1
|
Liu X. Analytical solution of l-i SEIR model-Comparison of l-i SEIR model with conventional SEIR model in simulation of epidemic curves. PLoS One 2023; 18:e0287196. [PMID: 37315097 PMCID: PMC10266630 DOI: 10.1371/journal.pone.0287196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 05/31/2023] [Indexed: 06/16/2023] Open
Abstract
The Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model has been commonly used to analyze the spread of infectious diseases. This 4-compartment (S, E, I and R) model uses an approximation of temporal homogeneity of individuals in these compartments to calculate the transfer rates of the individuals from compartment E to I to R. Although this SEIR model has been generally adopted, the calculation errors caused by temporal homogeneity approximation have not been quantitatively examined. In this study, a 4-compartment l-i SEIR model considering temporal heterogeneity was developed from a previous epidemic model (Liu X., Results Phys. 2021; 20:103712), and a closed-form solution of the l-i SEIR model was derived. Here, l represents the latent period and i represents the infectious period. Comparing l-i SEIR model with the conventional SEIR model, we are able to examine how individuals move through each corresponding compartment in the two SEIR models to find what information may be missed by the conventional SEIR model and what calculation errors may be introduced by using the temporal homogeneity approximation. Simulations showed that l-i SEIR model could generate propagated curves of infectious cases under the condition of l>i. Similar propagated epidemic curves were reported in literature, but the conventional SEIR model could not generate propagated curves under the same conditions. The theoretical analysis showed that the conventional SEIR model overestimates or underestimates the rate at which individuals move from compartment E to I to R in the rising or falling phase of the number of infectious individuals, respectively. Increasing the rate of change in the number of infectious individuals leads to larger calculation errors in the conventional SEIR model. Simulations from the two SEIR models with assumed parameters or with reported daily COVID-19 cases in the United States and in New York further confirmed the conclusions of the theoretical analysis.
Collapse
Affiliation(s)
- Xiaoping Liu
- Department of Medicine, Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University Health Science Center, Morgantown, West Virginia, United States of America
| |
Collapse
|
2
|
Azhar Iqbal Kashif Butt, Muhammad Rafiq, Waheed Ahmad, Naeed Ahmad. Implementation of computationally efficient numerical approach to analyze a Covid-19 pandemic model. Alexandria Engineering Journal 2023; 69. [ DOI: 10.1016/j.aej.2023.01.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/05/2023] [Accepted: 01/23/2023] [Indexed: 09/17/2023]
Abstract
Corona virus disease (Covid-19) which has caused frustration in the human community remains the concern of the globe as every government struggles to defeat the pandemic. To deal with the situation, we have extensively studied a deadly Covid-19 model to provide a deep insight into the disease dynamics. A mathematical analysis of the model utilizing preventive measures is performed with the aim to reduce the disease burden. Some comprehensive mathematical techniques are employed to demonstrate several essential properties of solutions. To start with, we proved the existence and uniqueness of solutions. Equilibrium points are stated both in the absence and presence of the pandemic. Biologically important quantity known as threshold parameter is computed to handle the future disease dynamics and analyzed for its sensitivity. We proved the stability of the proposed model at equilibrium points by employing necessary conditions on threshold parameter. A reliable and competitive numerical analysis is conducted to observe the effectiveness of implemented strategies and to verify obtained analytical results. The most sensitive parameters are determined through sensitivity analysis. An important feature of this study is to employ Non-Standard Finite Difference (NSFD) numerical scheme to solve the system instead of other standard methods like Runge–Kutta method of order 4 (RK4). Finally, several numerical simulations are performed to validate our former theoretical analysis. Numerical results exhibiting dynamical behavior of Covid-19 system under the influence of involved parameters suggest that both the implemented strategies, especially quarantine of exposed individuals, are effective for the substantial reduction in the diseased population and to achieve the herd immunity.
Collapse
|
3
|
Liu J, Lai S, Rai AA, Hassan A, Mushtaq RT. Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace. Int J Environ Res Public Health 2023; 20:3930. [PMID: 36900941 PMCID: PMC10001733 DOI: 10.3390/ijerph20053930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.
