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Lyu M, Liu K, Hall RW. Spatial Interaction Analysis of Infectious Disease Import and Export between Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:643. [PMID: 38791857 PMCID: PMC11120745 DOI: 10.3390/ijerph21050643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/07/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.
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Affiliation(s)
- Mingdong Lyu
- National Renewable Energy Laboratory, Mobility, Behavior, and Advanced Powertrains Department, Denver, CO 80401, USA
| | - Kuofu Liu
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
| | - Randolph W. Hall
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
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Waku J, Oshinubi K, Adam UM, Demongeot J. Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination. Diseases 2023; 11:135. [PMID: 37873779 PMCID: PMC10594474 DOI: 10.3390/diseases11040135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/20/2023] [Accepted: 09/26/2023] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. METHODS Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. RESULTS This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. CONCLUSION This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.
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Affiliation(s)
- Jules Waku
- IRD UMI 209 UMMISCO and LIRIMA, University of Yaounde I, Yaounde P.O. Box 337, Cameroon;
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Silva TC, Anghinoni L, Chagas CPD, Zhao L, Tabak BM. Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6758. [PMID: 37754616 PMCID: PMC10531329 DOI: 10.3390/ijerph20186758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/20/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023]
Abstract
In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil-a possible indicator of a second wave-these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures.
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Affiliation(s)
- Thiago Christiano Silva
- Universidade Católica de Brasília, Brasilia 71966-700, Brazil
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | - Leandro Anghinoni
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | | | - Liang Zhao
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | - Benjamin Miranda Tabak
- FGV/EPPG Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas (School of Public Policy and Government, Getulio Vargas Foundation), Brasilia 70830-020, Brazil
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Rehman AU, Mian SH, Usmani YS, Abidi MH, Mohammed MK. Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach. Healthcare (Basel) 2023; 11:healthcare11020260. [PMID: 36673628 PMCID: PMC9858678 DOI: 10.3390/healthcare11020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/08/2023] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19's exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible-exposed-infected-recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.
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Affiliation(s)
- Ateekh Ur Rehman
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
- Correspondence:
| | - Syed Hammad Mian
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Yusuf Siraj Usmani
- Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
| | - Mustufa Haider Abidi
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
| | - Muneer Khan Mohammed
- Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
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Zhang Y, Zhang J, Koura YH, Feng C, Su Y, Song W, Kong L. Multiple Concurrent Causal Relationships and Multiple Governance Pathways for Non-Pharmaceutical Intervention Policies in Pandemics: A Fuzzy Set Qualitative Comparative Analysis Based on 102 Countries and Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:931. [PMID: 36673700 PMCID: PMC9858854 DOI: 10.3390/ijerph20020931] [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: 12/02/2022] [Revised: 12/28/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
The global outbreak of COVID-19 has been wreaking havoc on all aspects of human societies. In addition to pharmaceutical interventions, non-pharmaceutical intervention policies have been proven to be crucial in slowing down the spread of the virus and reducing the impact of the outbreak on economic development, daily life, and social stability. However, no studies have focused on which non-pharmaceutical intervention policies are more effective; this is the focus of our study. We used data samples from 102 countries and regions around the world and selected seven categories of related policies, including work and school suspensions, assembly restrictions, movement restrictions, home isolation, international population movement restrictions, income subsidies, and testing and screening as the condition variables. A susceptible-exposed-infected-quarantined-recovered (SEIQR) model considering non-pharmaceutical intervention policies and latency with infectiousness was constructed to calculate the epidemic transmission rate as the outcome variable, and a fuzzy set qualitative comparative analysis (fsQCA) method was applied to explore the multiple concurrent causal relationships and multiple governance paths of non-pharmaceutical intervention policies for epidemics from the configuration perspective. We found a total of four non-pharmaceutical intervention policy pathways. Among them, L1 was highly suppressive, L2 was moderately suppressive, and L3 was externally suppressive. The results also showed that individual non-pharmaceutical intervention policy could not effectively suppress the spread of the pandemic. Moreover, three specific non-pharmaceutical intervention policies, including work stoppage and school closure, testing and screening, and economic subsidies, had a universal effect in the policies grouping for effective control of the pandemic transmission.
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Affiliation(s)
- Yaming Zhang
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Jiaqi Zhang
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Yaya Hamadou Koura
- School of Foreign Languages, Yanshan University, Qinhuangdao 066004, China
| | - Changyuan Feng
- Business School, University of Granada, Campus Universitario de Cartuja, 18071 Granada, Spain
| | - Yanyuan Su
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Wenjie Song
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
| | - Linghao Kong
- School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
- Internet Plus and Industrial Development Research Center, Yanshan University, Qinhuangdao 066004, China
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Zhang L, She GH, She YR, Li R, She ZS. Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:476. [PMID: 36612798 PMCID: PMC9819631 DOI: 10.3390/ijerph20010476] [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: 11/12/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has revealed new features in terms of substantial changes in rates of infection, cure, and death as a result of social interventions, which significantly challenges traditional SEIR-type models. In this paper we developed a symmetry-based model for quantifying social interventions for combating COVID-19. We found that three key order parameters, separating degree (S) for susceptible populations, healing degree (H) for mild cases, and rescuing degree (R) for severe cases, all display logistic dynamics, establishing a novel dynamic model named SHR. Furthermore, we discovered two evolutionary patterns of healing degree with a universal power law in 23 areas in the first wave. Remarkably, the model yielded a quantitative evaluation of the dynamic back-to-zero policy in the third wave in Beijing using 12 datasets of different sizes. In conclusion, the SHR model constitutes a rational basis by which we can understand this complex epidemic and policymakers can carry out sustainable anti-epidemic measures to minimize its impact.
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Affiliation(s)
- Lei Zhang
- Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Guang-Hui She
- Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Yu-Rong She
- Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Rong Li
- Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
- State Key Laboratory for Turbulence & Complex Systems, Peking University, Beijing 100871, China
| | - Zhen-Su She
- Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
- State Key Laboratory for Turbulence & Complex Systems, Peking University, Beijing 100871, China
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