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McAndrew F, Sacks-Davis R, Abeysuriya RG, Delport D, West D, Parta I, Majumdar S, Hellard M, Scott N. COVID-19 outbreaks in residential aged care facilities: an agent-based modeling study. Front Public Health 2024; 12:1344916. [PMID: 38835609 PMCID: PMC11148262 DOI: 10.3389/fpubh.2024.1344916] [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: 11/27/2023] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia. Methods The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%). Results Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs. Conclusion Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.
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Affiliation(s)
| | - Rachel Sacks-Davis
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Romesh G Abeysuriya
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Dominic Delport
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Daniel West
- Victorian Government Department of Health, Melbourne, VIC, Australia
| | - Indra Parta
- Victorian Government Department of Health, Melbourne, VIC, Australia
| | - Suman Majumdar
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, VIC, Australia
| | - Margaret Hellard
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, VIC, Australia
- Department of Infectious Diseases, The University of Melbourne and Victorian Infectious Diseases Reference Laboratory, Parkville, VIC, Australia
| | - Nick Scott
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Qian W, Cooke A, Stanley KG, Osgood ND. Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach. J Med Internet Res 2024; 26:e38170. [PMID: 38422493 PMCID: PMC11025599 DOI: 10.2196/38170] [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: 04/01/2022] [Revised: 11/15/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. OBJECTIVE We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. METHODS We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. RESULTS Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). CONCLUSIONS We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.
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Affiliation(s)
- Weicheng Qian
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Aranock Cooke
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Kevin Gordon Stanley
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Nathaniel David Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Community Health & Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
- Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada
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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [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: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Zhu K, Yin L, Liu K, Liu J, Shi Y, Li X, Zou H, Du H. Generating synthetic population for simulating the spatiotemporal dynamics of epidemics. PLoS Comput Biol 2024; 20:e1011810. [PMID: 38346079 PMCID: PMC10890746 DOI: 10.1371/journal.pcbi.1011810] [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: 04/18/2023] [Revised: 02/23/2024] [Accepted: 01/08/2024] [Indexed: 02/25/2024] Open
Abstract
Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.
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Affiliation(s)
- Kemin Zhu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Junli Liu
- Hangzhou Institute of Technology, Xidian University, Hangzhou, China
| | - Yepeng Shi
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xuan Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hongyang Zou
- College of Management and Economics, Tianjin University, Tianjin, China
- National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China
| | - Huibin Du
- College of Management and Economics, Tianjin University, Tianjin, China
- National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China
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Zhang K, Xia Z, Huang S, Sun GQ, Lv J, Ajelli M, Ejima K, Liu QH. Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies. PLoS Comput Biol 2023; 19:e1011423. [PMID: 37656743 PMCID: PMC10501547 DOI: 10.1371/journal.pcbi.1011423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 09/14/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023] Open
Abstract
There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.
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Affiliation(s)
- Kun Zhang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhichu Xia
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Shudong Huang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Gui-Quan Sun
- Department of Mathematics, North University of China, Taiyuan, China
- Complex Systems Research Center, Shanxi University, Taiyuan, China
| | - Jiancheng Lv
- College of Computer Science, Sichuan University, Chengdu, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Keisuke Ejima
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Quan-Hui Liu
- College of Computer Science, Sichuan University, Chengdu, China
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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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Moon SA, Marathe A, Vullikanti A. Are all underimmunized measles clusters equally critical? MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.11.23288263. [PMID: 37131740 PMCID: PMC10153322 DOI: 10.1101/2023.04.11.23288263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Disruptions in routine immunizations due to the COVID-19 pandemic have been a cause of significant concern for health organizations worldwide. This research develops a system science approach to examine the potential risk of geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage at the state level for measles, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. To estimate the criticality of these clusters, a stochastic agent-based network epidemic model is used. Results show that different clusters can cause vastly different outbreaks in the region, depending on their size, location, and network characteristics. This research aims to understand why some underimmunized geographical clusters do not cause a large outbreak while others do. A detailed network analysis shows that it is not the average degree of the cluster or the percentage of underimmunized individuals in the cluster but the average eigenvector centrality of the cluster that is important in determining its potential risk.
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Affiliation(s)
- Sifat Afroj Moon
- Biocomplexity Institute, University of Virginia, Charlottesville, VA
| | - Achla Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA
- Department of Computer Science, University of Virginia, Charlottesville, VA
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Sajid MJ, Khan SAR, Sun Y, Yu Z. The long-term dynamic relationship between communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures: preparing for COVID-19-like pandemics. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:26164-26177. [PMID: 36352073 PMCID: PMC9646471 DOI: 10.1007/s11356-022-23984-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/31/2022] [Indexed: 06/16/2023]
Abstract
The spread of communicable diseases, such as COVID-19, has a detrimental effect on our socio-economic structure. In a dynamic log-run world, socio-economic and environmental factors interact to spread communicable diseases. We investigated the long-term interdependence of communicable disease spread, economic prosperity, greenhouse gas emissions, and government health expenditures in India's densely populated economy using a variance error correction (VEC) approach. The VEC model was validated using stationarity, cointegration, autocorrelation, heteroscedasticity, and normality tests. Our impulse response and variance decomposition analyses revealed that economic prosperity (GNI) significantly impacts the spread of communicable diseases, greenhouse gas emissions, government health expenditures, and GNI. Current health expenditures can reduce the need for future increases, and the spread of communicable diseases is detrimental to economic growth. Developing economies should prioritize economic growth and health spending to combat pandemics. Simultaneously, the adverse effects of economic prosperity on environmental degradation should be mitigated through policy incentives.