Collapse
Affiliation(s)
- Jun Liu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Shuang Lai
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi’an 710072, China
| | - Ayesha Akram Rai
- School of Medicine, Xi’an Jiaotong University, Xi’an 710049, China
| | - Abual Hassan
- Faculty of Mechanical Engineering and Ship Technology, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Ray Tahir Mushtaq
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| |
Collapse
|
4
|
Xie H, Huang X, Zhang Q, Wei Y, Zeng X, Chang F, Wu S. The Prevalence of and Factors Associated With Anxiety and Depression Among Working-Age Adults in Mainland China at the Early Remission Stage of the Coronavirus 2019 Pandemic. Front Psychol 2022; 13:839852. [PMID: 35432080 PMCID: PMC9009372 DOI: 10.3389/fpsyg.2022.839852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/16/2022] [Indexed: 12/20/2022] Open
Abstract
BackgroundThe Coronavirus 2019 (COVID-19) outbreak has led to a considerable proportion of adverse psychological symptoms in different subpopulations. This study aimed to investigate the status of anxiety and depression and their associated factors in the adult, working-age population in Mainland China at the early remission stage of the COVID-19 pandemic.MethodsAn online study was conducted among 1,863 participants in 29 provinces in Mainland China from March 23 to 31, 2020. Their mental health was evaluated by the generalized anxiety disorder scale (GAD-7) and the patient health questionnaire (PHQ-9). Descriptive analysis, Chi-square, and multiple logistic regressions were applied.ResultsAbout 44.5% of the participants had anxiety, 49.2% had depression, and 37.9% showed a combination of depression and anxiety. Around 83.7% of the participants claimed that the pandemic had a negative impact on their medical needs, which was the primary predictor of mental health, the degree of impact being positively related to the prevalence of anxiety and depression. More chronic diseases, moderate to bad self-rated health, severe perceived infection risk, and younger age group were the common risk factors for anxiety and depression. Having no children, unemployment, and a college-level educational background were associated with higher anxiety prevalence, whereas unmarried participants were correlated with higher depression prevalence.ConclusionThe working-age population showed a relatively high risk of anxiety and depression in Mainland China at the early remission stage of the pandemic. To improve medical services capacity for routine and delayed medical service needs should be a part of policy-makers’ priority agenda during this period of crisis.
Collapse
Affiliation(s)
- Haixia Xie
- Department of Social Work, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Xiaowei Huang
- Department of Gastroenterology and Hepatology, Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Qi Zhang
- School of Community and Environmental Health, Old Dominion University, Norfolk, VA, United States
| | - Yan Wei
- China Research Center on Disability, School of Public Health, Fudan University, Shanghai, China
| | - Xuheng Zeng
- China Research Center on Disability, School of Public Health, Fudan University, Shanghai, China
| | - Fengshui Chang
- China Research Center on Disability, School of Public Health, Fudan University, Shanghai, China
- *Correspondence: Fengshui Chang,
| | - Shuyin Wu
- School of Health Management, Bengbu Medical College, Bengbu, China
- Shuyin Wu,
| |
Collapse
|
5
|
Affiliation(s)
- Wala Abdalla
- Faculty of Science and Engineering University of Wolverhampton Wolverhampton UK
| | - Suresh Renukappa
- Faculty of Science and Engineering University of Wolverhampton Wolverhampton UK
| | - Subashini Suresh
- Faculty of Science and Engineering University of Wolverhampton Wolverhampton UK
| |
Collapse
|
6
|
Abstract
OBJECTIVES To determine whether patients admitted to an ICU during times of unprecedented ICU capacity strain, during the COVID-19 pandemic in the United Kingdom, experienced a higher risk of death. DESIGN Multicenter, observational cohort study using routine clinical audit data. SETTING Adult general ICUs participating the Intensive Care National Audit & Research Centre Case Mix Programme in England, Wales, and Northern Ireland. PATIENTS One-hundred thirty-thousand six-hundred eighty-nine patients admitted to 210 adult general ICUs in 207 hospitals. INTERVENTIONS Multilevel, mixed effects, logistic regression models were used to examine the relationship between levels of ICU capacity strain on the day of admission (typical low, typical, typical high, pandemic high, and pandemic extreme) and risk-adjusted hospital mortality. MEASUREMENTS AND MAIN RESULTS In adjusted analyses, compared with patients admitted during periods of typical ICU capacity strain, we found that COVID-19 patients admitted during periods of pandemic high or pandemic extreme ICU capacity strain during the first wave had no difference in hospital mortality, whereas those admitted during the pandemic high or pandemic extreme ICU capacity strain in the second wave had a 17% (odds ratio [OR], 1.17; 95% CI, 1.05-1.30) and 15% (OR, 1.15; 95% CI, 1.00-1.31) higher odds of hospital mortality, respectively. For non-COVID-19 patients, there was little difference in trend between waves, with those admitted during periods of pandemic high and pandemic extreme ICU capacity strain having 16% (OR, 1.16; 95% CI, 1.08-1.25) and 30% (OR, 1.30; 95% CI, 1.14-1.48) higher overall odds of acute hospital mortality, respectively. CONCLUSIONS For patients admitted to ICU during the pandemic, unprecedented levels of ICU capacity strain were significantly associated with higher acute hospital mortality, after accounting for differences in baseline characteristics. Further study into possible differences in the provision of care and outcome for COVID-19 and non-COVID-19 patients is needed.
