minneapolis, MN, USA
- Department of Plant Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Eva A. Enns
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Lauren A. White
- National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, MD, USA
| | - Marie L. J. Gilbertson
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Meggan E. Craft
- Department of Ecology, Evolution and behavior, University of Minnesota, Minneapolis, MN, USA
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15
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Silva GC, Ribeiro EMS. The impact of Brazil's transport network on the spread of COVID-19. Sci Rep 2023; 13:2240. [PMID: 36755064 PMCID: PMC9906601 DOI: 10.1038/s41598-022-27139-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 12/27/2022] [Indexed: 02/10/2023] Open
Abstract
The transport network between cities is key in understanding epidemic outbreaks, especially in a vast country like Brazil with 5569 cities spread out over 8.5 million square kilometers. In order to study the COVID-19 spread in Brazil, we built a transport network where each city is a node and the edges are connections by land and air. Our findings have shown that by adding air connections, the average path length substantially decreases (70%) while the clustering coefficient remains almost unchanged, very typical of small-world networks. The airways are shortcuts connecting previously distant cities and hubs, therefore shrinking the distances in the network. Also, the cities with airports are central nodes, which makes them dissemination hotspots and key targets for interventions.
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Affiliation(s)
- Giovanna Cavali Silva
- PECE Programa de Educação Continuada, Escola Politécnica da Universidade de São Paulo, Sao Paulo, 05508-030, Brazil.
| | - Evandro Marcos Saidel Ribeiro
- Faculdade de Economia Administração e Contabilidade de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, 14040-905, Brazil
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16
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Davies K, Lenhart S, Day J, Lloyd AL, Lanzas C. Extensions of mean-field approximations for environmentally-transmitted pathogen networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1637-1673. [PMID: 36899502 DOI: 10.3934/mbe.2023075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Many pathogens spread via environmental transmission, without requiring host-to-host direct contact. While models for environmental transmission exist, many are simply constructed intuitively with structures analogous to standard models for direct transmission. As model insights are generally sensitive to the underlying model assumptions, it is important that we are able understand the details and consequences of these assumptions. We construct a simple network model for an environmentally-transmitted pathogen and rigorously derive systems of ordinary differential equations (ODEs) based on different assumptions. We explore two key assumptions, namely homogeneity and independence, and demonstrate that relaxing these assumptions can lead to more accurate ODE approximations. We compare these ODE models to a stochastic implementation of the network model over a variety of parameters and network structures, demonstrating that with fewer restrictive assumptions we are able to achieve higher accuracy in our approximations and highlighting more precisely the errors produced by each assumption. We show that less restrictive assumptions lead to more complicated systems of ODEs and the potential for unstable solutions. Due to the rigour of our derivation, we are able to identify the reason behind these errors and propose potential resolutions.
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Affiliation(s)
- Kale Davies
- Department of Mathematics, University of Chicago, Chicago, IL, USA
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Judy Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | - Alun L Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| | - Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
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17
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Epidemic dynamics in census-calibrated modular contact network. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023; 12:14. [PMID: 36685658 PMCID: PMC9838429 DOI: 10.1007/s13721-022-00402-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/11/2023]
Abstract
Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.
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18
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Hall CL, Siebert BA. Exact solutions and bounds for network SIR and SEIR models using a rooted-tree approximation. J Math Biol 2023; 86:22. [PMID: 36625970 PMCID: PMC9832100 DOI: 10.1007/s00285-022-01854-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/18/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023]
Abstract
In this paper, we develop a new node-based approximate model to describe contagion dynamics on networks. We prove that our approximate model is exact for Markovian SIR (susceptible-infectious-recovered) and SEIR (susceptible-exposed-infectious-recovered) dynamics on tree graphs with a single source of infection, and that the model otherwise gives upper bounds on the probabilities of each node being susceptible. Our analysis of SEIR contagion dynamics is general to SEIR models with arbitrarily many classes of exposed/latent state. In all cases of a tree graph with a single source of infection, our approach yields a system of linear differential equations that exactly describes the evolution of node-state probabilities; we use this to state explicit closed-form solutions for an SIR model on a tree. For more general networks, our approach yields a cooperative system of differential equations that can be used to bound the true solution.
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19
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Network diffusion of gender diversity on boards: A process of two-speed opposing forces. PLoS One 2022; 17:e0277214. [DOI: 10.1371/journal.pone.0277214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/18/2022] [Indexed: 11/15/2022] Open
Abstract
Network diffusion processes or how information spreads through networks have been widely examined in numerous disciplines such as epidemiology, physics, sociology, politics, or computer science. In this paper, we extend previous developments by considering a generalization of the diffusion by considering the possibility of differences in the speed of diffusion and reduction depending on the forces’ directions. In this situation, the differential speed of diffusion produces deviations from the standard solution around the average of the initial conditions in the network. In fact, this asymmetry gives rise to non-linear dynamics in which, contrary to the symmetric case, the final solution depends on the topology of the graph as well as on the distribution of the initial values. Counter-intuitively, less central nodes in the network are able to exert a higher influence on the final solution. This behavior applies also for different simulated networks such as random, small-world, and scale-free. We show an example of this kind of asymmetric diffusion process in a real case. To do so, we use a network of US Boards of Directors, where boards are the nodes and the directors working for more than one board, are the links. Changes in the proportion of women serving on each board are influenced by the gradient between adjacent boards. We also show that there is an asymmetry: the gradient is reduced at a slower (faster) rhythm if the board has less (more) women than neighboring boards. We are able to quantify the accumulated effect of this asymmetry from 2000 to 2015 in the overall proportion of women on boards, in a 4.7 percentage points (the proportion should have been an 14.61% instead of the observed 9.93% in 2015).
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20
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Cristóbal T, Quesada-Arencibia A, de Blasio GS, Padrón G, Alayón F, García CR. Data mining methodology for obtaining epidemiological data in the context of road transport systems. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:9253-9275. [PMID: 36212894 PMCID: PMC9525233 DOI: 10.1007/s12652-022-04427-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 09/14/2022] [Indexed: 06/08/2023]
Abstract
Millions of people use public transport systems daily, hence their interest for the epidemiology of respiratory infectious diseases, both from a scientific and a health control point of view. This article presents a methodology for obtaining epidemiological information on these types of diseases in the context of a public road transport system. This epidemiological information is based on an estimation of interactions with risk of infection between users of the public transport system. The methodology is novel in its aim since, to the best of our knowledge, there is no previous study in the context of epidemiology and public transport systems that addresses this challenge. The information is obtained by mining the data generated from trips made by transport users who use contactless cards as a means of payment. Data mining therefore underpins the methodology. One achievement of the methodology is that it is a comprehensive approach, since, starting from a formalisation of the problem based on epidemiological concepts and the transport activity itself, all the necessary steps to obtain the required epidemiological knowledge are described and implemented. This includes the estimation of data that are generally unknown in the context of public transport systems, but that are required to generate the desired results. The outcome is useful epidemiological data based on a complete and reliable description of all estimated potentially infectious interactions between users of the transport system. The methodology can be implemented using a variety of initial specifications: epidemiological, temporal, geographic, inter alia. Another feature of the methodology is that with the information it provides, epidemiological studies can be carried out involving a large number of people, producing large samples of interactions obtained over long periods of time, thereby making it possible to carry out comparative studies. Moreover, a real use case is described, in which the methodology is applied to a road transport system that annually moves around 20 million passengers, in a period that predates the COVID-19 pandemic. The results have made it possible to identify the group of users most exposed to infection, although they are not the largest group. Finally, it is estimated that the application of a seat allocation strategy that minimises the risk of infection reduces the risk by 50%.
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Affiliation(s)
- Teresa Cristóbal
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
| | - Alexis Quesada-Arencibia
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
| | - Gabriele Salvatore de Blasio
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
| | - Gabino Padrón
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
| | - Francisco Alayón
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
| | - Carmelo R. García
- Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas de Gran Canaria, Spain
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21
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Health behavior homophily can mitigate the spread of infectious diseases in small-world networks. Soc Sci Med 2022; 312:115350. [PMID: 36183539 DOI: 10.1016/j.socscimed.2022.115350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/14/2022] [Accepted: 09/01/2022] [Indexed: 11/23/2022]
Abstract
Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.
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22
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Rise of the Metaverse’s Immersive Virtual Reality Malware and the Man-in-the-Room Attack & Defenses. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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23
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Miró Pina V, Nava-Trejo J, Tóbiás A, Nzabarushimana E, González-Casanova A, González-Casanova I. The role of connectivity on COVID-19 preventive approaches. PLoS One 2022; 17:e0273906. [PMID: 36048855 PMCID: PMC9436065 DOI: 10.1371/journal.pone.0273906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/17/2022] [Indexed: 11/19/2022] Open
Abstract
Preventive and modeling approaches to address the COVID-19 pandemic have been primarily based on the age or occupation, and often disregard the importance of heterogeneity in population contact structure and individual connectivity. To address this gap, we developed models based on Erdős-Rényi and a power law degree distribution that first incorporate the role of heterogeneity and connectivity and then can be expanded to make assumptions about demographic characteristics. Results demonstrate that variations in the number of connections of individuals within a population modify the impact of public health interventions such as lockdown or vaccination approaches. We conclude that the most effective strategy will vary depending on the underlying contact structure of individuals within a population and on timing of the interventions.
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Affiliation(s)
- Verónica Miró Pina
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- National Autonomous University of Mexico (UNAM), Mexico, Mexico
| | | | | | | | | | - Inés González-Casanova
- Indiana University Bloomington, Bloomington, Indiana, United States of America
- * E-mail: (AG-C); (IG-C)
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24
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Hâncean MG, Lerner J, Perc M, Oană I, Bunaciu DA, Stoica AA, Ghiţă MC. Occupations and their impact on the spreading of COVID-19 in urban communities. Sci Rep 2022; 12:14115. [PMID: 35982107 PMCID: PMC9387884 DOI: 10.1038/s41598-022-18392-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 08/10/2022] [Indexed: 11/09/2022] Open
Abstract
The current pandemic has disproportionally affected the workforce. To improve our understanding of the role that occupations play in the transmission of COVID-19, we analyse real-world network data that were collected in Bucharest between August 1st and October 31st 2020. The data record sex, age, and occupation of 6895 patients and the 13,272 people they have interacted with, thus providing a social network from an urban setting through which COVID-19 has spread. Quite remarkably, we find that medical occupations have no significant effect on the spread of the virus. Instead, we find common transmission chains to start with infected individuals who hold jobs in the private sector and are connected with non-active alters, such as spouses, siblings, or elderly relatives. We use relational hyperevent models to assess the most likely homophily and network effects in the community transmission. We detect homophily with respect to age and anti-homophily with respect to sex and employability. We note that, although additional data would be welcomed to perform more in-depth network analyses, our findings may help public authorities better target under-performing vaccination campaigns.
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Affiliation(s)
- Marian-Gabriel Hâncean
- Department of Sociology, University of Bucharest, Panduri 90-92, 050663, Bucharest, Romania.
| | - Jürgen Lerner
- Department of Computer and Information Science, University of Konstanz, 78457, Konstanz, Germany.,Human Technology Center, RWTH Aachen University, 52062, Aachen, Germany
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000, Maribor, Slovenia.,Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 404332, Taiwan.,Alma Mater Europaea, Slovenska ulica 17, 2000, Maribor, Slovenia.,Complexity Science Hub Vienna, Josefstädterstraße 39, 1080, Vienna, Austria
| | - Iulian Oană
- Department of Sociology, University of Bucharest, Panduri 90-92, 050663, Bucharest, Romania
| | - David-Andrei Bunaciu
- Department of Sociology, University of Bucharest, Panduri 90-92, 050663, Bucharest, Romania
| | | | - Maria-Cristina Ghiţă
- Department of Sociology, University of Bucharest, Panduri 90-92, 050663, Bucharest, Romania
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25
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Okamoto KW, Ong V, Wallace R, Wallace R, Chaves LF. When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses? NONLINEAR DYNAMICS 2022; 111:927-949. [PMID: 35757097 PMCID: PMC9207439 DOI: 10.1007/s11071-022-07548-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/05/2022] [Indexed: 06/15/2023]
Abstract
Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-022-07548-7.
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Affiliation(s)
- Kenichi W. Okamoto
- Department of Biology, University of St. Thomas, St. Paul, MN 55105 USA
- Agroecology and Rural Economics Research Corps, St. Paul, MN USA
| | - Virakbott Ong
- Department of Biology, University of St. Thomas, St. Paul, MN 55105 USA
| | - Robert Wallace
- Agroecology and Rural Economics Research Corps, St. Paul, MN USA
| | | | - Luis Fernando Chaves
- Instituto Conmemorativo Gorgas de Estudios de la Salud (ICGES), Avenida Justo Arosemena, Panama, Panama
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26
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Zerenner T, Di Lauro F, Dashti M, Berthouze L, Kiss IZ. Probabilistic predictions of SIS epidemics on networks based on population-level observations. Math Biosci 2022; 350:108854. [PMID: 35659615 DOI: 10.1016/j.mbs.2022.108854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 05/17/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022]
Abstract
We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erdős-Rényi and Barabási-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data at population-level, which is always likely to be available. This, combined with its numerical efficiency, makes the proposed method attractive to be used either as a standalone inference and prediction scheme or in conjunction with other inference and/or predictive models.
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Affiliation(s)
- T Zerenner
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
| | - F Di Lauro
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK; Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FL, UK
| | - M Dashti
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK
| | - L Berthouze
- Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QH, UK
| | - I Z Kiss
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
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27
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Ma W, Zhang P, Zhao X, Xue L. The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks. PHYSICA A 2022; 588:126558. [PMID: 34744294 PMCID: PMC8559433 DOI: 10.1016/j.physa.2021.126558] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/22/2021] [Indexed: 06/01/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold.
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Affiliation(s)
- Weicai Ma
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Peng Zhang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xin Zhao
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Leyang Xue
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
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28
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Michalska-Smith M, Song Z, Spawn-Lee SA, Hansen ZA, Johnson M, May G, Borer ET, Seabloom EW, Kinkel LL. Network structure of resource use and niche overlap within the endophytic microbiome. THE ISME JOURNAL 2022; 16:435-446. [PMID: 34413476 PMCID: PMC8776778 DOI: 10.1038/s41396-021-01080-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/28/2021] [Accepted: 07/27/2021] [Indexed: 02/07/2023]
Abstract
Endophytes often have dramatic effects on their host plants. Characterizing the relationships among members of these communities has focused on identifying the effects of single microbes on their host, but has generally overlooked interactions among the myriad microbes in natural communities as well as potential higher-order interactions. Network analyses offer a powerful means for characterizing patterns of interaction among microbial members of the phytobiome that may be crucial to mediating its assembly and function. We sampled twelve endophytic communities, comparing patterns of niche overlap between coexisting bacteria and fungi to evaluate the effect of nutrient supplementation on local and global competitive network structure. We found that, despite differences in the degree distribution, there were few significant differences in the global network structure of niche-overlap networks following persistent nutrient amendment. Likewise, we found idiosyncratic and weak evidence for higher-order interactions regardless of nutrient treatment. This work provides a first-time characterization of niche-overlap network structure in endophytic communities and serves as a framework for higher-resolution analyses of microbial interaction networks as a consequence and a cause of ecological variation in microbiome function.
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Affiliation(s)
- Matthew Michalska-Smith
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA.
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA.
| | - Zewei Song
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA
| | - Seth A Spawn-Lee
- Department of Geography, University of Wisconsin, Madison, WI, USA
- Center for Sustainability and the Global Environment (SAGE), University of Wisconsin, Madison, WI, USA
| | - Zoe A Hansen
- Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI, USA
| | - Mitch Johnson
- Department of Horticultural Science, University of Minnesota, St Paul, MN, USA
| | - Georgiana May
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Elizabeth T Borer
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Eric W Seabloom
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Linda L Kinkel
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA
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29
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Nunner H, van de Rijt A, Buskens V. Prioritizing high-contact occupations raises effectiveness of vaccination campaigns. Sci Rep 2022; 12:737. [PMID: 35031651 PMCID: PMC8760242 DOI: 10.1038/s41598-021-04428-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
A twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.
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Affiliation(s)
- Hendrik Nunner
- Department of Sociology/ICS, Utrecht University, Utrecht, The Netherlands.
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands.
| | - Arnout van de Rijt
- Department of Political and Social Sciences, European University Institute, Florence, Italy
| | - Vincent Buskens
- Department of Sociology/ICS, Utrecht University, Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Utrecht, The Netherlands
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30
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Schweinberger M, Bomiriya RP, Babkin S. A Semiparametric Bayesian Approach to Epidemics, with Application to the Spread of the Coronavirus MERS in South Korea in 2015. J Nonparametr Stat 2022; 34:628-662. [PMID: 36172077 PMCID: PMC9512273 DOI: 10.1080/10485252.2021.1972294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modeling framework with at least three advantages. First, it enables researchers to study the structure of a population contact network and its impact on the spread of infectious diseases. Second, it can accommodate short- and long-tailed degree distributions and detect potential superspreaders, who represent an important public health concern. Third, it addresses the important issue of incomplete data. Starting from first principles, we show when the incomplete-data generating process is ignorable for the purpose of Bayesian inference for the parameters of the population model. We demonstrate the semiparametric modeling framework by simulations and an application to the partially observed MERS epidemic in South Korea in 2015. We conclude with an extended discussion of open questions and directions for future research.
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Affiliation(s)
- Michael Schweinberger
- Corresponding author. Address: Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USA. . Phone: + 1 713-348-2278. Fax: +1 713-348-5476
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31
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Kuchler T, Russel D, Stroebel J. JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook. JOURNAL OF URBAN ECONOMICS 2022; 127:103314. [PMID: 35250112 PMCID: PMC8886493 DOI: 10.1016/j.jue.2020.103314] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/11/2020] [Indexed: 05/05/2023]
Abstract
We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
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Affiliation(s)
- Theresa Kuchler
- New York University, Stern School of Business, NBER, and CEPR
| | - Dominic Russel
- New York University, Stern School of Business, NBER, and CEPR
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32
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Sah P, Otterstatter M, Leu ST, Leviyang S, Bansal S. Revealing mechanisms of infectious disease spread through empirical contact networks. PLoS Comput Biol 2021; 17:e1009604. [PMID: 34928936 PMCID: PMC8758098 DOI: 10.1371/journal.pcbi.1009604] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/13/2022] [Accepted: 10/31/2021] [Indexed: 11/28/2022] Open
Abstract
The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints. Network models are widely used to understand and predict infectious disease spread in human and animal populations. However, the choice of network model often relies on subjective expert knowledge or disease transmission experiments that are time-consuming and difficult to perform. We developed a novel tool, called INoDS (Identifying contact Networks of infectious Disease Spread), that uses robust statistical approach to establish relevance of a network model in explaining transmission pathways of an infectious disease outbreak. We used computer simulations and real-world dataset to test the accuracy of our tool and robustness to missing data. We found that INoDS is robust to common data-collection constraints, broadly applicable and accurate compared to current approaches. The tool that we have developed can therefore provide immediate epidemiological insights in the event of an epidemic outbreak, and can be used to improve targeted disease control.
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Affiliation(s)
- Pratha Sah
- Department of Biology, Georgetown University, Washington, District of Columbia, United States of America
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Stephan T. Leu
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, Australia
| | - Sivan Leviyang
- Department of Mathematics & Statistics, Georgetown University, Washington, District of Columbia, United States of America
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, District of Columbia, United States of America
- * E-mail:
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33
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Meng L, Masuda N. Epidemic dynamics on metapopulation networks with node2vec mobility. J Theor Biol 2021; 534:110960. [PMID: 34774664 DOI: 10.1016/j.jtbi.2021.110960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/02/2021] [Accepted: 11/07/2021] [Indexed: 11/29/2022]
Abstract
Metapopulation models have been a powerful tool for both theorizing and simulating epidemic dynamics. In a metapopulation model, one considers a network composed of subpopulations and their pairwise connections, and individuals are assumed to migrate from one subpopulation to another obeying a given mobility rule. While how different mobility rules affect epidemic dynamics in metapopulation models has been studied, there have been relatively few efforts on comparison of the effects of simple (i.e., unbiased) random walks and more complex mobility rules. Here we study a susceptible-infectious-susceptible (SIS) dynamics in a metapopulation model in which individuals obey a parametric second-order random-walk mobility rule called the node2vec. We map the second-order mobility rule of the node2vec to a first-order random walk in a network whose each node is a directed edge connecting a pair of subpopulations and then derive the epidemic threshold. For various networks, we find that the epidemic threshold is large (therefore, epidemic spreading tends to be suppressed) when the individuals infrequently backtrack or infrequently visit the common neighbors of the currently visited and the last visited subpopulations than when the individuals obey the simple random walk. The amount of change in the epidemic threshold induced by the node2vec mobility is in general not as large as, but is sometimes comparable with, the one induced by the change in the diffusion rate for individuals.
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Affiliation(s)
- Lingqi Meng
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA; Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY 14260-5030, USA; Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
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34
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Inferring the effect of interventions on COVID-19 transmission networks. Sci Rep 2021; 11:21913. [PMID: 34754025 PMCID: PMC8578219 DOI: 10.1038/s41598-021-01407-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory.
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35
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Morrison RE, Mushimiyimana Y, Stoinski TS, Eckardt W. Rapid transmission of respiratory infections within but not between mountain gorilla groups. Sci Rep 2021; 11:19622. [PMID: 34620899 PMCID: PMC8497490 DOI: 10.1038/s41598-021-98969-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/09/2021] [Indexed: 02/06/2023] Open
Abstract
Minimizing disease transmission between humans and wild apes and controlling outbreaks in ape populations is vital to both ape conservation and human health, but information on the transmission of real infections in wild populations is rare. We analyzed respiratory outbreaks in a subpopulation of wild mountain gorillas (Gorilla beringei beringei) between 2004 and 2020. We investigated transmission within groups during 7 outbreaks using social networks based on contact and proximity, and transmission between groups during 15 outbreaks using inter-group encounters, transfers and home range overlap. Patterns of contact and proximity within groups were highly predictable based on gorillas' age and sex. Disease transmission within groups was rapid with a median estimated basic reproductive number (R0) of 4.18 (min = 1.74, max = 9.42), and transmission was not predicted by the social network. Between groups, encounters and transfers did not appear to have enabled disease transmission and the overlap of groups' ranges did not predict concurrent outbreaks. Our findings suggest that gorilla social structure, with many strong connections within groups and weak ties between groups, may enable rapid transmission within a group once an infection is present, but limit the transmission of infections between groups.
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Affiliation(s)
- Robin E Morrison
- Dian Fossey Gorilla Fund, Musanze, Rwanda.
- Centre for Research in Animal Behavior, University of Exeter, Exeter, UK.
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Nunner H, Buskens V, Kretzschmar M. A model for the co-evolution of dynamic social networks and infectious disease dynamics. COMPUTATIONAL SOCIAL NETWORKS 2021; 8:19. [PMID: 34642614 PMCID: PMC8495675 DOI: 10.1186/s40649-021-00098-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 08/29/2021] [Indexed: 12/12/2022]
Abstract
Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.
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Affiliation(s)
- Hendrik Nunner
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Vincent Buskens
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Mirjam Kretzschmar
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands
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Hüttel FB, Iversen AM, Bo Hansen M, Kjær Ersbøll B, Ellermann-Eriksen S, Lundtorp Olsen N. Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes. PLoS One 2021; 16:e0257684. [PMID: 34543324 PMCID: PMC8452062 DOI: 10.1371/journal.pone.0257684] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 09/07/2021] [Indexed: 02/05/2023] Open
Abstract
Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.
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Maciejewski W. Teaching math in real time. EDUCATIONAL STUDIES IN MATHEMATICS 2021; 108:143-159. [PMID: 34934239 PMCID: PMC8387551 DOI: 10.1007/s10649-021-10090-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 06/14/2023]
Abstract
Narrative, first-person accounts of a collective, traumatic event preserve the authenticity of the experience and defend against inaccurate retrospective idealizations. Such artifacts allow us time to process the event, extract the lessons it has for us, and to bring these lessons to bear on our practices. I offer my own narrative here, as a practitioner and researcher, of daily experiences of teaching mathematics in the USA during the SARS-CoV-2 pandemic. Sudden perturbations to our regular educational practices expose ways in which those practices are incomplete or outright unstable. This, in turn, troubles the theories underpinning our practices. I offer my narrative as a point of communal reflection on what we do and know, and how we might do and know it better.
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Kuo PF, Chiu CS. Airline transportation and arrival time of international disease spread: A case study of Covid-19. PLoS One 2021; 16:e0256398. [PMID: 34411198 PMCID: PMC8375981 DOI: 10.1371/journal.pone.0256398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/06/2021] [Indexed: 11/18/2022] Open
Abstract
In this era of globalization, airline transportation has greatly increased international trade and travel within the World Airport Network (WAN). Unfortunately, this convenience has expanded the scope of infectious disease spread from a local to a worldwide occurrence. Thus, scholars have proposed several methods to measure the distances between airports and define the relationship between the distances and arrival times of infectious diseases in various countries. However, such studies suffer from the following limitations. (1) Only traditional statistical methods or graphical representations were utilized to show that the effective distance performed better than the geographical distance technique. Researchers seldom use the survival model to quantify the actual differences among arrival times via various distance methods. (2) Although scholars have found that most diseases tend to spread via the random walk rather than the shortest path method, this hypothesis may no longer be true because the network has been severally altered due to recent COVID-related travel reductions. Therefore, we used 2017 IATA (International Air Transport Association) to establish an airline network via various chosen path strategies (random walk and shortest path). Then, we employed these two networks to quantify each model's predictive performance in order to estimate the importation probability function of COVID-19 into various countries. The effective distance model was found to more accurately predict arrival dates of COVID-19 than the geographical distance model. However, if pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to recent flight suspensions and travel restrictions during the epidemic. Lastly, when testing effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. The results can help authorities design more effective international epidemic prevention and control strategies.
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Affiliation(s)
- Pei-Fen Kuo
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | - Chui-Sheng Chiu
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
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40
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Ruiz-Herrera A, Torres PJ. The Role of Movement Patterns in Epidemic Models on Complex Networks. Bull Math Biol 2021; 83:98. [PMID: 34410514 PMCID: PMC8376740 DOI: 10.1007/s11538-021-00929-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 07/21/2021] [Indexed: 12/29/2022]
Abstract
In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.
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Affiliation(s)
- Alfonso Ruiz-Herrera
- Department of Mathematics, Faculty of Science, University of Oviedo, Oviedo, Spain.
| | - Pedro J Torres
- Department of Applied Mathematics, Faculty of Science, University of Granada, Granada, Spain
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41
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Mann P, Smith VA, Mitchell JBO, Dobson S. Two-pathogen model with competition on clustered networks. Phys Rev E 2021; 103:062308. [PMID: 34271633 DOI: 10.1103/physreve.103.062308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
Networks provide a mathematically rich framework to represent social contacts sufficient for the transmission of disease. Social networks are often highly clustered and fail to be locally treelike. In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross-immunity coupling, deriving conditions for the threshold of coexistence of the second strain. We show that clustering reduces the coexistence threshold of the second strain and its outbreak size in Poisson networks, while exhibiting the opposite effects on uniform-degree models. We conclude that clustering within a population must increase the ability of the second wave of an epidemic to spread over a network. We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph at two distinct transmissibilities.
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Affiliation(s)
- Peter Mann
- School of Computer Science, University of St Andrews, St Andrews, Fife KY16 9SX, United Kingdom.,EaStCHEM School of Chemistry & BSRC, University of St Andrews, St Andrews, Fife KY16 9ST, United Kingdom.,School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom
| | - V Anne Smith
- School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom
| | - John B O Mitchell
- EaStCHEM School of Chemistry & BSRC, University of St Andrews, St Andrews, Fife KY16 9ST, United Kingdom
| | - Simon Dobson
- School of Computer Science, University of St Andrews, St Andrews, Fife KY16 9SX, United Kingdom
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Hu Y, Guo J, Li G, Lu X, Li X, Zhang Y, Cong L, Kang Y, Jia X, Shi X, Xie G, Zhang L. Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study. BMJ Open 2021; 11:e045886. [PMID: 34233974 PMCID: PMC8266432 DOI: 10.1136/bmjopen-2020-045886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated. SETTING We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a 'T' compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed. PRIMARY AND SECONDARY OUTCOME MEASURES Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people. RESULTS (1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate. CONCLUSIONS Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.
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Affiliation(s)
- Yiying Hu
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Jianying Guo
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Guanqiao Li
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
- Tsinghua Clinical Research Institute (TCRI), School of Medicine, Tsinghua University, Beijing, China
| | - Xi Lu
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
| | - Xiang Li
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Yuan Zhang
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Lin Cong
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Yanni Kang
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Xiaoyu Jia
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
| | - Xuanling Shi
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
| | - Guotong Xie
- Ping An Healthcare Technology, Ping An Insurance Group Company of China, Shenzhen, China
- Ping An Health Cloud Company, Ping An Insurance Group Company of China, Shenzhen, China
- Ping An International Smart City Technology Co., Ltd, Ping An Insurance Group Company of China, Shenzhen, China
| | - Linqi Zhang
- School of Medicine and Vanke School of Public Health Beijing, Tsinghua University, Beijing, China
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Okello WO, Amongi CA, Muhanguzi D, MacLeod ET, Waiswa C, Shaw AP, Welburn SC. Livestock Network Analysis for Rhodesiense Human African Trypanosomiasis Control in Uganda. Front Vet Sci 2021; 8:611132. [PMID: 34262958 PMCID: PMC8273440 DOI: 10.3389/fvets.2021.611132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness. Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (R o 1.287), hence regarded as "slow" pathogen, and evaluate the effects of disease interventions. Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network. Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public-private partnerships, would serve to limit its spread.
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Affiliation(s)
- Walter O Okello
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Commonwealth and Scientific Research Organization, Land & Water Business Unit, Canberra, ACT, Australia
| | - Christine A Amongi
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Dennis Muhanguzi
- Biotechnical and Laboratory Sciences, Department of Biomolecular and Biolaboratory Sciences, School of Biosecurity, College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Ewan T MacLeod
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Charles Waiswa
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Biotechnical and Laboratory Sciences, Department of Biomolecular and Biolaboratory Sciences, School of Biosecurity, College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda.,The Coordinating Office for Control of Trypanosomiasis in Uganda (COCTU), Kampala, Uganda
| | - Alexandra P Shaw
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Avia-GIS, Zoersel, Belgium
| | - Susan C Welburn
- Infection Medicine, Biomedical Sciences, Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.,Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
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Shepelev IA, Muni SS, Schöll E, Strelkova GI. Repulsive inter-layer coupling induces anti-phase synchronization. CHAOS (WOODBURY, N.Y.) 2021; 31:063116. [PMID: 34241296 DOI: 10.1063/5.0054770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/25/2021] [Indexed: 06/13/2023]
Abstract
We present numerical results for the synchronization phenomena in a bilayer network of repulsively coupled 2D lattices of van der Pol oscillators. We consider the cases when the network layers have either different or the same types of intra-layer coupling topology. When the layers are uncoupled, the lattice of van der Pol oscillators with a repulsive interaction typically demonstrates a labyrinth-like pattern, while the lattice with attractively coupled van der Pol oscillators shows a regular spiral wave structure. We reveal for the first time that repulsive inter-layer coupling leads to anti-phase synchronization of spatiotemporal structures for all considered combinations of intra-layer coupling. As a synchronization measure, we use the correlation coefficient between the symmetrical pairs of network nodes, which is always close to -1 in the case of anti-phase synchronization. We also study how the form of synchronous structures depends on the intra-layer coupling strengths when the repulsive inter-layer coupling is varied.
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Affiliation(s)
- Igor A Shepelev
- Institute of Physics, Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia
| | - Sishu S Muni
- School of Fundamental Sciences, Massey University, Palmerston North 4410, New Zealand
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Galina I Strelkova
- Institute of Physics, Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia
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Pina VM, Nava-Trejo J, Tóbiás A, Nzabarushimana E, González-Casanova A, González-Casanova I. The role of connectivity on COVID-19 preventive approaches. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.03.11.21253348. [PMID: 33758876 PMCID: PMC7987035 DOI: 10.1101/2021.03.11.21253348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Preventive and modelling approaches to address the COVID-19 pandemic have been primarily based on the age or occupation, and often disregard the importance of heterogeneity in population contact structure and individual connectivity. To address this gap, we developed models based on Erdős-Rényi and a power law degree distribution that first incorporate the role of heterogeneity and connectivity and then can be expanded to make assumptions about demographic characteristics. Results demonstrate that variations in the number of connections of individuals within a population modify the impact of public health interventions such as lockdown or vaccination approaches. We conclude that the most effective strategy will vary depending on the underlying contact structure of individuals within a population and on timing of the interventions.
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Affiliation(s)
- V. Miró Pina
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- National Autonomous University of Mexico (UNAM)
| | | | - A. Tóbiás
- Technische Universität Berlin, Berlin, Germany
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Helms YB, Hamdiui N, Eilers R, Hoebe C, Dukers-Muijrers N, van den Kerkhof H, Timen A, Stein ML. Online respondent-driven detection for enhanced contact tracing of close-contact infectious diseases: benefits and barriers for public health practice. BMC Infect Dis 2021; 21:358. [PMID: 33863279 PMCID: PMC8051831 DOI: 10.1186/s12879-021-06052-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 04/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Online respondent-driven detection (RDD) is a novel method of case finding that can enhance contact tracing (CT). However, the advantages and challenges of RDD for CT have not yet been investigated from the perspective of public health professionals (PHPs). Therefore, it remains unclear if, and under what circumstances, PHPs are willing to apply RDD for CT. METHODS Between March and April 2019, we conducted semi-structured interviews with Dutch PHPs responsible for CT in practice. Questions were derived from the 'diffusion of innovations' theory. Between May and June 2019, we distributed an online questionnaire among 260 Dutch PHPs to quantify the main qualitative findings. Using different hypothetical scenarios, we assessed anticipated advantages and challenges of RDD, and PHPs' intention to apply RDD for CT. RESULTS Twelve interviews were held, and 70 PHPs completed the online questionnaire. A majority of questionnaire respondents (71%) had a positive intention towards using RDD for CT. Anticipated advantages of RDD were 'accommodating easy and autonomous participation in CT of index cases and contact persons', and 'reaching contact persons more efficiently'. Anticipated challenges were 'limited opportunities for PHPs to support, motivate, and coordinate the execution of CT', 'not being able to adequately convey measures to index cases and contact persons', and 'anticipated unrest among index cases and contact persons'. Circumstances under which PHPs anticipated RDD applicable for CT included index cases and contact persons being reluctant to share information directly with PHPs, digitally skilled and literate persons being involved, and large scale CT. Circumstances under which PHPs anticipated RDD less applicable for CT included severe consequences of missing information or contact persons for individual or public health, involvement of complex or impactful measures for index cases and contact persons, and a disease being perceived as severe or sensitive by index cases and their contact persons. CONCLUSIONS PHPs generally perceived RDD as a potentially beneficial method for public health practice, that may help overcome challenges present in traditional CT, and could be used during outbreaks of infectious diseases that spread via close contact. The circumstances under which CT is performed, appear to strongly influence PHPs' intention to use RDD for CT.
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Affiliation(s)
- Yannick B Helms
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Nora Hamdiui
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Department of Primary and Community Care, Radboud University Medical Centre, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Renske Eilers
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Christian Hoebe
- Department of Sexual Health, Infectious Diseases, and Environmental Health, South Limburg Public Health Service, Heerlen, The Netherlands
- Department of Social Medicine and Medical Microbiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Nicole Dukers-Muijrers
- Department of Sexual Health, Infectious Diseases, and Environmental Health, South Limburg Public Health Service, Heerlen, The Netherlands
- Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Hans van den Kerkhof
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Aura Timen
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Athena Institute for Research on Innovation and Communication in Health and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands
| | - Mart L Stein
- National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
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47
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Kim Y, Métras R, Dommergues L, Youssouffi C, Combo S, Le Godais G, Pfeiffer DU, Cêtre-Sossah C, Cardinale E, Filleul L, Youssouf H, Subiros M, Fournié G. The role of livestock movements in the spread of Rift Valley fever virus in animals and humans in Mayotte, 2018-19. PLoS Negl Trop Dis 2021; 15:e0009202. [PMID: 33684126 PMCID: PMC7939299 DOI: 10.1371/journal.pntd.0009202] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/03/2021] [Indexed: 11/18/2022] Open
Abstract
Rift Valley fever (RVF) is a vector-borne viral disease of major animal and public health importance. In 2018–19, it caused an epidemic in both livestock and human populations of the island of Mayotte. Using Bayesian modelling approaches, we assessed the spatio-temporal pattern of RVF virus (RVFV) infection in livestock and human populations across the island, and factors shaping it. First, we assessed if (i) livestock movements, (ii) spatial proximity from communes with infected animals, and (iii) livestock density were associated with the temporal sequence of RVFV introduction into Mayotte communes’ livestock populations. Second, we assessed whether the rate of human infection was associated with (a) spatial proximity from and (b) livestock density of communes with infected animals. Our analyses showed that the temporal sequence of RVFV introduction into communes’ livestock populations was associated with livestock movements and spatial proximity from communes with infected animals, with livestock movements being associated with the best model fit. Moreover, the pattern of human cases was associated with their spatial proximity from communes with infected animals, with the risk of human infection sharply increasing if livestock in the same or close communes were infected. This study highlights the importance of understanding livestock movement networks in informing the design of risk-based RVF surveillance programs. Rift Valley fever (RVF) is a vector-borne zoonotic disease, endemic in many sub-Saharan Africa regions with substantial outbreaks. RVF virus (RVFV) is transmitted to animals primarily by the bite of infected mosquitos, whereas direct or indirect contact with infected animals forms the primary route of RVFV transmission to humans. In 2018–19, Mayotte, an archipelago in the Indian Ocean between Madagascar and the coast of Eastern Africa, experienced an RVF epidemic in both livestock and humans. In this study, we investigated factors shaping the spatio-temporal pattern of RVFV infection in livestock and human populations across Mayotte. The diffusion of RVFV through Mayotte’s livestock population was associated with livestock movements and, potentially to a lesser extent, spatial proximity from communes with infected animals. Moreover, the pressure of infection on humans was the highest if nearby livestock were infected. This study highlights the value of accounting for the structure of livestock movement networks in the surveillance of zoonotic diseases at the human-animal interface, and the need for One Health approaches.
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Affiliation(s)
- Younjung Kim
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
- * E-mail:
| | - Raphaëlle Métras
- INSERM, Sorbonne Université, Institut Pierre Louis d’Épidémiologie et de Santé Publique (UMRS-1136), Paris, France
| | | | | | - Soihibou Combo
- Direction de l’Alimentation, de l’Agriculture et de la Forêt de Mayotte, Mamoudzou, France
| | - Gilles Le Godais
- Direction de l’Alimentation, de l’Agriculture et de la Forêt de Mayotte, Mamoudzou, France
| | - Dirk U. Pfeiffer
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
- Veterinary Epidemiology, Economics and Public Health group, Department of Pathobiology and Population Sciences, The Royal Veterinary College, Hatfield, United Kingdom
| | - Catherine Cêtre-Sossah
- CIRAD, UMR ASTRE, Sainte Clotilde, La Réunion, France
- ASTRE, CIRAD, Univ Montpellier, INRAE, Montpellier, France
| | - Eric Cardinale
- CIRAD, UMR ASTRE, Sainte Clotilde, La Réunion, France
- ASTRE, CIRAD, Univ Montpellier, INRAE, Montpellier, France
| | | | | | | | - Guillaume Fournié
- Veterinary Epidemiology, Economics and Public Health group, Department of Pathobiology and Population Sciences, The Royal Veterinary College, Hatfield, United Kingdom
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48
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So MKP, Chu AMY, Tiwari A, Chan JNL. On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics. Sci Rep 2021; 11:5112. [PMID: 33664280 PMCID: PMC7933279 DOI: 10.1038/s41598-021-84094-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 02/06/2021] [Indexed: 11/08/2022] Open
Abstract
The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of 'co-movement' of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pandemic control. The preparedness risk score contributed by countries in Asia is between 25% and 50% most of the time after February and America contributes around 40% in July 2020. The high preparedness risk contribution implies the importance of travel restrictions between those countries. The severity risk score of America and Europe contribute around 90% in December 2020, signifying that the control of COVID-19 is still worrying in America and Europe. We can keep track of the pandemic situation in each country using an online dashboard to update the pandemic risk scores and contributions.
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Affiliation(s)
- Mike K P So
- Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Amanda M Y Chu
- Department of Social Sciences, The Education University of Hong Kong, Hong Kong, China
| | - Agnes Tiwari
- LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Nursing, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Jacky N L Chan
- Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, China
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49
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Xue Y, Ruan X, Xiao Y. Measles dynamics on network models with optimal control strategies. ADVANCES IN DIFFERENCE EQUATIONS 2021; 2021:138. [PMID: 33679964 PMCID: PMC7910804 DOI: 10.1186/s13662-021-03306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/15/2021] [Indexed: 06/05/2023]
Abstract
To investigate the influences of heterogeneity and waning immunity on measles transmission, we formulate a network model with periodic transmission rate, and theoretically examine the threshold dynamics. We numerically find that the waning of immunity can lead to an increase in the basic reproduction number R 0 and the density of infected individuals. Moreover, there exists a critical level for average degree above which R 0 increases quicker in the scale-free network than in the random network. To design the effective control strategies for the subpopulations with different activities, we examine the optimal control problem of the heterogeneous model. Numerical studies suggest us no matter what the network is, we should implement control measures as soon as possible once the outbreak takes off, and particularly, the subpopulation with high connectivity should require high intensity of interventions. However, with delayed initiation of controls, relatively strong control measures should be given to groups with medium degrees. Furthermore, the allocation of costs (or resources) should coincide with their contact patterns.
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Affiliation(s)
- Yuyi Xue
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, P.R. China
| | - Xiaoe Ruan
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, P.R. China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, P.R. China
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50
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Wang J, Wang K, Li J, Jiang J, Wang Y, Mei J, Li S. Accelerating Epidemiological Investigation Analysis by Using NLP and Knowledge Reasoning: A Case Study on COVID-19. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1258-1267. [PMID: 33936502 PMCID: PMC8075493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
COVID-19 is threatening the health of the entire human population. In order to control the spread of the disease, epidemiological investigations should be conducted, to trace the infection source of each confirmed patient and isolate their close contacts. However, the analysis on a mass of case reports in epidemiological investigation is extremely time-consuming and labor-intensive. This paper presents an end-to-end framework for automatic epidemiological case report analysis and inference, in which a Tuple-based Multi-Task Neural Network (TMT-NN) is designed and implemented for jointly recognizing epidemiological entities and relations from case reports, and an epidemiological knowledge graph and its corresponding inference engine are built to uncover the infection modes, sources and pathways. Preliminary experiments demonstrate the promising results, and we published a real data set of COVID-19 epidemiological investigation corpora at Github, as well as contributing our COVID-19 epidemiological knowledge graph to the open community OpenKG.cn.
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Affiliation(s)
| | - Ke Wang
- IBM Research, Beijing, China
| | - Jing Li
- IBM Research, Beijing, China
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