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Affiliation(s)
- Muhammad Jawad Sajid
- School of Engineering Management, Xuzhou University of Technology, Xuzhou, 221000, Jiangsu, China.
| | - Syed Abdul Rehman Khan
- School of Engineering Management, Xuzhou University of Technology, Xuzhou, 221000, Jiangsu, China
- Department of Business Administration, ILMA University, Karachi, 75190, Pakistan
| | - Yubo Sun
- School of Engineering Management, Xuzhou University of Technology, Xuzhou, 221000, Jiangsu, China
| | - Zhang Yu
- Department of Business Administration, ILMA University, Karachi, 75190, Pakistan
- School of Economics and Management, Chang'an University, Xi'an, 710064, China
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Ahmad RA, Imron MA, Ramadona AL, Lathifah N, Azzahra F, Widyastuti K, Fuad A. Modeling social interaction and metapopulation mobility of the COVID-19 pandemic in main cities of highly populated Java Island, Indonesia: An agent-based modeling approach. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2022.958651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
IntroductionCoronavirus transmission is strongly influenced by human mobilities and interactions within and between different geographical regions. Human mobility within and between cities is motivated by several factors, including employment, cultural-driven, holidays, and daily routines.MethodWe developed a sustained metapopulation (SAMPAN) model, an agent-based model (ABM) for simulating the effect of individual mobility and interaction behavior on the spreading of COVID-19 viruses across main cities on Java Island, Indonesia. The model considers social classes and social mixing affecting the mobility and interaction behavior within a sub-population of a city in the early pandemic. Travelers’ behavior represents the mobility among cities from central cities to other cities and commuting behavior from the surrounding area of each city.ResultsLocal sensitivity analysis using one factor at a time was performed to test the SAMPAN model, and we have identified critical parameters for the model. While validation was carried out for the Jakarta area, we are confident in implementing the model for a larger area with the concept of metapopulation dynamics. We included the area of Bogor, Depok, Bekasi, Bandung, Semarang, Surakarta, Yogyakarta, Surabaya, and Malang cities which have important roles in the COVID-19 pandemic spreading on this island.DiscussionOur SAMPAN model can simulate various waves during the first year of the pandemic caused by various phenomena of large social mobilities and interactions, particularly during religious occasions and long holidays.
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Van Yperen J, Campillo-Funollet E, Inkpen R, Memon A, Madzvamuse A. A hospital demand and capacity intervention approach for COVID-19. PLoS One 2023; 18:e0283350. [PMID: 37134085 PMCID: PMC10156009 DOI: 10.1371/journal.pone.0283350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/06/2023] [Indexed: 05/04/2023] Open
Abstract
The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached.
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Affiliation(s)
- James Van Yperen
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, United Kingdom
| | - Eduard Campillo-Funollet
- Department of Mathematics, School of Mathematical, Statistical and Actuarial Sciences, University of Kent, Canterbury, United Kingdom
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Rebecca Inkpen
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, United Kingdom
| | - Anjum Memon
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Anotida Madzvamuse
- Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, United Kingdom
- Department of Mathematics, University of Johannesburg, Johannesburg, South Africa
- Department of Mathematics, University of British Columbia, Vancouver, Canada
- Department of Mathematics, University of Pretoria, Pretoria, South Africa
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Harweg T, Wagner M, Weichert F. Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:545. [PMID: 36612868 PMCID: PMC9819456 DOI: 10.3390/ijerph20010545] [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: 10/29/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.
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Affiliation(s)
- Thomas Harweg
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
| | - Mathias Wagner
- Department of Pathology, University of Saarland Medical School, Homburg Saar Campus, Kirrberger Strasse 100, 66424 Homburg Saar, Saarland, Germany
| | - Frank Weichert
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, North Rhine-Westphalia, Germany
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Huynh PK, Setty AR, Tran QM, Yadav OP, Yodo N, Le TQ. A domain-knowledge modeling of hospital-acquired infection risk in Healthcare personnel from retrospective observational data: A case study for COVID-19. PLoS One 2022; 17:e0272919. [PMID: 36409727 PMCID: PMC9678325 DOI: 10.1371/journal.pone.0272919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 07/28/2022] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION Hospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral infections, and subsequently higher rates of morbidity and mortality. MATERIALS AND METHODS We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets, including individual-level factors, engineering control factors, and administrative control factors. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies. RESULTS Regarding the individual-level risk model, the variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California's higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level. CONCLUSION The accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.
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Affiliation(s)
- Phat K. Huynh
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, United States of America
- Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, North Dakota, United States of America
| | - Arveity R. Setty
- University of North Dakota, Fargo, North Dakota, United States of America
- Sanford Hospital, Fargo, North Dakota, United States of America
| | - Quan M. Tran
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Om P. Yadav
- Department of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, North Carolina, United States of America
| | - Nita Yodo
- Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, North Dakota, United States of America
| | - Trung Q. Le
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, United States of America
- Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, North Dakota, United States of America
- * E-mail:
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Novakovic A, Marshall AH. The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions. PATTERN RECOGNITION 2022; 130:108790. [PMID: 35601479 PMCID: PMC9107333 DOI: 10.1016/j.patcog.2022.108790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 05/16/2023]
Abstract
The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.
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Affiliation(s)
- Aleksandar Novakovic
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
| | - Adele H Marshall
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
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Dong T, Dong W, Xu Q. Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10242. [PMID: 36011877 PMCID: PMC9407715 DOI: 10.3390/ijerph191610242] [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: 07/13/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China's seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the "heart, window and name card" of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.
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Affiliation(s)
- Tao Dong
- School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
| | - Wen Dong
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
- GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
| | - Quanli Xu
- Faculty of Geography, Yunnan Normal University, Kunming 650500, China
- GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
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15
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Liu J, Ong GP, Pang VJ. Modelling effectiveness of COVID-19 pandemic control policies using an Area-based SEIR model with consideration of infection during interzonal travel. TRANSPORTATION RESEARCH. PART A, POLICY AND PRACTICE 2022; 161:25-47. [PMID: 35603124 PMCID: PMC9110328 DOI: 10.1016/j.tra.2022.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This paper studies the effectiveness of several pandemic restriction measures adopted in Singapore during the COVID-19 outbreak. To this end, the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model widely used to describe the dynamic process of epidemic propagation is extended to an area-based SEIR model with the consideration of exposure to infections during commute and quarantine. The proposed model considers infections within areas and infections occurred during the commute of individuals. A case study of the Singapore MRT system is presented to show the effectiveness of pandemic restriction policies implemented in Singapore, namely social distancing, work shift and Circuit Breaker (CB) and phase advisories. A long-term investigation of COVID-19 pandemic in Singapore is performed, and the disease transmission dynamics in 2020-2021 (which covers the first wave and second wave of COVID-19 pandemic in Singapore) is modelled.