Collapse
|
7
|
Saâdaoui F, Rabbouch H, Saadaoui H, Dutheil F. Multiscaled causality of infections on viral testing volumes: The case of COVID-19 in Tunisia. Int J Health Plann Manage 2022; 37:1838-1846. [PMID: 35150453 PMCID: PMC9087424 DOI: 10.1002/hpm.3427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/06/2021] [Accepted: 01/18/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives Coronavirus disease (COVID‐19) is one of the most detrimental pandemics that affected the humanity throughout the ages. The irregular historical progression of the virus over the first year of the pandemic was accompanied with far‐reaching health and social damages. To prepare logistically against this worsening disaster, many public authorities around the world had set up screening and forecasting studies. This article aims to analyse the time‐frequency co‐evolution of the number of confirmed cases (NCC) in Tunisia and the related number of performed polymerase chain reaction (PCR) tests over the COVID‐19 first year. Accurately predicting such a relationship allows Tunisian authorities to set up an effective health prevention plan. Study Design In order to keep pace with the speed of evolution of the virus, we used uninterrupted daily time series from the Tunisian Ministry of Public Health (TMPH) recorded over the COVID‐19 first year. The objective is to: (1) analyse the time‐frequency progress of the NCC in relationship with the number of PCR tests, (2) identify a multi‐scale two‐factor stochastic model in order to develop a robust bivariate forecasting technique. Methods We assume a bivariate stochastic process which is projected onto a set of wavelet sub‐spaces to investigate the scale‐by‐scale co‐evolvement the NCC/PCR over the COVID‐19 first year. A wavelet‐based multiresolutional causality test is then performed. Results The main results recommend the rejection of the null hypothesis of no instantaneous causality in both directions, while the statistics of the Granger test suggest failing to reject the null hypothesis of non‐causality. However, by proceeding scale‐by‐scale, the Granger causality is proven significant in both directions over varying frequency bands. Conclusions It is important to include the NCC and PCR variables in any time series model intended to predict one of these variables. Such a bivariate and multi‐scale model is supposed to better predict the needs of the public health sector in screening tests. On this basis, testing campaigns with multiple periodicities can be planned by the Tunisian authorities. We test the causality of COVID‐19 infections on viral testing volumes in Tunisia A wavelet‐based approach is applied to examine the relationship scale‐by‐scale The results recommend the rejection of the noncausality at all frequency bands Screening campaigns with multiple periodicities can be conducted by Tunisian MOH
Collapse
Affiliation(s)
- Foued Saâdaoui
- Department of Statistics, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hana Rabbouch
- Université de Tunis, Institut Supérieur de Gestion de Tunis, Tunis, Tunisia
| | | | - Frédéric Dutheil
- Université Clermont Auvergne, CNRS, LaPSCo, Physiological and Psychosocial Stress, University Hospital of Clermont-Ferrand, CHU Clermont-Ferrand, Occupational and Environmental Medicine, WittyFit, Clermont-Ferrand, France
| |
Collapse
|
8
|
Zhou L, Rong X, Fan M, Yang L, Chu H, Xue L, Hu G, Liu S, Zeng Z, Chen M, Sun W, Liu J, Liu Y, Wang S, Zhu H. Modeling and Evaluation of the Joint Prevention and Control Mechanism for Curbing COVID-19 in Wuhan. Bull Math Biol 2022; 84:28. [PMID: 34982256 PMCID: PMC8724762 DOI: 10.1007/s11538-021-00983-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/30/2021] [Indexed: 02/08/2023]
Abstract
The spread of COVID-19 in Wuhan was successfully curbed under the strategy of “Joint Prevention and Control Mechanism.” To understand how this measure stopped the epidemics in Wuhan, we establish a compartmental model with time-varying parameters over different stages. In the early stage of the epidemic, due to resource limitations, the number of daily reported cases may lower than the actual number. We employ a dynamic-based approach to calibrate the accumulated clinically diagnosed data with a sudden jump on February 12 and 13. The model simulation shows reasonably good match with the adjusted data which allows the prediction of the cumulative confirmed cases. Numerical results reveal that the “Joint Prevention and Control Mechanism” played a significant role on the containment of COVID-19. The spread of COVID-19 cannot be inhibited if any of the measures was not effectively implemented. Our analysis also illustrates that the Fangcang Shelter Hospitals are very helpful when the beds in the designated hospitals are insufficient. Comprised with Fangcang Shelter Hospitals, the designated hospitals can contain the transmission of COVID-19 more effectively. Our findings suggest that the combined multiple measures are essential to curb an ongoing epidemic if the prevention and control measures can be fully implemented.
Collapse
Affiliation(s)
- Linhua Zhou
- School of Science, Changchun University of Science and Technology, Changchun, China
| | - Xinmiao Rong
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China.,College of Mathematical Sciences, Harbin Engineering University, Harbin, China
| | - Meng Fan
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
| | - Liu Yang
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China
| | - Huidi Chu
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China
| | - Ling Xue
- College of Mathematical Sciences, Harbin Engineering University, Harbin, China
| | - Guorong Hu
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China
| | - Siyu Liu
- Jilin University, Changchun, China
| | - Zhijun Zeng
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China
| | - Ming Chen
- School of Science, Dalian Maritime University, Dalian, China
| | - Wei Sun
- School of Science, Changchun University of Science and Technology, Changchun, China
| | - Jiamin Liu
- School of Mathematics, Harbin Institute of Technology, Harbin, China
| | | | - Shishen Wang
- Changchun Center for Disease Control and Prevention, Changchun, China
| | - Huaiping Zhu
- Center for Disease Modelling, York University, Toronto, Canada.
| |
Collapse
|
9
|
Ahmad W, Abbas M, Rafiq M, Baleanu D. Mathematical analysis for the effect of voluntary vaccination on the propagation of Corona virus pandemic. Results Phys 2021; 31:104917. [PMID: 34722138 PMCID: PMC8536489 DOI: 10.1016/j.rinp.2021.104917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/10/2021] [Accepted: 10/13/2021] [Indexed: 05/04/2023]
Abstract
In this manuscript, a new nonlinear model for the rapidly spreading Corona virus disease (COVID-19) is developed. We incorporate an additional class of vaccinated humans which ascertains the impact of vaccination strategy for susceptible humans. A complete mathematical analysis of this model is conducted to predict the dynamics of Corona virus in the population. The analysis proves the effectiveness of vaccination strategy employed and helps public health services to control or to reduce the burden of corona virus pandemic. We first prove the existence and uniqueness and then boundedness and positivity of solutions. Threshold parameter for the vaccination model is computed analytically. Stability of the proposed model at fixed points is investigated analytically with the help of threshold parameter to examine epidemiological relevance of the pandemic. We apply LaSalle's invariance principle from the theory of Lyapunov function to prove the global stability of both the equilibria. Two well known numerical techniques namely Runge-Kutta method of order 4 (RK4), and the Non-Standard Finite Difference (NSFD) method are employed to solve the system of ODE's and to validate our obtained theoretical results. For different coverage levels of voluntary vaccination, we explored a complete quantitative analysis of the model. To draw our conclusions, the effect of proposed vaccination on threshold parameter is studied numerically. It is claimed that Corona virus disease could be eradicated faster if a human community selfishly adopts mandatory vaccination measures at various coverage levels with proper awareness. Finally, we have executed the joint variability of all classes to understand the effect of vaccination strategy on a disease dynamics.
Collapse
Affiliation(s)
- W Ahmad
- Department of Mathematics, GC University, Lahore, Pakistan
| | - M Abbas
- Department of Mathematics, GC University, Lahore, Pakistan
| | - M Rafiq
- Department of Mathematics, Faculty of Sciences, University of Central Punjab Lahore, Pakistan
| | - D Baleanu
- Department of Mathematics, Cankaya University, Ankara, Turkey
- Institute of Space Sciences, Magurele, Bucharest, Romania
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
| |
Collapse
|
10
|
Pribadi DO, Saifullah K, Putra AS, Nurdin M, Iman LOS, Rustiadi E. Spatial analysis of COVID-19 outbreak to assess the effectiveness of social restriction policy in dealing with the pandemic in Jakarta. Spat Spatiotemporal Epidemiol 2021; 39:100454. [PMID: 34774260 PMCID: PMC8400519 DOI: 10.1016/j.sste.2021.100454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 05/07/2021] [Accepted: 08/25/2021] [Indexed: 11/30/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has been spread globally and brought health and socioeconomic issues. Jakarta tried to accommodate health and economic interests through the Large-Scale Social Restriction (LSSR) policy that should be assessed. This study aims to (1) visualize the spatial patterns of confirmed Covid-19 cases and the locations of potential risk of transmission, and (2) determine the spatial processes underlying the spatial patterns of Covid-19 cases. The emerging hot spot analysis and space-time scan statistic were employed to analyze the dynamic of infected cases and transmission risk. A Geographical Weighted Regression (GWR) model was developed to define factors that influence the spatial transmission. The result shows that spatial transmission keeps continuing, despite a decline in the aggregate pandemic curve during LSSR implementation. This was likely affected by settlements types and population density distribution, and transportation networks. Spatial analysis supports the aggregate pandemic curve to increase the pandemic surveillance effectiveness.
Collapse
Affiliation(s)
- Didit Okta Pribadi
- Research Center for Plant Conservation and Botanic Gardens, Indonesian Institute of Sciences, Bogor 16003, Indonesia; Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| | - Khalid Saifullah
- Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| | - Andi Syah Putra
- Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| | - Muhammad Nurdin
- Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| | - La Ode Syamsul Iman
- Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| | - Ernan Rustiadi
- Center for Regional Systems Analysis, Planning and Development (CRESTPENT/P4W), IPB University, Bogor 16144, Indonesia.
| |
Collapse
|
11
|
Rahman A, Kuddus MA, Ip RHL, Bewong M. A Review of COVID-19 Modelling Strategies in Three Countries to Develop a Research Framework for Regional Areas. Viruses 2021; 13:2185. [PMID: 34834990 PMCID: PMC8623457 DOI: 10.3390/v13112185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 12/17/2022] Open
Abstract
At the end of December 2019, an outbreak of COVID-19 occurred in Wuhan city, China. Modelling plays a crucial role in developing a strategy to prevent a disease outbreak from spreading around the globe. Models have contributed to the perspicacity of epidemiological variations between and within nations and the planning of desired control strategies. In this paper, a literature review was conducted to summarise knowledge about COVID-19 disease modelling in three countries-China, the UK and Australia-to develop a robust research framework for the regional areas that are urban and rural health districts of New South Wales, Australia. In different aspects of modelling, summarising disease and intervention strategies can help policymakers control the outbreak of COVID-19 and may motivate modelling disease-related research at a finer level of regional geospatial scales in the future.
Collapse
Affiliation(s)
- Azizur Rahman
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
- Institute for Land, Water and Society (ILWS), Charles Sturt University, Albury, NSW 2640, Australia
| | - Md Abdul Kuddus
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4814, Australia
- Department of Mathematics, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Ryan H. L. Ip
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
| | - Michael Bewong
- School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; (M.A.K.); (R.H.L.I.); (M.B.)
| |
Collapse
|
12
|
Riswantini D, Nugraheni E, Arisal A, Khotimah PH, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. BDCC 2021; 5:30. [DOI: 10.3390/bdcc5030030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.
Collapse
|
13
|
Shadabfar M, Mahsuli M, Sioofy Khoojine A, Hosseini VR. Time-variant reliability-based prediction of COVID-19 spread using extended SEIVR model and Monte Carlo sampling. Results Phys 2021; 26:104364. [PMID: 34094819 PMCID: PMC8169594 DOI: 10.1016/j.rinp.2021.104364] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 05/12/2023]
Abstract
A probabilistic method is proposed in this study to predict the spreading profile of the coronavirus disease 2019 (COVID-19) in the United State (US) via time-variant reliability analysis. To this end, an extended susceptible-exposed-infected-vaccinated-recovered (SEIVR) epidemic model is first established deterministically, considering the quarantine and vaccination effects, and then applied to the available COVID-19 data from US. Afterwards, the prediction results are described as a time-series of the number of people infected, recovered, and dead. