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Ton MJ, Ingels MW, de Bruijn JA, de Moel H, Reimann L, Botzen WJW, Aerts JCJH. A global dataset of 7 billion individuals with socio-economic characteristics. Sci Data 2024; 11:1096. [PMID: 39375378 PMCID: PMC11458621 DOI: 10.1038/s41597-024-03864-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 09/11/2024] [Indexed: 10/09/2024] Open
Abstract
In global impact modeling, there is a need to address the heterogeneous characteristics of households and individuals that drive different behavioral responses to, for example, environmental risk, socio-economic policy changes and spread of diseases. In this research, we present GLOPOP-S, the first global synthetic population dataset with 1,999,227,130 households and 7,335,881,094 individuals for the year 2015, consistent with population statistics at an administrative unit 1 level. GLOPOS-S contains the following attributes: age, education, gender, income/wealth, settlement type (urban/rural), household size, household type, and for selected countries in the Global South, ownership of agricultural land and dwelling characteristics. To generate GLOPOP-S, we use microdata from the Luxembourg Income Study (LIS) and Demographic and Health Surveys (DHS) and apply synthetic reconstruction techniques to fit national survey data to regional statistics, thereby accounting for spatial differences within and across countries. Additionally, we have developed methods to generate data for countries without available microdata. The dataset can be downloaded per region or country. GLOPOP-S is open source and can be extended with other attributes.
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Affiliation(s)
- Marijn J Ton
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Michiel W Ingels
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jens A de Bruijn
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
| | - Hans de Moel
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lena Reimann
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Wouter J W Botzen
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen C J H Aerts
- Institute for Environmental Studies (IVM); Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Deltares, Delft, The Netherlands
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2
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Xia F, Xiao Y, Ma J. The optimal spatially-dependent control measures to effectively and economically eliminate emerging infectious diseases. PLoS Comput Biol 2024; 20:e1012498. [PMID: 39374303 PMCID: PMC11486435 DOI: 10.1371/journal.pcbi.1012498] [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: 11/24/2023] [Revised: 10/17/2024] [Accepted: 09/17/2024] [Indexed: 10/09/2024] Open
Abstract
Non-pharmaceutical interventions (NPIs) are effective in mitigating infections during the early stages of an infectious disease outbreak. However, these measures incur significant economic and livelihood costs. To address this, we developed an optimal control framework aimed at identifying strategies that minimize such costs while ensuring full control of a cross-regional outbreak of emerging infectious diseases. Our approach uses a spatial SEIR model with interventions for the epidemic process, and incorporates population flow in a gravity model dependent on gross domestic product (GDP) and geographical distance. We applied this framework to identify an optimal control strategy for the COVID-19 outbreak caused by the Delta variant in Xi'an City, Shaanxi, China, between December 2021 and January 2022. The model was parameterized by fitting it to daily case data from each district of Xi'an City. Our findings indicate that an increase in the basic reproduction number, the latent period or the infectious period leads to a prolonged outbreak and a larger final size. This indicates that diseases with greater transmissibility are more challenging and costly to control, and so it is important for governments to quickly identify cases and implement control strategies. Indeed, the optimal control strategy we identified suggests that more costly control measures should be implemented as soon as they are deemed necessary. Our results demonstrate that optimal control regimes exhibit spatial, economic, and population heterogeneity. More populated and economically developed regions require a robust regular surveillance mechanism to ensure timely detection and control of imported infections. Regions with higher GDP tend to experience larger-scale epidemics and, consequently, require higher control costs. Notably, our proposed optimal strategy significantly reduced costs compared to the actual expenditures for the Xi'an outbreak.
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Affiliation(s)
- Fan Xia
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| | - Junling Ma
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
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3
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Diallo D, Schönfeld J, Blanken TF, Hecking T. Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns through Human Mobility Models from Real-World Data. ENTROPY (BASEL, SWITZERLAND) 2024; 26:703. [PMID: 39202173 DOI: 10.3390/e26080703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/28/2024] [Accepted: 07/31/2024] [Indexed: 09/03/2024]
Abstract
This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. Typically, large-scale epidemiological simulations overlook the specifics of human mobility within confined spaces or rely on overly simplistic models. By focusing on the distinct aspects of infection propagation within specific locations, our approach strengthens the realism of such pandemic simulations. The resulting models shed light on the role of spatial encounters in disease spread and improve the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers a new perspective on temporal network generation for epidemiological modeling.
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Affiliation(s)
- Diaoulé Diallo
- Institute of Software Technology, German Aerospace Center (DLR), 51147 Cologne, Germany
| | - Jurij Schönfeld
- Institute of Software Technology, German Aerospace Center (DLR), 51147 Cologne, Germany
| | - Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, 1018WS Amsterdam, The Netherlands
| | - Tobias Hecking
- Institute of Software Technology, German Aerospace Center (DLR), 51147 Cologne, Germany
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4
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Wang L, Xu C, Hu M, Wang J, Qiao J, Chen W, Zhu Q, Wang Z. Modeling tuberculosis transmission flow in China, 2010-2012. BMC Infect Dis 2024; 24:784. [PMID: 39103752 DOI: 10.1186/s12879-024-09649-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/23/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. METHODS 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. RESULTS Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. CONCLUSIONS Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.
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Affiliation(s)
- Li Wang
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, KaiFeng, 475001, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Maogui Hu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiajun Qiao
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China.
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, KaiFeng, 475001, China.
| | - Wei Chen
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Qiankun Zhu
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
| | - Zhipeng Wang
- College of Geography and Environmental Science, Henan University, KaiFeng, 475001, China
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St-Onge G, Davis JT, Hébert-Dufresne L, Allard A, Urbinati A, Scarpino SV, Chinazzi M, Vespignani A. Optimization and performance analytics of global aircraft-based wastewater surveillance networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.02.24311418. [PMID: 39132478 PMCID: PMC11312644 DOI: 10.1101/2024.08.02.24311418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Aircraft wastewater surveillance has been proposed as a novel approach to monitor the global spread of pathogens. Here we develop a computational framework to provide actionable information for designing and estimating the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potentials and find that networks of 10 to 20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSNs effectiveness while minimizing resource use. Our findings highlight that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSNs capabilities, stressing the importance of resource optimization. We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.
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Affiliation(s)
- Guillaume St-Onge
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA
- The Roux Institute, Northeastern University, Portland, ME 04101, USA
| | - Jessica T Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05401, USA
- Département de physique, de génie physique et d'optique, Université Laval, Québec City, QC G1V 0A6, Canada
| | - Antoine Allard
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05401, USA
- Département de physique, de génie physique et d'optique, Université Laval, Québec City, QC G1V 0A6, Canada
| | - Alessandra Urbinati
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA
| | - Samuel V Scarpino
- Institute for Experiential AI, Northeastern University, Boston, MA 02115, USA
- Network Science Institute, Northeastern University, Boston, MA 02115, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA
- The Roux Institute, Northeastern University, Portland, ME 04101, USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA
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6
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Sergio AR, Schimit PHT. Optimizing Contact Network Topological Parameters of Urban Populations Using the Genetic Algorithm. ENTROPY (BASEL, SWITZERLAND) 2024; 26:661. [PMID: 39202131 PMCID: PMC11353388 DOI: 10.3390/e26080661] [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: 05/27/2024] [Revised: 07/11/2024] [Accepted: 07/26/2024] [Indexed: 09/03/2024]
Abstract
This paper explores the application of complex network models and genetic algorithms in epidemiological modeling. By considering the small-world and Barabási-Albert network models, we aim to replicate the dynamics of disease spread in urban environments. This study emphasizes the importance of accurately mapping individual contacts and social networks to forecast disease progression. Using a genetic algorithm, we estimate the input parameters for network construction, thereby simulating disease transmission within these networks. Our results demonstrate the networks' resemblance to real social interactions, highlighting their potential in predicting disease spread. This study underscores the significance of complex network models and genetic algorithms in understanding and managing public health crises.
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Barreras F, Watts DJ. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. NATURE COMPUTATIONAL SCIENCE 2024; 4:398-411. [PMID: 38898315 DOI: 10.1038/s43588-024-00637-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 05/02/2024] [Indexed: 06/21/2024]
Abstract
Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.
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Affiliation(s)
- Francisco Barreras
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Chinazzi M, Davis JT, Y Piontti AP, Mu K, Gozzi N, Ajelli M, Perra N, Vespignani A. A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US. Epidemics 2024; 47:100757. [PMID: 38493708 DOI: 10.1016/j.epidem.2024.100757] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/22/2024] [Accepted: 02/26/2024] [Indexed: 03/19/2024] Open
Abstract
The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.
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Affiliation(s)
- Matteo Chinazzi
- The Roux Institute, Northeastern University, Portland, ME, USA; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Jessica T Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Nicolò Gozzi
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Nicola Perra
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; School of Mathematical Sciences, Queen Mary University, London, UK
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; Institute for Scientific Interchange Foundation, Turin, Italy.
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Nunes MC, Thommes E, Fröhlich H, Flahault A, Arino J, Baguelin M, Biggerstaff M, Bizel-Bizellot G, Borchering R, Cacciapaglia G, Cauchemez S, Barbier--Chebbah A, Claussen C, Choirat C, Cojocaru M, Commaille-Chapus C, Hon C, Kong J, Lambert N, Lauer KB, Lehr T, Mahe C, Marechal V, Mebarki A, Moghadas S, Niehus R, Opatowski L, Parino F, Pruvost G, Schuppert A, Thiébaut R, Thomas-Bachli A, Viboud C, Wu J, Crépey P, Coudeville L. Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infect Dis Model 2024; 9:501-518. [PMID: 38445252 PMCID: PMC10912817 DOI: 10.1016/j.idm.2024.02.008] [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: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.
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Affiliation(s)
- Marta C. Nunes
- Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon (HCL) and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé Publique, Épidémiologie et Écologie Évolutive des Maladies Infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard Lyon 1, Lyon, France
- South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Edward Thommes
- New Products and Innovation (NPI), Sanofi Vaccines (Global), Toronto, Ontario, Canada
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT (b-it), Bonn, Germany
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland and Swiss School of Public Health, Zürich, Switzerland
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew Biggerstaff
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Gaston Bizel-Bizellot
- Departement of Computational Biology, Departement of Global Health, Institut Pasteur, Paris, France
| | - Rebecca Borchering
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Giacomo Cacciapaglia
- Institut de Physique des Deux Infinis de Lyon (IP2I), UMR5822, IN2P3/CNRS, Université Claude Bernard Lyon 1, Villeurbanne, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000 CNRS, Paris, France
| | - Alex Barbier--Chebbah
- Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR 3571, France
| | - Carsten Claussen
- Fraunhofer-Institute for Translational Medicine and Pharmacology, Hamburg, Germany
| | - Christine Choirat
- Institute of Global Health, Faculty of Medicine, University of Geneva, Switzerland
| | - Monica Cojocaru
- Mathematics & Statistics Department, College of Engineering and Physical Sciences, University of Guelph, Guelph, Ontario, Canada
| | | | - Chitin Hon
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
| | - Jude Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | | | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | | | - Vincent Marechal
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France
| | | | - Seyed Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Lulla Opatowski
- UMR 1018, Team “Anti-infective Evasion and Pharmacoepidemiology”, Université Paris-Saclay, UVSQ, INSERM, France
- Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, Paris, France
| | - Francesco Parino
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | | | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Rodolphe Thiébaut
- Bordeaux University, Department of Public Health, Inserm UMR 1219 Bordeaux Population Health Research Center, Inria SISTM, Bordeaux, France
| | | | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Jianhong Wu
- York Emergency Mitigation, Engagement, Response, and Governance Institute, Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
| | - Pascal Crépey
- EHESP, Université de Rennes, CNRS, IEP Rennes, Arènes - UMR 6051, RSMS – Inserm U 1309, Rennes, France
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10
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García-García D, Fernández-Martínez B, Bartumeus F, Gómez-Barroso D. Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study. JMIR Public Health Surveill 2024; 10:e51191. [PMID: 38801767 PMCID: PMC11165286 DOI: 10.2196/51191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/18/2023] [Accepted: 03/05/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge. OBJECTIVE This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel. METHODS We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the "travelers' index" from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases. RESULTS The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue. CONCLUSIONS Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors.
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Affiliation(s)
- David García-García
- Department of Communicable Diseases, National Centre of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- Epidemiology and Public Health Biomedical Network Research Consortium (CIBERESP), Madrid, Spain
| | - Beatriz Fernández-Martínez
- Department of Communicable Diseases, National Centre of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- Epidemiology and Public Health Biomedical Network Research Consortium (CIBERESP), Madrid, Spain
| | - Frederic Bartumeus
- Group of Theoretical and Computational Ecology, Centre for Advanced Studies of Blanes, Spanish Research Council, Blanes, Spain
- Ecological and Forestry Applications Research Centre, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, Barcelona, Spain
| | - Diana Gómez-Barroso
- Department of Communicable Diseases, National Centre of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- Epidemiology and Public Health Biomedical Network Research Consortium (CIBERESP), Madrid, Spain
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11
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Aljabali AAA, Obeid MA, El-Tanani M, Mishra V, Mishra Y, Tambuwala MM. Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics. Gene 2024; 905:148174. [PMID: 38242374 DOI: 10.1016/j.gene.2024.148174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.
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Affiliation(s)
- Alaa A A Aljabali
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan.
| | - Mohammad A Obeid
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan
| | - Mohamed El-Tanani
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.
| | - Vijay Mishra
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Yachana Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Murtaza M Tambuwala
- Lincoln Medical School, University of Lincoln, Brayford Pool Campus, Lincoln LN6 7TS, United Kingdom.
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12
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John RS, Miller JC, Muylaert RL, Hayman DTS. High connectivity and human movement limits the impact of travel time on infectious disease transmission. J R Soc Interface 2024; 21:20230425. [PMID: 38196378 PMCID: PMC10777149 DOI: 10.1098/rsif.2023.0425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.
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Affiliation(s)
- Reju Sam John
- Massey University, Palmerston North 4474, New Zealand
- University of Auckland, Auckland 1010, New Zealand
| | - Joel C. Miller
- La Trobe University, Melbourne 3086, Victoria, Australia
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13
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Klamser PP, Zachariae A, Maier BF, Baranov O, Jongen C, Schlosser F, Brockmann D. Inferring country-specific import risk of diseases from the world air transportation network. PLoS Comput Biol 2024; 20:e1011775. [PMID: 38266041 PMCID: PMC10843136 DOI: 10.1371/journal.pcbi.1011775] [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: 05/03/2023] [Revised: 02/05/2024] [Accepted: 12/21/2023] [Indexed: 01/26/2024] Open
Abstract
Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.
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Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Adrian Zachariae
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Benjamin F. Maier
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Olga Baranov
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Clara Jongen
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Frank Schlosser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Dirk Brockmann
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- Center Synergy of Systems (SynoSys), Center for Interdisciplinary Digital Sciences, Technische Universität Dresden, Dresden, Germany
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14
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Hassani M, De Haro C, Flores L, Emish M, Kim S, Kelani Z, Ugarte DA, Hightow-Weidman L, Castel A, Li X, Theall KP, Young S. Exploring mobility data for enhancing HIV care engagement in Black/African American and Hispanic/Latinx individuals: a longitudinal observational study protocol. BMJ Open 2023; 13:e079900. [PMID: 38101845 PMCID: PMC10729277 DOI: 10.1136/bmjopen-2023-079900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION Increasing engagement in HIV care among people living with HIV, especially those from Black/African American and Hispanic/Latinx communities, is an urgent need. Mobility data that measure individuals' movements over time in combination with sociostructural data (eg, crime, census) can potentially identify barriers and facilitators to HIV care engagement and can enhance public health surveillance and inform interventions. METHODS AND ANALYSIS The proposed work is a longitudinal observational cohort study aiming to enrol 400 Black/African American and Hispanic/Latinx individuals living with HIV in areas of the USA with high prevalence rates of HIV. Each participant will be asked to share at least 14 consecutive days of mobility data per month through the study app for 1 year and complete surveys at five time points (baseline, 3, 6, 9 and 12 months). The study app will collect Global Positioning System (GPS) data. These GPS data will be merged with other data sets containing information related to HIV care facilities, other healthcare, business and service locations, and sociostructural data. Machine learning and deep learning models will be used for data analysis to identify contextual predictors of HIV care engagement. The study includes interviews with stakeholders to evaluate the implementation and ethical concerns of using mobility data to increase engagement in HIV care. We seek to study the relationship between mobility patterns and HIV care engagement. ETHICS AND DISSEMINATION Ethical approval has been obtained from the Institutional Review Board of the University of California, Irvine (#20205923). Collected data will be deidentified and securely stored. Dissemination of findings will be done through presentations, posters and research papers while collaborating with other research teams.
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Affiliation(s)
- Maryam Hassani
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
| | - Cristina De Haro
- University of California Irvine, Paul Merage School of Business, Irvine, California, USA
| | - Lidia Flores
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
| | - Mohamed Emish
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
| | - Seungjun Kim
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
| | - Zeyad Kelani
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
| | - Dominic Arjuna Ugarte
- Department of Emergency Medicine, University of California Irvine, Orange, California, USA
| | | | - Amanda Castel
- Department of Epidemiology, The George Washington University, Washington, District of Columbia, USA
- The George Washington University, Milken Institute of Public Health, Washington, District of Columbia, USA
| | - Xiaoming Li
- University of South Carolina, Arnold School of Public Health, Columbia, South Carolina, USA
| | - Katherine P Theall
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Sean Young
- University of California Irvine, Donald Bren School of Information and Computer Sciences, Irvine, California, USA
- Department of Emergency Medicine, University of California Irvine, Orange, California, USA
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15
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Milanesi S, De Nicolao G. Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions. PLoS One 2023; 18:e0295079. [PMID: 38060513 PMCID: PMC10703316 DOI: 10.1371/journal.pone.0295079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded.
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Affiliation(s)
- Simone Milanesi
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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16
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Gao S, Dai X, Wang L, Perra N, Wang Z. Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:7686-7698. [PMID: 36054390 DOI: 10.1109/tcyb.2022.3198732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.
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17
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Delussu F, Tizzoni M, Gauvin L. The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach. PNAS NEXUS 2023; 2:pgad302. [PMID: 37811338 PMCID: PMC10558401 DOI: 10.1093/pnasnexus/pgad302] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023]
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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Affiliation(s)
- Federico Delussu
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Applied Mathematics and Computer Science, DTU, Richard Petersens Plads, DK-2800 Copenhagen, Denmark
| | - Michele Tizzoni
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Sociology and Social Research, University of Trento, via Verdi 26, I-38122 Trento, Italy
| | - Laetitia Gauvin
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- UMR 215 PRODIG, Institute for Research on Sustainable Development - IRD, 5 cours des Humanités, F-93 322 Aubervilliers Cedex, France
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18
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Ódor G, Vuckovic J, Ndoye MAS, Thiran P. Source identification via contact tracing in the presence of asymptomatic patients. APPLIED NETWORK SCIENCE 2023; 8:53. [PMID: 37614376 PMCID: PMC10442312 DOI: 10.1007/s41109-023-00566-3] [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: 02/01/2023] [Accepted: 06/26/2023] [Indexed: 08/25/2023]
Abstract
Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.
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19
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Cot C, Aksentijević D, Jugović A, Cacciapaglia G, Mannarini G. Maritime transportation and people mobility in the early diffusion of COVID-19 in Croatia. Front Public Health 2023; 11:1183047. [PMID: 37663862 PMCID: PMC10469838 DOI: 10.3389/fpubh.2023.1183047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction The outbreak of COVID-19 in Europe began in early 2020, leading to the emergence of several waves of infection with varying timings across European countries. The largest wave of infection occurred in August-September. Croatia, known for being a hotspot of tourism in the Mediterranean region, raised concerns that it might have played a role in incubating the pandemic during the summer of 2020. Methods To investigate this possibility, we conducted a data-driven study to examine the potential influence of passenger mobility to and within Croatia, utilizing various modes of transportation. To achieve this, we integrated observational datasets into the "epidemic Renormalization Group" modeling framework. Results By comparing the models with epidemiological data, we found that in the case of Croatia in 2020, neither maritime nor train transportation played a prominent role in propagating the infection. Instead, our analysis highlighted the leading role of both road and airborne mobility in the transmission of the virus. Discussion The proposed framework serves to test hypotheses concerning the causation of infectious waves, offering the capacity to rule out unrelated factors from consideration.
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Affiliation(s)
- Corentin Cot
- Laboratoire de Physique des 2 Infinis Irène Joliot Curie (UMR 9012), Centre Nationale de la Recherche Scientifique (CNRS)/IN2P3, Orsay, France
| | - Dea Aksentijević
- Pomorski Fakultet Sveučilišta u Rijeci/Faculty of Maritime Studies, University of Rijeka, Rijeka, Croatia
| | - Alen Jugović
- Pomorski Fakultet Sveučilišta u Rijeci/Faculty of Maritime Studies, University of Rijeka, Rijeka, Croatia
| | - Giacomo Cacciapaglia
- Univ Lyon, Univ Claude Bernard Lyon 1, Centre Nationale de la Recherche Scientifique (CNRS)/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, France
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20
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Gambetta D, Mauro G, Pappalardo L. Mobility constraints in segregation models. Sci Rep 2023; 13:12087. [PMID: 37495661 PMCID: PMC10372033 DOI: 10.1038/s41598-023-38519-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/10/2023] [Indexed: 07/28/2023] Open
Abstract
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.
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Affiliation(s)
- Daniele Gambetta
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
- University of Pisa, Pisa, Italy.
| | - Giovanni Mauro
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
- University of Pisa, Pisa, Italy.
- IMT School for Advanced Studies, Lucca, Italy.
| | - Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
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21
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Rahaman H, Barik D. Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230377. [PMID: 37501658 PMCID: PMC10369033 DOI: 10.1098/rsos.230377] [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: 03/24/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.
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Affiliation(s)
- Hafijur Rahaman
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
| | - Debashis Barik
- School of Chemistry, University of Hyderabad, Central University PO, Hyderabad 500046, Telangana, India
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22
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Pujante-Otalora L, Canovas-Segura B, Campos M, Juarez JM. The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review. J Biomed Inform 2023; 143:104422. [PMID: 37315830 DOI: 10.1016/j.jbi.2023.104422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 06/05/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. METHODS a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. RESULTS Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. CONCLUSION We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.
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Affiliation(s)
- Lorena Pujante-Otalora
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain.
| | | | - Manuel Campos
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), El Palmar, Murcia 30120, Spain.
| | - Jose M Juarez
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain.
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23
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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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24
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Gozzi N, Chinazzi M, Dean NE, Longini IM, Halloran ME, Perra N, Vespignani A. Estimating the impact of COVID-19 vaccine inequities: a modeling study. Nat Commun 2023; 14:3272. [PMID: 37277329 DOI: 10.1038/s41467-023-39098-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54-94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6-50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15-70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
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Affiliation(s)
- Nicolò Gozzi
- Networks and Urban Systems Centre, University of Greenwich, London, UK
- ISI Foundation, Turin, Italy
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E Dean
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - M Elizabeth Halloran
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nicola Perra
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
- School of Mathematical Sciences, Queen Mary University, London, UK.
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
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25
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Xiao Y, Zhou J, Cheng Q, Yang J, Chen B, Zhang T, Xu L, Xu B, Ren Z, Liu Z, Shen C, Wang C, Liu H, Li X, Li R, Yu L, Guan D, Zhang W, Wang J, Hou L, Deng K, Bai Y, Xu B, Dou D, Gong P. Global age-structured spatial modeling for emerging infectious diseases like COVID-19. PNAS NEXUS 2023; 2:pgad127. [PMID: 37143866 PMCID: PMC10153731 DOI: 10.1093/pnasnexus/pgad127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/27/2023] [Accepted: 03/30/2023] [Indexed: 05/06/2023]
Abstract
Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.
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Affiliation(s)
- Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Jingbo Zhou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jun Yang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bin Chen
- Division of Landscape Architecture, The University of Hong Kong, Hong Kong 999007, China
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Bo Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhehao Ren
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhaoyang Liu
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Chong Shen
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Che Wang
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Han Liu
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Xiaoting Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Ruiyun Li
- School of Public Health (SPH), Nanjing Medical University, Nanjing 211166, China
| | - Le Yu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Wusheng Zhang
- Department of Computer Science and Technology, Institute of High Performance Computing, Tsinghua University, Beijing 100084, China
| | - Jie Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
- AI for Earth Laboratory, Cross-Strait Institute, Tsinghua University, Beijing 100084, China
| | - Lin Hou
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Yuqi Bai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bing Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dejing Dou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Peng Gong
- To whom correspondence should be addressed:
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26
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Poznyak A, Chairez I, Anyutin A. Differential Neural Networks Prediction Using Slow and Fast Hybrid Learning: Application to Prognosis of Infectionsand Deaths of COVID-19 Dynamics. Neural Process Lett 2023:1-17. [PMID: 37359130 PMCID: PMC10035488 DOI: 10.1007/s11063-023-11216-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/26/2023] [Indexed: 03/25/2023]
Abstract
This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.
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Affiliation(s)
- A. Poznyak
- CINVESTAV IPN, DCA, Cd. de Mexico, Mexico
| | - I. Chairez
- Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Cd. de Guadalajara, Mexico
| | - A. Anyutin
- Institute of Radio Engineering and Electronics, Fryazino Branch, Ran, Fryazino, Russia
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27
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de Miguel Arribas A, Aleta A, Moreno Y. Assessing the effectiveness of perimeter lockdowns as a response to epidemics at the urban scale. Sci Rep 2023; 13:4474. [PMID: 36934138 PMCID: PMC10024032 DOI: 10.1038/s41598-023-31614-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/14/2023] [Indexed: 03/20/2023] Open
Abstract
From September 2020 to May 2021 Madrid region (Spain) followed a rather unique non-pharmaceutical intervention (NPI) by establishing a strategy of perimeter lockdowns (PLs) that banned travels to and from areas satisfying certain epidemiological risk criteria. PLs were pursued to avoid harsher restrictions, but some studies have found that the particular implementation by Madrid authorities was rather ineffective. Based on Madrid's case, we devise a general, minimal framework to investigate the PLs effectiveness by using a data-driven metapopulation epidemiological model of a city, and explore under which circumstances the PLs could be a good NPI. The model is informed with real mobility data from Madrid to contextualize its results, but it can be generalized elsewhere. The lowest lockdown activation threshold [Formula: see text] considered (14-day cumulative incidence rate of 20 cases per every [Formula: see text] inhabitants) shows a prevalence reduction [Formula: see text] with respect to the scenario [Formula: see text], more akin to the case of Madrid, and assuming no further mitigation. Only the combination of [Formula: see text] and mobility reduction [Formula: see text] can avoid PLs for more than [Formula: see text] of the system. The combination of low [Formula: see text] and strong local transmissibility reduction is key to minimize the impact, but the latter is harder to achieve given that we assume a situation with highly mitigated transmission, resembling the one observed during the second wave of COVID-19 in Madrid. Thus, we conclude that a generalized lockdown is hard to avoid under any realistic setting if only this strategy is applied.
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Affiliation(s)
- Alfonso de Miguel Arribas
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain.
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain.
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
- Centai, Turin, Italy
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28
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Nazia N, Law J, Butt ZA. Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada. Health Place 2023; 80:102988. [PMID: 36791508 PMCID: PMC9922578 DOI: 10.1016/j.healthplace.2023.102988] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 12/16/2022] [Accepted: 02/09/2023] [Indexed: 02/16/2023]
Abstract
Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada; School of Planning, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON, N2L3G1, Canada.
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29
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Santos ES, Miranda JG, Saba H, Skalinski LM, Araújo ML, Veiga RV, Costa MDCN, Cardim LL, Paixão ES, Teixeira MG, Andrade RF, Barreto ML. Complex network analysis of arboviruses in the same geographic domain: Differences and similarities. CHAOS, SOLITONS, AND FRACTALS 2023; 168:None. [PMID: 36876054 PMCID: PMC9980430 DOI: 10.1016/j.chaos.2023.113134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/29/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Arbovirus can cause diseases with a broad spectrum from mild to severe and long-lasting symptoms, affecting humans worldwide and therefore considered a public health problem with global and diverse socio-economic impacts. Understanding how they spread within and across different regions is necessary to devise strategies to control and prevent new outbreaks. Complex network approaches have widespread use to get important insights on several phenomena, as the spread of these viruses within a given region. This work uses the motif-synchronization methodology to build time varying complex networks based on data of registered infections caused by Zika, chikungunya, and dengue virus from 2014 to 2020, in 417 cities of the state of Bahia, Brazil. The resulting network sets capture new information on the spread of the diseases that are related to the time delay in the synchronization of the time series among different municipalities. Thus the work adds new and important network-based insights to previous results based on dengue dataset in the period 2001-2016. The most frequent synchronization delay time between time series in different cities, which control the insertion of edges in the networks, ranges 7 to 14 days, a period that is compatible with the time of the individual-mosquito-individual transmission cycle of these diseases. As the used data covers the initial periods of the first Zika and chikungunya outbreaks, our analyses reveal an increasing monotonic dependence between distance among cities and the time delay for synchronization between the corresponding time series. The same behavior was not observed for dengue, first reported in the region back in 1986, either in the previously 2001-2016 based results or in the current work. These results show that, as the number of outbreaks accumulates, different strategies must be adopted to combat the dissemination of arbovirus infections.
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Affiliation(s)
- Eslaine S. Santos
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
| | - José G.V. Miranda
- Physics Institute, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Hugo Saba
- Centro Universitário SENAI CIMATEC, Av. Orlando Gomes, 1845—Piatã, Salvador 41650-010, Brazil
- Department of Exact and Earth Sciences, University of the State of Bahia, R. Silveira Martins, 2555—Cabula, Salvador 41180-045, Brazil
| | - Lacita M. Skalinski
- Collective Health Institute, Federal University of Bahia, Salvador, Bahia, Brazil
- Santa Cruz State University, Ilhéus, Bahia, Brazil
| | - Marcio L.V. Araújo
- Instituto Federal de Ciência e Tecnologia da Bahia (IFBA), R. São Cristóvão, s/n - Novo Horizonte, Lauro de Freitas, 42700-000, Brazil
| | - Rafael V. Veiga
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
- The Babraham Institute, Laboratory of Lymphocyte Signalling and Development, Cambridge, United Kingdom
| | | | - Luciana L. Cardim
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
| | - Enny S. Paixão
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Maria Glória Teixeira
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
- Collective Health Institute, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Roberto F.S. Andrade
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
- Physics Institute, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Maurício L. Barreto
- Center of Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, Bahia, Brazil
- Collective Health Institute, Federal University of Bahia, Salvador, Bahia, Brazil
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30
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Del-Águila-Mejía J, García-García D, Rojas-Benedicto A, Rosillo N, Guerrero-Vadillo M, Peñuelas M, Ramis R, Gómez-Barroso D, Donado-Campos JDM. Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4356. [PMID: 36901366 PMCID: PMC10001675 DOI: 10.3390/ijerph20054356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.
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Affiliation(s)
- Javier Del-Águila-Mejía
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Servicio de Medicina Preventiva, Hospital Universitario de Móstoles, Calle Río Júcar s/n, 28935 Móstoles, Spain
| | - David García-García
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Ayelén Rojas-Benedicto
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
- Universidad Nacional de Educación a Distancia (UNED), Calle de Bravo Murillo 38, 28015 Madrid, Spain
| | - Nicolás Rosillo
- Servicio de Medicina Preventiva, Hospital Universitario 12 de Octubre, Avenida de Córdoba s/n, 28041 Madrid, Spain
| | - María Guerrero-Vadillo
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Marina Peñuelas
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Rebeca Ramis
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Diana Gómez-Barroso
- Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Juan de Mata Donado-Campos
- Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Calle Monforte de Lemos 3-5, 28029 Madrid, Spain
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31
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Shirzadian P, Antony B, Gattani AG, Tasnina N, Heath LS. A time evolving online social network generation algorithm. Sci Rep 2023; 13:2395. [PMID: 36765153 PMCID: PMC9918740 DOI: 10.1038/s41598-023-29443-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.
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Affiliation(s)
- Pouyan Shirzadian
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US.
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US
| | | | - Nure Tasnina
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US
| | - Lenwood S Heath
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US
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32
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Melnyk A, Kozarov L, Wachsmann-Hogiu S. A deconvolution approach to modelling surges in COVID-19 cases and deaths. Sci Rep 2023; 13:2361. [PMID: 36759700 PMCID: PMC9910232 DOI: 10.1038/s41598-023-29198-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks.
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Affiliation(s)
- Adam Melnyk
- Department of Bioengineering, McGill University, 3480 Rue University, Montreal, QC, H3A 0E9, Canada.
| | - Lena Kozarov
- Department of Bioengineering, McGill University, 3480 Rue University, Montreal, QC, H3A 0E9, Canada
| | - Sebastian Wachsmann-Hogiu
- Department of Bioengineering, McGill University, 3480 Rue University, Montreal, QC, H3A 0E9, Canada.
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Epidemic Spreading on Complex Networks as Front Propagation into an Unstable State. Bull Math Biol 2022; 85:4. [PMID: 36471174 DOI: 10.1007/s11538-022-01110-7] [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: 09/27/2021] [Accepted: 11/23/2022] [Indexed: 12/12/2022]
Abstract
We study epidemic arrival times in meta-population disease models through the lens of front propagation into unstable states. We demonstrate that several features of invasion fronts in the PDE context are also relevant to the network case. We show that the susceptible-infected-recovered model on a network is linearly determined in the sense that the arrival times in the nonlinear system are approximated by the arrival times of the instability in the system linearized near the disease-free state. Arrival time predictions are extended to general compartmental models with a susceptible-exposed-infected-recovered model as the primary example. We then study a recent model of social epidemics where higher-order interactions lead to faster invasion speeds. For these pushed fronts, we compute corrections to the estimated arrival time in this case. Finally, we show how inhomogeneities in local infection rates lead to faster average arrival times.
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Pastor-Escuredo D, Gardeazabal A, Koo J, Imai A, Treleaven P. Multi-scale governance and data for sustainable development. Front Big Data 2022; 5:1025256. [PMID: 36532845 PMCID: PMC9753694 DOI: 10.3389/fdata.2022.1025256] [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] [Received: 08/22/2022] [Accepted: 09/28/2022] [Indexed: 09/29/2024] Open
Abstract
Future societal systems will be characterized by heterogeneous human behaviors and data-driven collective action. Complexity will arise as a consequence of the 5th Industrial Revolution and 2nd Data Revolution possible, thanks to a new generation of digital systems and the Metaverse. These technologies will enable new computational methods to tackle inequality while preserving individual rights and self-development. In this context, we do not only need data innovation and computational science, but also new forms of digital policy and governance. The emerging fragility or robustness of the system will depend on how complexity and governance are developed. Through data, humanity has been able to study a number of multi-scale systems from biological to migratory. Multi-scale governance is the new paradigm that feeds the Data Revolution in a world that would be highly digitalized. In the social dimension, we will encounter meta-populations sharing economy and human values. In the temporal dimension, we still need to make all real-time response, evaluation, and mitigation systems a standard integrated system into policy and governance to build up a resilient digital society. Top-down governance is not sufficient to manage all the complexities and exploit all the data available. Coordinating top-down agencies with bottom-up digital platforms will be the design principle. Digital platforms have to be built on top of data innovation and implement Artificial Intelligence (AI)-driven systems to connect, compute, collaborate, and curate data to implement data-driven policy for sustainable development based on Collective Intelligence.
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Affiliation(s)
- David Pastor-Escuredo
- Computer Science Department, University College London, London, United Kingdom
- LifeD Lab, Madrid, Spain
| | - Andrea Gardeazabal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jawoo Koo
- International Food Policy Research Institute, Washington, DC, United States
| | - Asuka Imai
- United Nations High Commissioner for Refugees, Geneva, Switzerland
| | - Philip Treleaven
- Computer Science Department, University College London, London, United Kingdom
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Ghazal I, Rachadi A, Ez-Zahraouy H. Optimal allocation strategies for prioritized geographical vaccination for Covid-19. PHYSICA A 2022; 607:128166. [PMID: 36090308 PMCID: PMC9446606 DOI: 10.1016/j.physa.2022.128166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 08/14/2022] [Indexed: 06/15/2023]
Abstract
While SARS-CoV-2 vaccine distribution campaigns are underway across the world communities, these efforts face the challenge of effective distribution of limited supplies. We wonder whether suitable spatial allocation strategies might significantly improve a campaignfls efficacy in averting damaging outcomes. In the context of a limited and intermittent COVID-19 supply, we investigate spatial prioritization strategies based on six metrics using the SLIR compartmental epidemic model. We found that the strategy based on the prevalence of susceptible individuals is optimal especially in early interventions and for intermediate values of vaccination rate. It minimizes the cumulative incidence and consequently averts most infections. Our results suggest also that a better performance is obtained if the single batch allocation is supplemented with one or more updating of the priority list. Moreover, the splitting of supply in two or more batches may significantly improve the optimality of the operation.
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Affiliation(s)
- Ikram Ghazal
- Laboratoire de Matière Condensée et des Sciences Interdisciplinaires (LaMCScI), Unité de recherche labellisée CNRST, Faculté des Sciences, Université Mohammed V de Rabat, B.P. 1014, Morocco
| | - Abdeljalil Rachadi
- Laboratoire de Matière Condensée et des Sciences Interdisciplinaires (LaMCScI), Unité de recherche labellisée CNRST, Faculté des Sciences, Université Mohammed V de Rabat, B.P. 1014, Morocco
| | - Hamid Ez-Zahraouy
- Laboratoire de Matière Condensée et des Sciences Interdisciplinaires (LaMCScI), Unité de recherche labellisée CNRST, Faculté des Sciences, Université Mohammed V de Rabat, B.P. 1014, Morocco
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Geard N, Bradhurst R, Tellioglu N, Oktaria V, McVernon J, Handley A, Bines JE. Model-based estimation of the impact on rotavirus disease of RV3-BB vaccine administered in a neonatal or infant schedule. Hum Vaccin Immunother 2022; 18:2139097. [PMID: 36409459 DOI: 10.1080/21645515.2022.2139097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Rotavirus infection is a common cause of severe diarrheal disease and a major cause of deaths and hospitalizations among young children. Incidence of rotavirus has declined globally with increasing vaccine coverage. However, it remains a significant cause of morbidity and mortality in low-income countries where vaccine access is limited and efficacy is lower. The oral human neonatal vaccine RV3-BB can be safely administered earlier than other vaccines, and recent trials in Indonesia have demonstrated high efficacy. In this study, we use a stochastic individual-based model of rotavirus transmission and disease to estimate the anticipated population-level impact of RV3-BB following delivery according to either an infant (2, 4, 6 months) and neonatal (0, 2, 4 months) schedule. Using our model, which incorporated an age- and household-structured population and estimates of vaccine efficacy derived from trial data, we found both delivery schedules to be effective at reducing infection and disease. We estimated 95-96% reductions in infection and disease in children under 12 months of age when vaccine coverage is 85%. We also estimate high levels of indirect protection from vaccination, including 78% reductions in infection in adults over 17 years of age. Even for lower vaccine coverage of 55%, we estimate reductions of 84% in infection and disease in children under 12 months of age. While open questions remain about the drivers of observed lower efficacy in low-income settings, our model suggests RV3-BB could be effective at reducing infection and preventing disease in young infants at the population level.
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Affiliation(s)
- Nicholas Geard
- School of Computing and Information Systems, The University of Melbourne, Parkville, Australia
| | - Richard Bradhurst
- Centre of Excellence for Biosecurity Risk Analysis, School of BioSciences, The University of Melbourne, Parkville, Australia
| | - Nefel Tellioglu
- School of Computing and Information Systems, The University of Melbourne, Parkville, Australia
| | - Vicka Oktaria
- Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.,Center for Child Health - Pediatric Research Office, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Jodie McVernon
- Department of Infectious Diseases and Victorian Infectious Diseases Reference Laboratory Epidemiology Unit, The Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Parkville, Australia.,Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia.,Murdoch Children's Research Institute, Parkville, Australia
| | - Amanda Handley
- Murdoch Children's Research Institute, Parkville, Australia.,Medicines Development for Global Health, Southbank, Australia
| | - Julie E Bines
- Murdoch Children's Research Institute, Parkville, Australia.,Department of Gastroenterology and Clinical Nutrition, Royal Children's Hospital, Parkville, Australia.,Department of Paediatrics, The University of Melbourne, Parkville, Australia
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Haileselassie W, Getnet A, Solomon H, Deressa W, Yan G, Parker DM. Mobile phone handover data for measuring and analysing human population mobility in Western Ethiopia: implication for malaria disease epidemiology and elimination efforts. Malar J 2022; 21:323. [PMID: 36369036 PMCID: PMC9652832 DOI: 10.1186/s12936-022-04337-w] [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: 05/10/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Human mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology. METHODS In this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour. RESULTS Heterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population. CONCLUSIONS High human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.
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Affiliation(s)
- Werissaw Haileselassie
- grid.7123.70000 0001 1250 5688School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
| | - Ashagrie Getnet
- grid.7123.70000 0001 1250 5688Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia
| | - Hiwot Solomon
- grid.414835.f0000 0004 0439 6364Ministry of Health, Addis Ababa, Ethiopia
| | - Wakgari Deressa
- grid.7123.70000 0001 1250 5688School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
| | - Guiyun Yan
- grid.266093.80000 0001 0668 7243Program in Public Health, College of Health Sciences, University of California at Irvine, Irvine, CA 92697 USA
| | - Daniel M. Parker
- grid.266093.80000 0001 0668 7243Program in Public Health, College of Health Sciences, University of California at Irvine, Irvine, CA 92697 USA
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Wang P, Zheng X, Liu H. Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review. Front Public Health 2022; 10:1033432. [PMID: 36330112 PMCID: PMC9623320 DOI: 10.3389/fpubh.2022.1033432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.
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Affiliation(s)
- Peipei Wang
- School of Information Engineering, China University of Geosciences, Beijing, China
| | - Xinqi Zheng
- School of Information Engineering, China University of Geosciences, Beijing, China
- Technology Innovation Center for Territory Spatial Big-Data, MNR of China, Beijing, China
| | - Haiyan Liu
- School of Economic and Management, China University of Geosciences, Beijing, China
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Tan Y, Zhang Y, Cheng X, Zhou XH. Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions. Sci Rep 2022; 12:16630. [PMID: 36198691 PMCID: PMC9534028 DOI: 10.1038/s41598-022-18775-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/18/2022] [Indexed: 11/09/2022] Open
Abstract
A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.
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Affiliation(s)
- Yixuan Tan
- Department of Mathematics, Duke University, Durham, USA
| | - Yuan Zhang
- School of Statistics, Renmin University of China, Beijing, China
| | - Xiuyuan Cheng
- Department of Mathematics, Duke University, Durham, USA.
| | - Xiao-Hua Zhou
- Center for Statistical Sciences, Peking University, Beijing, China.
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China.
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40
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Recchi E, Ferrara A, Rodriguez Sanchez A, Deutschmann E, Gabrielli L, Iacus S, Bastiani L, Spyratos S, Vespe M. The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries. Sci Rep 2022; 12:16522. [PMID: 36192435 PMCID: PMC9527720 DOI: 10.1038/s41598-022-20263-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 09/12/2022] [Indexed: 11/24/2022] Open
Abstract
Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April-October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a 'travel as spark' principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.
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Affiliation(s)
- Ettore Recchi
- Sciences Po, Centre for Research On Social Inequalities (CRIS), CNRS, Paris, France.
- Migration Policy Centre (MPC), European University Institute, Florence, Italy.
| | | | - Alejandra Rodriguez Sanchez
- Humboldt Universität, Berlin, Germany
- Deutsche Zentrum für Integrations-und Migrationsforschung (DeZIM), Berlin, Germany
| | - Emanuel Deutschmann
- Migration Policy Centre (MPC), European University Institute, Florence, Italy
- Europa-Universität Flensburg, Flensburg, Germany
| | - Lorenzo Gabrielli
- Migration Policy Centre (MPC), European University Institute, Florence, Italy
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Stefano Iacus
- Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA
| | - Luca Bastiani
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy
| | | | - Michele Vespe
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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Kaleta M, Kęsik-Brodacka M, Nowak K, Olszewski R, Śliwiński T, Żółtowska I. Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks. COMPUTERS & OPERATIONS RESEARCH 2022; 146:105919. [PMID: 35755160 PMCID: PMC9212736 DOI: 10.1016/j.cor.2022.105919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/01/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.
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Affiliation(s)
- Mariusz Kaleta
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | | | | | - Robert Olszewski
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | - Tomasz Śliwiński
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
| | - Izabela Żółtowska
- Warsaw University of Technology, Pl. Politechniki 1, Warsaw 00-661, Poland
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Schoot Uiterkamp MHH, Gösgens M, Heesterbeek H, van der Hofstad R, Litvak N. The role of inter-regional mobility in forecasting SARS-CoV-2 transmission. J R Soc Interface 2022; 19:20220486. [PMID: 36043288 PMCID: PMC9428544 DOI: 10.1098/rsif.2022.0486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 08/11/2022] [Indexed: 11/21/2022] Open
Abstract
In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.
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Affiliation(s)
| | - Martijn Gösgens
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Remco van der Hofstad
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Nelly Litvak
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands
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Perrotta D, Frias-Martinez E, Pastore y Piontti A, Zhang Q, Luengo-Oroz M, Paolotti D, Tizzoni M, Vespignani A. Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia. PLoS Negl Trop Dis 2022; 16:e0010565. [PMID: 35857744 PMCID: PMC9299334 DOI: 10.1371/journal.pntd.0010565] [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: 08/19/2021] [Accepted: 06/06/2022] [Indexed: 11/19/2022] Open
Abstract
Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.
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Affiliation(s)
- Daniela Perrotta
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
- * E-mail:
| | | | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, New York, State of New York, United States of America
| | | | | | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
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Zachreson C, Chang S, Harding N, Prokopenko M. The effects of local homogeneity assumptions in metapopulation models of infectious disease. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211919. [PMID: 35845852 PMCID: PMC9277238 DOI: 10.1098/rsos.211919] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 06/23/2022] [Indexed: 05/10/2023]
Abstract
Computational models of infectious disease can be broadly categorized into two types: individual-based (agent-based) or compartmental models. While there are clear conceptual distinctions between these methodologies, a fair comparison of the approaches is difficult to achieve. Here, we carry out such a comparison by building a set of compartmental metapopulation models from an agent-based representation of a real population. By adjusting the compartmental model to approximately match the dynamics of the agent-based model, we identify two key qualitative properties of the individual-based dynamics which are lost upon aggregation into metapopulations. These are (i) the local depletion of susceptibility to infection and (ii) decoupling of different regional groups due to correlation between commuting behaviours and contact rates. The first of these effects is a general consequence of aggregating small, closely connected groups (i.e. families) into larger homogeneous metapopulations. The second can be interpreted as a consequence of aggregating two distinct types of individuals: school children, who travel short distances but have many potentially infectious contacts, and adults, who travel further but tend to have fewer contacts capable of transmitting infection. Our results could be generalized to other types of correlations between the characteristics of individuals and the behaviours that distinguish them.
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Affiliation(s)
- Cameron Zachreson
- School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Sheryl Chang
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nathan Harding
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, New South Wales 2145, Australia
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45
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Zachreson C, Chang S, Harding N, Prokopenko M. The effects of local homogeneity assumptions in metapopulation models of infectious disease. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211919. [PMID: 35845852 DOI: 10.5281/zenodo.6486795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 06/23/2022] [Indexed: 05/25/2023]
Abstract
Computational models of infectious disease can be broadly categorized into two types: individual-based (agent-based) or compartmental models. While there are clear conceptual distinctions between these methodologies, a fair comparison of the approaches is difficult to achieve. Here, we carry out such a comparison by building a set of compartmental metapopulation models from an agent-based representation of a real population. By adjusting the compartmental model to approximately match the dynamics of the agent-based model, we identify two key qualitative properties of the individual-based dynamics which are lost upon aggregation into metapopulations. These are (i) the local depletion of susceptibility to infection and (ii) decoupling of different regional groups due to correlation between commuting behaviours and contact rates. The first of these effects is a general consequence of aggregating small, closely connected groups (i.e. families) into larger homogeneous metapopulations. The second can be interpreted as a consequence of aggregating two distinct types of individuals: school children, who travel short distances but have many potentially infectious contacts, and adults, who travel further but tend to have fewer contacts capable of transmitting infection. Our results could be generalized to other types of correlations between the characteristics of individuals and the behaviours that distinguish them.
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Affiliation(s)
- Cameron Zachreson
- School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Sheryl Chang
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nathan Harding
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, New South Wales 2145, Australia
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46
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Bonnet T, Mancusi D, Zoia A. Space and time correlations for diffusion models with prompt and delayed birth-and-death events. Phys Rev E 2022; 105:064105. [PMID: 35854529 DOI: 10.1103/physreve.105.064105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Understanding the statistical properties of a collection of individuals subject to random displacements and birth-and-death events is key to several applications in physics and life sciences, encompassing the diagnostic of nuclear reactors and the analysis of epidemic patterns. Previous investigations of the critical regime, where births and deaths balance on average, have shown that highly non-Poissonian fluctuations might occur in the population, leading to spontaneous spatial clustering, and eventually to a "critical catastrophe," where fluctuations can result in the extinction of the population. A milder behavior is observed when the population size is kept constant: the fluctuations asymptotically level off and the critical catastrophe is averted. In this paper, we extend these results by considering the broader class of models with prompt and delayed birth-and-death events, which mimic the presence of precursors in nuclear reactor physics or incubation in epidemics. We consider models with and without population control mechanisms. Analytical or semi-analytical results for the density, the two-point correlation function, and the mean-squared pair distance will be derived and compared with Monte Carlo simulations, which will be used as a reference.
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Affiliation(s)
- Théophile Bonnet
- Université Paris-Saclay, CEA, Service d'Etudes des Réacteurs et de Mathématiques Appliquées, 91191, Gif-sur-Yvette, France
| | - Davide Mancusi
- Université Paris-Saclay, CEA, Service d'Etudes des Réacteurs et de Mathématiques Appliquées, 91191, Gif-sur-Yvette, France
| | - Andrea Zoia
- Université Paris-Saclay, CEA, Service d'Etudes des Réacteurs et de Mathématiques Appliquées, 91191, Gif-sur-Yvette, France
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47
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Wang B, Yang L, Han Y. Intervention strategies for epidemic spreading on bipartite metapopulation networks. Phys Rev E 2022; 105:064305. [PMID: 35854601 DOI: 10.1103/physreve.105.064305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Intervention strategies are of great significance for controlling large-scale outbreaks of epidemics. Since the spread of epidemic depends largely on the movement of individuals and the heterogeneity of the network structure, understanding potential factors that affect the epidemic is fundamental for the design of reasonable intervention strategies to suppress the epidemic. So far, most of previous studies mainly consider intervention strategies on the network composed of a single type of locations, while ignoring the movement behavior of individuals to and from locations that are composed of different types, i.e., residences and public places, which often presents heterogeneous structure. In addition, the transmission rate in public places with different population flows is heterogeneous. Inspired by the above observation, we build a bipartite metapopulation network model and propose intervention strategies based on the importance of public places. With the Markovian Chain approach, we derive the epidemic threshold under intervention strategies. Experimental results show that, compared with the uniform intervention to residences or public places, nonuniform intervention to public places is more effective for suppressing the epidemic with an increased epidemic threshold. Specifically, interventions to public places with large degree can further suppress the epidemic. Our study opens a new path for understanding the spatial epidemic spread and provides guidance for the design of intervention strategies for epidemics in the future.
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Affiliation(s)
- Bing Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, People's Republic of China
| | - Lizhen Yang
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, People's Republic of China
| | - Yuexing Han
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, People's Republic of China
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48
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Gozzi N, Chinazzi M, Davis JT, Mu K, Pastore y Piontti A, Ajelli M, Perra N, Vespignani A. Anatomy of the first six months of COVID-19 vaccination campaign in Italy. PLoS Comput Biol 2022; 18:e1010146. [PMID: 35613248 PMCID: PMC9173644 DOI: 10.1371/journal.pcbi.1010146] [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: 12/06/2021] [Revised: 06/07/2022] [Accepted: 04/27/2022] [Indexed: 01/09/2023] Open
Abstract
We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and spreading of the more transmissible Alpha variant. We consider six sub-national regions and study the effect of vaccines in terms of number of averted deaths, infections, and reduction in the Infection Fatality Rate (IFR) with respect to counterfactual scenarios with the actual non-pharmaceuticals interventions but no vaccine administration. Furthermore, we compare the effectiveness in counterfactual scenarios with different vaccines allocation strategies and vaccination rates. Our results show that, as of 2021/07/05, vaccines averted 29, 350 (IQR: [16, 454-42, 826]) deaths and 4, 256, 332 (IQR: [1, 675, 564-6, 980, 070]) infections and a new pandemic wave in the country. During the same period, they achieved a -22.2% (IQR: [-31.4%; -13.9%]) IFR reduction. We show that a campaign that would have strictly prioritized age groups at higher risk of dying from COVID-19, besides frontline workers and the fragile population, would have implied additional benefits both in terms of avoided fatalities and reduction in the IFR. Strategies targeting the most active age groups would have prevented a higher number of infections but would have been associated with more deaths. Finally, we study the effects of different vaccination intake scenarios by rescaling the number of available doses in the time period under study to those administered in other countries of reference. The modeling framework can be applied to other countries to provide a mechanistic characterization of vaccination campaigns worldwide.
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Affiliation(s)
- Nicolò Gozzi
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Jessica T. Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, United States of America
| | - Nicola Perra
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
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49
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Valgañón P, Soriano-Paños D, Arenas A, Gómez-Gardeñes J. Contagion-diffusion processes with recurrent mobility patterns of distinguishable agents. CHAOS (WOODBURY, N.Y.) 2022; 32:043102. [PMID: 35489866 DOI: 10.1063/5.0085532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
The analysis of contagion-diffusion processes in metapopulations is a powerful theoretical tool to study how mobility influences the spread of communicable diseases. Nevertheless, many metapopulation approaches use indistinguishable agents to alleviate analytical difficulties. Here, we address the impact that recurrent mobility patterns, and the spatial distribution of distinguishable agents, have on the unfolding of epidemics in large urban areas. We incorporate the distinguishable nature of agents regarding both their residence and their usual destination. The proposed model allows both a fast computation of the spatiotemporal pattern of the epidemic trajectory and the analytical calculation of the epidemic threshold. This threshold is found as the spectral radius of a mixing matrix encapsulating the residential distribution and the specific commuting patterns of agents. We prove that the simplification of indistinguishable individuals overestimates the value of the epidemic threshold.
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Affiliation(s)
- P Valgañón
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| | - D Soriano-Paños
- Instituto Gulbenkian de Ciência (IGC), 2780-156 Oeiras, Portugal
| | - A Arenas
- Departament de Matemáticas i Enginyeria Informática, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - J Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
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50
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Aguilar J, Bassolas A, Ghoshal G, Hazarie S, Kirkley A, Mazzoli M, Meloni S, Mimar S, Nicosia V, Ramasco JJ, Sadilek A. Impact of urban structure on infectious disease spreading. Sci Rep 2022; 12:3816. [PMID: 35264587 PMCID: PMC8907266 DOI: 10.1038/s41598-022-06720-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/04/2022] [Indexed: 12/31/2022] Open
Abstract
The ongoing SARS-CoV-2 pandemic has been holding the world hostage for several years now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics.
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Affiliation(s)
- Javier Aguilar
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain
| | - Aleix Bassolas
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.,School of Mathematical Sciences, Queen Mary University of London, E1 4NS, London, UK.,Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, 43007, Tarragona, Spain
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA.,Department of Computer Science, University of Rochester, Rochester, NY, 14627, USA
| | - Surendra Hazarie
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA
| | - Alec Kirkley
- School of Data Science, City University of Hong Kong, 85PF Hong Kong, China.,Department of Physics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Mattia Mazzoli
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.,INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Sandro Meloni
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain
| | - Sayat Mimar
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA
| | - Vincenzo Nicosia
- School of Mathematical Sciences, Queen Mary University of London, E1 4NS, London, UK
| | - José J Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
| | - Adam Sadilek
- Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
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