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Affiliation(s)
- Jielun Liu
- Department of Civil & Environmental Engineering, National University of Singapore, 117576, Singapore
| | - Ghim Ping Ong
- Department of Civil & Environmental Engineering, National University of Singapore, 117576, Singapore
| | - Vincent Junxiong Pang
- Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore
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16
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Guo X, Chen P, Liang S, Jiao Z, Li L, Yan J, Huang Y, Liu Y, Fan W. PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning. Med Decis Making 2022; 42:1064-1077. [PMID: 35775610 DOI: 10.1177/0272989x221107902] [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: 11/16/2022]
Abstract
BACKGROUND Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information. METHODS We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth. RESULTS Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond. LIMITATIONS The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control. CONCLUSIONS The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy. HIGHLIGHTS We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.
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Affiliation(s)
- Xudong Guo
- Department of Automation, Tsinghua University, Beijing, China
| | - Peiyu Chen
- Department of Automation, Tsinghua University, Beijing, China
| | - Shihao Liang
- Yidu Cloud AI Lab, Yidu Cloud (Beijing) Technology Co., Ltd, Beijing, China
| | - Zengtao Jiao
- Yidu Cloud AI Lab, Yidu Cloud (Beijing) Technology Co., Ltd, Beijing, China
| | - Linfeng Li
- Yidu Cloud AI Lab, Yidu Cloud (Beijing) Technology Co., Ltd, Beijing, China
| | - Jun Yan
- Yidu Cloud AI Lab, Yidu Cloud (Beijing) Technology Co., Ltd, Beijing, China
| | - Yadong Huang
- Department of Automation, Tsinghua University, Beijing, China
| | - Yi Liu
- Department of Automation, Tsinghua University, Beijing, China
| | - Wenhui Fan
- Department of Automation, Tsinghua University, Beijing, China
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Guerra SS, Seixas E, Ribeiro AI, Duarte R. Tell me where you went, I may tell who you infected. JORNAL BRASILEIRO DE PNEUMOLOGIA : PUBLICACAO OFICIAL DA SOCIEDADE BRASILEIRA DE PNEUMOLOGIA E TISILOGIA 2022; 48:e20220099. [PMID: 35703673 PMCID: PMC9262441 DOI: 10.36416/1806-3756/e20220099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Sónia Silva Guerra
- . Serviço de Pneumologia, Centro Hospitalar Tondela-Viseu, Viseu, Portugal
| | - Eduarda Seixas
- . Serviço de Pneumologia, Centro Hospitalar Baixo-Vouga, Aveiro, Portugal
| | - Ana Isabel Ribeiro
- . EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,. Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional, Porto, Portugal.,. Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal
| | - Raquel Duarte
- . EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,. ICBAS-UP - Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Porto, Portugal.,. Serviço de Pneumologia, Centro Hospitalar de Vila Nova de Gaia, Vila Nova de Gaia, Portugal.,. Unidade de Investigação Clínica, Administração Regional de Saúde do Norte, Porto, Portugal
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18
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Martinez K, Brown G, Pankavich S. Spatially-heterogeneous embedded stochastic SEIR models for the 2014–2016 Ebola outbreak in West Africa. Spat Spatiotemporal Epidemiol 2022; 41:100505. [DOI: 10.1016/j.sste.2022.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 12/03/2021] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
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Abstract
Epidemics of infectious diseases, such as the one caused by the rapid spread of the coronavirus disease 2019 (COVID-19), have tested the world's more advanced health systems and have caused an enormous societal and economic damage. The mechanism of contagion is well understood. As people move around, over time, they regularly engage in social interactions. The spatiotemporal network representing these interactions constitutes the backbone on which an epidemic spreads, causing outbreaks. At the same time, advanced technological responses have claimed some success in controlling the epidemic based on digital contact tracing technologies. Motivated by these observations, we design, develop and evaluate a stochastic agent-basedSEIRmodel of epidemic spreading in spatiotemporal networks informed by mobility data of individuals (trajectories). The model focuses on individual variation in mobility patterns that affects the degree of exposure to the disease. Understanding the role that individual nodes play in the process of disease spreading through network effects is fundamental as it allows to (i) assess the risk of infection of individuals, (ii) assess the size of a disease outbreak due to specific individuals, and (iii) assess targeted intervention strategies that aim to control the epidemic spreading. We perform a comprehensive analysis of the model employing COVID-19 as a use case. The results indicate that simple individual-based intervention strategies that exhibit significant network effects can effectively control the spread of an epidemic. We have also demonstrated that targeted interventions can outperform generic intervention strategies. Overall, our work provides an evidence-based data-driven model to support decision making and inform public policy regarding intervention strategies for containing or mitigating the epidemic spread.
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20
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Asgary A, Blue H, Solis AO, McCarthy Z, Najafabadi M, Tofighi MA, Wu J. Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052635. [PMID: 35270344 PMCID: PMC8910468 DOI: 10.3390/ijerph19052635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 02/01/2023]
Abstract
The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facility’s population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes.
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Affiliation(s)
- Ali Asgary
- Disaster and Emergency Management Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (A.A.); (H.B.)
| | - Hudson Blue
- Disaster and Emergency Management Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (A.A.); (H.B.)
| | - Adriano O. Solis
- Decision Sciences Area, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada;
| | - Zachary McCarthy
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; (Z.M.); (J.W.)
| | - Mahdi Najafabadi
- Advanced Disaster, Emergency, and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada; (M.N.); (M.A.T.)
| | - Mohammad Ali Tofighi
- Advanced Disaster, Emergency, and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada; (M.N.); (M.A.T.)
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; (Z.M.); (J.W.)
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21
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Palomo-Briones GA, Siller M, Grignard A. An agent-based model of the dual causality between individual and collective behaviors in an epidemic. Comput Biol Med 2022; 141:104995. [PMID: 34774336 PMCID: PMC8570178 DOI: 10.1016/j.compbiomed.2021.104995] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 12/16/2022]
Abstract
The evolution of an epidemic is strongly related to the behavior of individuals, and the consideration of cause and effect of social phenomena can extend epidemiological models and allow for better identification, prediction and control of the impacts of containment and mitigation measures. This work proposes an agent-based model to simulate the double causality that exists between individual behaviors, influenced by the cultural orientation of a population, and the evolution of an epidemic, focusing on recent studies on the COVID-19 pandemic. To do this, concepts from the social sciences are used, such as the theory of planned behavior, as well as Bayesian inference to abstract the decision-making processes involved in human behavior. A set of simulation experiments with different populations was developed to demonstrate the role that the cultural orientation of a population plays in the management of an epidemic. The results agree with the revised theory, showing that in populations that have a greater inclination towards collectivism, epidemiological indicators evolve in a better way than in those populations where the culture is individualistic. This work contributes to the field of computational epidemiology by providing a new way of including the social aspects of studied populations in agent-based models to help develop better interventions.
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Affiliation(s)
| | - Mario Siller
- Cinvestav Unidad Guadalajara, Av. Del Bosque, 1145, El Bajio, Zapopan, Jal, Mexico,Corresponding author
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22
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Chumachenko D, Meniailov I, Bazilevych K, Chumachenko T, Yakovlev S. On intelligent agent-based simulation of COVID-19 epidemic process in Ukraine. PROCEDIA COMPUTER SCIENCE 2022; 198:706-711. [PMID: 35103090 PMCID: PMC8790955 DOI: 10.1016/j.procs.2021.12.310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/29/2022]
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Lopes PH, Wellacott L, de Almeida L, Villavicencio LMM, Moreira ALDL, Andrade DS, Souza AMDC, de Sousa RKR, Silva PDS, Lima L, Lones M, do Nascimento JD, Vargas PA, Moioli RC, Blanco Figuerola W, Rennó-Costa C. Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000540. [PMID: 36962551 PMCID: PMC10021960 DOI: 10.1371/journal.pgph.0000540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/23/2022] [Indexed: 11/05/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic hit almost all cities in Brazil in early 2020 and lasted for several months. Despite the effort of local state and municipal governments, an inhomogeneous nationwide response resulted in a death toll amongst the highest recorded globally. To evaluate the impact of the nonpharmaceutical governmental interventions applied by different cities-such as the closure of schools and businesses in general-in the evolution and epidemic spread of SARS-CoV-2, we constructed a full-sized agent-based epidemiological model adjusted to the singularities of particular cities. The model incorporates detailed demographic information, mobility networks segregated by economic segments, and restricting bills enacted during the pandemic period. As a case study, we analyzed the early response of the City of Natal-a midsized state capital-to the pandemic. Although our results indicate that the government response could be improved, the restrictive mobility acts saved many lives. The simulations show that a detailed analysis of alternative scenarios can inform policymakers about the most relevant measures for similar pandemic surges and help develop future response protocols.
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Affiliation(s)
- Paulo Henrique Lopes
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Liam Wellacott
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Leandro de Almeida
- Physics Department, Federal University of Rio Grande do Norte, Natal, Brazil
- Laboratório Nacional de Astrofísica, Itajubá, MG, Brazil
| | | | - André Luiz de Lucena Moreira
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Dhiego Souto Andrade
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Alyson Matheus de Carvalho Souza
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | | | | | - Luciana Lima
- Demography Graduate Program, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Michael Lones
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | | | - Patricia A Vargas
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Renan Cipriano Moioli
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Wilfredo Blanco Figuerola
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Computer Science Department, State University of Rio Grande do Norte, Natal, Brazil
| | - César Rennó-Costa
- Bioinformatics Multidisciplinary Environment of the Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Robotics Laboratory, Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2022; 49:1265-1293. [PMID: 34276105 PMCID: PMC8275455 DOI: 10.1007/s11116-021-10210-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/09/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J. Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H. Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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Dunant A, Bebbington M, Davies T, Horton P. Multihazards Scenario Generator: A Network-Based Simulation of Natural Disasters. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2021; 41:2154-2176. [PMID: 33733516 DOI: 10.1111/risa.13723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 01/13/2020] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
The impact of natural disasters has been increasing in recent years. Despite the developing international interest in multihazard events, few studies quantify the dynamic interactions that characterize these phenomena. It is argued that without considering the dynamic complexity of natural catastrophes, impact assessments will underestimate risk and misinform emergency management priorities. The ability to generate multihazard scenarios with impacts at a desired level is important for emergency planning and resilience assessment. This article demonstrates a framework for using graph theory and networks to generate and model the complex impacts of multihazard scenarios. First, the combination of maximal hazard footprints and exposed nodes (e.g., infrastructure) is used to create the hazard network. Iterative simulation of the network, defined by actual hazard magnitudes, is then used to provide the overall compounded impact from a sequence of hazards. Outputs of the method are used to study distributional ranges of multihazards impact. The 2016 Kaikōura earthquake is used as a calibrating event to demonstrate that the method can reproduce the same scale of impacts as a real event. The cascading hazards included numerous landslides, allowing us to show that the scenario set generated includes the actual impacts that occurred during the 2016 events.
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Affiliation(s)
- Alexandre Dunant
- Department of Geological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Mark Bebbington
- Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand
| | - Tim Davies
- Department of Geological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Pascal Horton
- Institute of Geography, University of Bern, Bern, Switzerland
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Carballosa A, Balsa-Barreiro J, Garea A, García-Selfa D, Miramontes Á, Muñuzuri AP. Risk evaluation at municipality level of a COVID-19 outbreak incorporating relevant geographic data: the study case of Galicia. Sci Rep 2021; 11:21248. [PMID: 34711874 PMCID: PMC8553869 DOI: 10.1038/s41598-021-00342-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 10/11/2021] [Indexed: 11/22/2022] Open
Abstract
The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus.
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Affiliation(s)
- Alejandro Carballosa
- Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - José Balsa-Barreiro
- Group of Territorial Analysis, Institute IDEGA, University of Santiago de Compostela, Santiago de Compostela, Spain.,MIT Media Lab, Massachusetts Institute of Technology, 75 Amherst St, Cambridge, MA, 02139, USA
| | - Adrián Garea
- Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain
| | - David García-Selfa
- Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.,CESGA (Supercomputing Center of Galicia), Avda. de Vigo s/n, Campus Sur, 15705, Santiago de Compostela, Spain
| | - Ángel Miramontes
- Group of Territorial Analysis, Institute IDEGA, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Alberto P Muñuzuri
- Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
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Šušteršič T, Blagojević A, Cvetković D, Cvetković A, Lorencin I, Šegota SB, Milovanović D, Baskić D, Car Z, Filipović N. Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union. Front Public Health 2021; 9:727274. [PMID: 34778171 PMCID: PMC8580942 DOI: 10.3389/fpubh.2021.727274] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/23/2021] [Indexed: 01/08/2023] Open
Abstract
Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.
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Affiliation(s)
- Tijana Šušteršič
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
| | - Andjela Blagojević
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
| | - Danijela Cvetković
- Institute for Information Technologies, University of Kragujevac, Kragujevac, Serbia
| | - Aleksandar Cvetković
- Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Ivan Lorencin
- Faculty of Engineering, University of Rijeka, Rijeka, Croatia
| | | | - Dragan Milovanović
- Clinical Centre Kragujevac, Kragujevac, Serbia
- Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Dejan Baskić
- Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
- Institute of Public Health Kragujevac, Kragujevac, Serbia
| | - Zlatan Car
- Faculty of Engineering, University of Rijeka, Rijeka, Croatia
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
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Uncertainty in geospatial health: challenges and opportunities ahead. Ann Epidemiol 2021; 65:15-30. [PMID: 34656750 DOI: 10.1016/j.annepidem.2021.10.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.
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Almagor J, Martin A, McCrorie P, Mitchell R. How can an agent-based model explore the impact of interventions on children's physical activity in an urban environment? Health Place 2021; 72:102688. [PMID: 34628149 PMCID: PMC8633766 DOI: 10.1016/j.healthplace.2021.102688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/23/2021] [Accepted: 09/28/2021] [Indexed: 12/28/2022]
Abstract
Insufficient physical activity (PA) among most children and adolescents is a global problem that is undermining the realisation of numerous developmental and health benefits. The aim of this study was to explore the potential impact of interventions on PA by using an agent-based model (ABM) simulating children's daily activities in an urban environment. Three domains for interventions were explored: outdoor play, school physical education and active travel. Simulated interventions increased children's average daily moderate-to-vigorous PA by 2–13 min and reduced the percentage of children not meeting PA guidelines, from 34% to 10%–29%, depending on the intervention. Promotion of active travel and outdoor play benefited more those in a higher socio-economic position. Agents' interactions suggested that: encouraging activity in diverse groups will reduce percentage of the least active in the population; and initiating outdoor events in neighbourhoods can generate an enhancing effect on children's engagement in PA. The ABM provided measurable outcomes for interventions that are difficult to estimate using reductionist methods. We suggest that ABMs should be used more commonly to explore the complexity of the social-environmental PA system. We developed an agent-based model simulating children's daily physical activity. Agents perform typical daily activities in a virtual urban model of Glasgow city. Simulations explore impact of interventions on outdoor play, school, active travel. Promotion of active travel had a differential impact across socio-economic position. Outdoor play intervention produced a non-linear increase in physical activity.
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Affiliation(s)
- Jonatan Almagor
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, G3 7HR, Glasgow, Scotland, UK.
| | - Anne Martin
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, G3 7HR, Glasgow, Scotland, UK
| | - Paul McCrorie
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, G3 7HR, Glasgow, Scotland, UK
| | - Rich Mitchell
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Berkeley Square, 99 Berkeley Street, G3 7HR, Glasgow, Scotland, UK
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30
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Nunner H, Buskens V, Kretzschmar M. A model for the co-evolution of dynamic social networks and infectious disease dynamics. COMPUTATIONAL SOCIAL NETWORKS 2021; 8:19. [PMID: 34642614 PMCID: PMC8495675 DOI: 10.1186/s40649-021-00098-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 08/29/2021] [Indexed: 12/12/2022]
Abstract
Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.
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Affiliation(s)
- Hendrik Nunner
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Vincent Buskens
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Mirjam Kretzschmar
- Centre for Complex Systems Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands
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31
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Asgary A, Najafabadi MM, Wendel SK, Resnick-Ault D, Zane RD, Wu J. Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation. HEALTH AND TECHNOLOGY 2021; 11:1359-1368. [PMID: 34631358 PMCID: PMC8492036 DOI: 10.1007/s12553-021-00594-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 08/31/2021] [Indexed: 01/15/2023]
Abstract
Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.
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Affiliation(s)
- Ali Asgary
- Disaster & Emergency Management, York University, 4700 Keele Street, Toronto, ON M3J 1P3 Canada
| | - Mahdi M. Najafabadi
- Postdoc Research Associate, City University of New York’s Graduate School of Public Health, New York, NY USA
| | - Sarah K. Wendel
- Department of Emergency Medicine, University of Colorado School of Medicine, Denver, CO USA
| | - Daniel Resnick-Ault
- Department of Emergency Medicine, University of Colorado School of Medicine, Denver, CO USA
| | - Richard D. Zane
- Department of Emergency Medicine, University of Colorado School of Medicine, Denver, CO USA
| | - Jianhong Wu
- Department of Mathematics and Statistics, University Distinguished Research Professor, York University, Toronto, ON Canada
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Younsi FZ, Hamdadou D. Dynamic Contact Network Simulation Model Based on Multi-Agent Systems. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2021. [DOI: 10.4018/ijhisi.289462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Epidemic spread poses a new challenge to the public health community. Given its very rapid spread, public health decision makers are mobilized to fight and stop it by setting disposal several tools. This ongoing research aims to design and develop a new system based on Multi-Agent System, Suscpetible-Infected-Removed (SIR) model and Geographic Information System (GIS) for public health officials. The proposed system aimed to find out the real and responsible factors for the epidemic spread and explaining its emergence in human population. Moreover, it allows to monitor the disease spread in space and time and provides rapid early warning alert of disease outbreaks. In this paper, a multi-agent epidemic spread simulation system is proposed, discussed and implemented. Simulation result shows that the proposed multi-agent disease spread system performs well in reflecting the evolution of dynamic disease spread system's behavior
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Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia. INFORMATION 2021. [DOI: 10.3390/info12080325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.
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Qi B, Tan J, Zhang Q, Cao M, Wang X, Zou Y. Unfixed Movement Route Model, Non-Overcrowding and Social Distancing Reduce the Spread of COVID-19 in Sporting Facilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8212. [PMID: 34360504 PMCID: PMC8346128 DOI: 10.3390/ijerph18158212] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/18/2021] [Accepted: 07/28/2021] [Indexed: 01/08/2023]
Abstract
Localized outbreaks of COVID-19 have been reported in sporting facilities. This study used the Agent-based Modeling (ABM) method to analyze the transmission rate of COVID-19 in different sporting models, sporting spaces per capita, and situations of gathering, which contributes to understanding how COVID-19 transmits in sports facilities. The simulation results show that the transmission rate of COVID-19 was higher under the Fixed Movement Route (FMR) than under the Unfixed Movement Route (UMR) in 10 different sporting spaces per capita (1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 m2) (p = 0.000). For both FMR and UMR, the larger the sporting space per capita, the lower the virus transmission rate. Additionally, when the sporting space per capita increases from 4 m2 to 5 m2, the virus transmission rate decreases most significantly (p = 0.000). In the FMR model with a per capita sporting space of 5 m2, minimizing gathering (no more than three people) could significantly slow down the transmission rate of the COVID-19 virus (p < 0.05). This study concluded that: (1) The UMR model is suggested in training facilities or playing grounds; (2) The sporting space should be non-overcrowding, and it is recommended that the sporting space per capita in the sporting grounds should not be less than 5 m2; (3) It is important to maintain safe social distancing and minimize gathering (no more than three people) when exercising.
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Affiliation(s)
- Bote Qi
- Department of Sport and Exercise Science, College of Education, Zhejiang University, 886 Yuhangtang Road, Hangzhou 310058, China; (B.Q.); (J.T.)
| | - Jingwang Tan
- Department of Sport and Exercise Science, College of Education, Zhejiang University, 886 Yuhangtang Road, Hangzhou 310058, China; (B.Q.); (J.T.)
| | - Qingwen Zhang
- College of Physical Education and Training, Shanghai University of Sport, 399 Chang Hai Road, Shanghai 200438, China;
| | - Meng Cao
- Institute of Physical Education, Normal College, Shenzhen University, 3688 Nan Hai Road, Shenzhen 518061, China;
| | - Xingxiong Wang
- College of Management and Economics, Tianjin University, 92 Wei Jin Road, Tianjin 300072, China;
| | - Yu Zou
- Department of Sport and Exercise Science, College of Education, Zhejiang University, 886 Yuhangtang Road, Hangzhou 310058, China; (B.Q.); (J.T.)
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The suppression effect of emotional contagion in the COVID-19 pandemic: A multi-layer hybrid modelling and simulation approach. PLoS One 2021; 16:e0253579. [PMID: 34320025 PMCID: PMC8318274 DOI: 10.1371/journal.pone.0253579] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/08/2021] [Indexed: 01/08/2023] Open
Abstract
The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren’t emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics.
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Abstract
Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters, such as the extent to which hygiene and social distancing are observed in a population. Our results provide qualitative indications of the effects of various policies and parameters, for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing is more important than personal hygiene, and that the growth of infection is significantly reduced for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.
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37
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2021; 49:1265-1293. [PMID: 34276105 DOI: 10.1101/2020.06.20.20135186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/28/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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38
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Paez A, Lopez FA, Menezes T, Cavalcanti R, Pitta MGDR. A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain. GEOGRAPHICAL ANALYSIS 2021; 53:397-421. [PMID: 32836331 PMCID: PMC7300768 DOI: 10.1111/gean.12241] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/06/2020] [Accepted: 05/12/2020] [Indexed: 05/08/2023]
Abstract
The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.
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Affiliation(s)
- Antonio Paez
- School of Geography and Earth SciencesMcMaster UniversityHamiltonONCanada
| | - Fernando A. Lopez
- Departamento de Metodos Cuantitativos, Ciencias Juridicas, y Lenguas ModernasUniversidad Politecnica de CartagenaCartagenaSpain
| | - Tatiane Menezes
- Departamento de EconomiaUniversidade Federal de PernambucoRecifeBrazil
| | - Renata Cavalcanti
- Núcleo de Pesquisa em Inovação Terapêutica NUPIT/UFPEUniversidade Federal de PernambucoRecifeBrazil
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Giacopelli G. A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development. JMIRX MED 2021; 2:e24630. [PMID: 34606524 PMCID: PMC8459738 DOI: 10.2196/24630] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/30/2021] [Accepted: 06/25/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND The COVID-19 outbreak, an event of global concern, has provided scientists the opportunity to use mathematical modeling to run simulations and test theories about the pandemic. OBJECTIVE The aim of this study was to propose a full-scale individual-based model of the COVID-19 outbreak in Lombardy, Italy, to test various scenarios pertaining to the pandemic and achieve novel performance metrics. METHODS The model was designed to simulate all 10 million inhabitants of Lombardy person by person via a simple agent-based approach using a commercial computer. In order to obtain performance data, a collision detection model was developed to enable cluster nodes in small cells that can be processed fully in parallel. Within this collision detection model, an epidemic model based mostly on experimental findings about COVID-19 was developed. RESULTS The model was used to explain the behavior of the COVID-19 outbreak in Lombardy. Different parameters were used to simulate various scenarios relating to social distancing and lockdown. According to the model, these simple actions were enough to control the virus. The model also explained the decline in cases in the spring and simulated a hypothetical vaccination scenario, confirming, for example, the herd immunity threshold computed in previous works. CONCLUSIONS The model made it possible to test the impact of people's daily actions (eg, maintaining social distance) on the epidemic and to investigate interactions among agents within a social network. It also provided insight on the impact of a hypothetical vaccine.
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Affiliation(s)
- Giuseppe Giacopelli
- Department of Mathematics and Informatics University of Palermo Palermo Italy
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40
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Paez A, Lopez FA, Menezes T, Cavalcanti R, Pitta MGDR. A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain. GEOGRAPHICAL ANALYSIS 2021; 53:397-421. [PMID: 32836331 PMCID: PMC7300768 DOI: 10.1111/gean.12241 10.1111/gean.12241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/06/2020] [Accepted: 05/12/2020] [Indexed: 06/12/2023]
Abstract
The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.
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Affiliation(s)
- Antonio Paez
- School of Geography and Earth SciencesMcMaster UniversityHamiltonONCanada
| | - Fernando A. Lopez
- Departamento de Metodos Cuantitativos, Ciencias Juridicas, y Lenguas ModernasUniversidad Politecnica de CartagenaCartagenaSpain
| | - Tatiane Menezes
- Departamento de EconomiaUniversidade Federal de PernambucoRecifeBrazil
| | - Renata Cavalcanti
- Núcleo de Pesquisa em Inovação Terapêutica NUPIT/UFPEUniversidade Federal de PernambucoRecifeBrazil
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41
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Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.
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Meierhofer MB, Lilley TM, Ruokolainen L, Johnson JS, Parratt SR, Morrison ML, Pierce BL, Evans JW, Anttila J. Ten-year projection of white-nose syndrome disease dynamics at the southern leading-edge of infection in North America. Proc Biol Sci 2021; 288:20210719. [PMID: 34074117 PMCID: PMC8170204 DOI: 10.1098/rspb.2021.0719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Predicting the emergence and spread of infectious diseases is critical for the effective conservation of biodiversity. White-nose syndrome (WNS), an emerging infectious disease of bats, has resulted in high mortality in eastern North America. Because the fungal causative agent Pseudogymnoascus destructans is constrained by temperature and humidity, spread dynamics may vary by geography. Environmental conditions in the southern part of the continent are different than the northeast, where disease dynamics are typically studied, making it difficult to predict how the disease will manifest. Herein, we modelled WNS pathogen spread in Texas based on cave densities and average dispersal distances of hosts, projecting these results out to 10 years. We parameterized a predictive model of WNS epidemiology and its effects on bat populations with observed cave environmental data. Our model suggests that bat populations in northern Texas will be more affected by WNS mortality than southern Texas. As such, we recommend prioritizing the preservation of large overwintering colonies of bats in north Texas through management actions. Our model illustrates that infectious disease spread and infectious disease severity can become uncoupled over a gradient of environmental variation and highlight the importance of understanding host, pathogen and environmental conditions across a breadth of environments.
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Affiliation(s)
- Melissa B Meierhofer
- Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, 534 John Kimbrough Boulevard, College Station, TX 77843, USA.,Natural Resources Institute, Texas A&M University, 534 John Kimbrough Boulevard, College Station, TX 77843, USA.,Finnish Museum of Natural History, University of Helsinki, Pohjoinen Rautatiekatu 13, 00100 Helsinki, Finland
| | - Thomas M Lilley
- Finnish Museum of Natural History, University of Helsinki, Pohjoinen Rautatiekatu 13, 00100 Helsinki, Finland
| | - Lasse Ruokolainen
- Department of Biosciences, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Joseph S Johnson
- Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
| | - Steven R Parratt
- Department of Ecology and Evolution, University of Liverpool, Liverpool L69 7BE, UK
| | - Michael L Morrison
- Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, 534 John Kimbrough Boulevard, College Station, TX 77843, USA
| | - Brian L Pierce
- Natural Resources Institute, Texas A&M University, 534 John Kimbrough Boulevard, College Station, TX 77843, USA
| | - Jonah W Evans
- Wildlife Diversity Program, Texas Parks and Wildlife, 4200 Smith School Road, Austin, TX 78744, USA
| | - Jani Anttila
- Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland
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43
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Banks DL, Hooten MB. Statistical Challenges in Agent-Based Modeling. AM STAT 2021. [DOI: 10.1080/00031305.2021.1900914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- David L. Banks
- Department of Statistical Science, Duke University, Durham,NC
| | - Mevin B. Hooten
- Department of Fish, Wildlife, and Conservation Biology, U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO
- Department of Statistics, Colorado State University, Fort Collins, CO
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Robertson LS. Predictors of COVID-19-Confirmed Cases and Fatalities in 883 US Counties with a Population of 50,000 or More: Estimated Effect of Initial Prevention Policies. J Urban Health 2021; 98:205-210. [PMID: 33492557 PMCID: PMC7831623 DOI: 10.1007/s11524-021-00514-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 11/17/2022]
Abstract
Control of diseases transmitted from person to person may be more effectively and less economically damaging if preventive and ameliorative efforts are focused on the more vulnerable local areas rather than entire countries, provinces, or states. The spread of the COVID-19 virus is highly concentrated in urban US counties. Sixteen factors known or thought to be related to spread of the COVID-19 virus were studied by Poisson regression analysis of confirmed cases and deaths in 883 US counties with a population of 50,000 or more as of May 31, 2020. Evidence of crowding in homes, workplaces, religious gatherings, preexisting health conditions in the population, and local economic and demographic conditions, with one exception, was predictive of incidence and mortality. Based on the correlation of cases and deaths to length of stay-at-home orders, the orders were associated with about 52% reduced cases and about 55% reduced deaths from those expected without the orders.
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Truszkowska A, Behring B, Hasanyan J, Zino L, Butail S, Caroppo E, Jiang Z, Rizzo A, Porfiri M. High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town. ADVANCED THEORY AND SIMULATIONS 2021; 4:2000277. [PMID: 33786413 PMCID: PMC7995144 DOI: 10.1002/adts.202000277] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/23/2020] [Indexed: 12/18/2022]
Abstract
Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY-one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches-in hospitals or drive-through facilities-and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.
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Affiliation(s)
- Agnieszka Truszkowska
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Brandon Behring
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Jalil Hasanyan
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Lorenzo Zino
- Faculty of Science and EngineeringUniversity of GroningenNijenborgh 49747 AGGroningenThe Netherlands
| | - Sachit Butail
- Department of Mechanical EngineeringNorthern Illinois UniversityDeKalbIL60115USA
| | - Emanuele Caroppo
- Mental Health DepartmentLocal Health Unit ROMA 200174RomeItaly
- Italy University Research Center He.R.A.Università Cattolica del Sacro Cuore00168RomeItaly
| | - Zhong‐Ping Jiang
- Department of Electrical and Computer EngineeringTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
| | - Alessandro Rizzo
- Department of Electronics and TelecommunicationsPolitecnico di Torino10129TorinoItaly
- Office of InnovationTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
- Department of Biomedical EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
- Center for Urban Science and ProgressTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
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46
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Shamil MS, Farheen F, Ibtehaz N, Khan IM, Rahman MS. An Agent-Based Modeling of COVID-19: Validation, Analysis, and Recommendations. Cognit Comput 2021:1-12. [PMID: 33643473 PMCID: PMC7893846 DOI: 10.1007/s12559-020-09801-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/30/2020] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted non-pharmaceutical interventions (NPI) to slow down the spread. This study proposes an agent-based model that simulates the spread of COVID-19 among the inhabitants of a city. The agent-based model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of total COVID-19 cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford County, KS, USA. Different interventions, including contact tracing, are applied on a scaled-down version of New York City, USA, and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with city-wide lockdown results in the effective reproduction number (R t ) to fall below 1 within 3 weeks of intervention. For 75% or more smartphone users, new infections are eliminated, and the spread is contained within 3 months of intervention. Contact tracing accompanied with early lockdown can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lockdown can go a long way to contain the spread. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at (10.1007/s12559-020-09801-w).
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Affiliation(s)
- Md. Salman Shamil
- Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205 Bangladesh
| | - Farhanaz Farheen
- Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205 Bangladesh
| | - Nabil Ibtehaz
- Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205 Bangladesh
| | | | - M. Sohel Rahman
- Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205 Bangladesh
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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Marschik T, Kopacka I, Stockreiter S, Schmoll F, Hiesel J, Höflechner-Pöltl A, Käsbohrer A, Pinior B. The Epidemiological and Economic Impact of a Potential Foot-and-Mouth Disease Outbreak in Austria. Front Vet Sci 2021; 7:594753. [PMID: 33521078 PMCID: PMC7838521 DOI: 10.3389/fvets.2020.594753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/10/2020] [Indexed: 01/15/2023] Open
Abstract
An outbreak of foot-and mouth disease (FMD) in an FMD-free country such as Austria would likely have serious consequences for the national livestock sector and economy. The objective of this study was to analyse the epidemiological and economic impact of an FMD outbreak in Austria in order to (i) evaluate the effectiveness of different control measures in two Austrian regions with different livestock structure and density, (ii) analyse the associated costs of the control measures and the losses resulting from trade restrictions on livestock and livestock products and (iii) assess the resources that would be required to control the FMD outbreak. The European Foot-and-Mouth Disease Spread Model (EuFMDiS) was used to simulate a potential FMD outbreak. Based on the epidemiological outputs of the model, the economic impact of the outbreak was assessed. The analysis of the simulations showed that the success of control strategies depends largely on the type of control measures, the geographical location, the availability of sufficient resources, and the speed of intervention. The comparison of different control strategies suggested that from an economic point of view the implementation of additional control measures, such as pre-emptive depopulation of susceptible herds, would be efficient if the epidemic started in an area with high livestock density. Depending on the chosen control measures and the affected region, the majority of the total costs would be attributable to export losses (e.g., each day of an FMD epidemic costs Austria € 9-16 million). Our analysis indicated that the currently estimated resources for surveillance, cleaning, and disinfection during an FMD outbreak in Austria would be insufficient, which would lead to an extended epidemic control duration. We have shown that the control of an FMD outbreak can be improved by implementing a contingency strategy adapted to the affected region and by placing particular focus on an optimal resource allocation and rapid detection of the disease in Austria. The model results can assist veterinary authorities in planning resources and implementing cost-effective control measures for future outbreaks of highly contagious viral diseases.
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Affiliation(s)
- Tatiana Marschik
- Unit of Veterinary Public Health and Epidemiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine Vienna, Vienna, Austria
- Division for Animal Health, Austrian Agency for Health and Food Safety (AGES), Mödling, Austria
| | - Ian Kopacka
- Division for Data, Statistics and Risk Assessment, Austrian Agency for Health and Food Safety (AGES), Graz, Austria
| | - Simon Stockreiter
- Department for Animal Health and Animal Disease Control, Federal Ministry of Labor, Social Affairs, Health and Consumer Protection, Vienna, Austria
| | - Friedrich Schmoll
- Division for Animal Health, Austrian Agency for Health and Food Safety (AGES), Mödling, Austria
| | - Jörg Hiesel
- Department of Veterinary Administration, Styrian Provincial Government, Graz, Austria
| | - Andrea Höflechner-Pöltl
- Department for Animal Health and Animal Disease Control, Federal Ministry of Labor, Social Affairs, Health and Consumer Protection, Vienna, Austria
| | - Annemarie Käsbohrer
- Unit of Veterinary Public Health and Epidemiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Beate Pinior
- Unit of Veterinary Public Health and Epidemiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine Vienna, Vienna, Austria
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Ashley C, Berry SD. The Association Between Race and Stroke Prevalence in a Patient Cohort in Mississippi. PERSPECTIVES IN HEALTH INFORMATION MANAGEMENT 2020; 18:1i. [PMID: 33633519 PMCID: PMC7883364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The risk factors for stroke, including hypertension, high cholesterol, heart disease, diabetes, heavy alcohol use, and prior history of stroke, are well known. In Mississippi, there is often a wider gulf of socioeconomic disparities between racial groups than in other regions within the United States. This increases the effect of these disparities in minority populations. The goal of this research is to determine whether there is an increased risk of stroke prevalence in the black community than in the white population. The odds ratio of 1.5 (CI 1.3818 - 1.5591) was returned for this analysis. White patients diagnosed with stroke represented 38 percent of the cohort while black patients totaled 62 percent of this cohort. There is a higher prevalence of stroke in the black population compared to the white population in this study cohort. The importance of this finding is apparent upon consideration of deficiencies in the management of risk factors. Note: The University of Mississippi Medical Center Patient Cohort explorer database search used for this study uses a data filter set for 'black' or 'African-American' in the search query. This study includes those patients designated 'black' or 'African-American' admitted with stroke at the University of Mississippi Medical Center. For clarity, this cohort will be identified in this paper as 'black Americans.'
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Affiliation(s)
- Christopher Ashley
- is a staff radiologic technologist at St. Dominic Health Services in Jackson, MS
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Alzu'bi AA, Alasal SIA, Watzlaf VJM. A Simulation Study of Coronavirus as an Epidemic Disease Using Agent-Based Modeling. PERSPECTIVES IN HEALTH INFORMATION MANAGEMENT 2020; 18:1g. [PMID: 33633517 PMCID: PMC7883357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
At the end of 2019, the world faced the novel coronavirus, and with it fear of economic collapse and mass fatalities. Simulation systems can be used to monitor the behavior of the virus. Simulation provides an abstract representation of reality by conveying details and characteristics of reality in a simple application. One of the most important ways to simulate is agent-based modeling. The health information professional plays an important role in developing these models. In this research, we simulate the spread of COVID-19 in a region restricted to a population with specific demographic characteristics and social relationships. This study aims to clarify the effects of preventative techniques that suppress the spread of epidemics, such as quarantines, social distancing, and reduced mass transit.
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Affiliation(s)
- Amal Adel Alzu'bi
- is Assistant Professor, Department of Computer Information Systems, Jordan University of Science and Technology, in Irbid, Jordan
| | - Sanaa Ibrahim Abu Alasal
- , is Research Assistant, Department of Computer Science, Jordan University of Science and Technology, in Irbid, Jordan
| | - Valerie J M Watzlaf
- Valerie J.M. Watzlaf, PhD, MPH, RHIA, FAHIMA, is Vice Chair of Education and Associate Professor, Department of Health Information Management, School of Health and Rehabilitation Science, University of Pittsburgh, in Pennsylvania
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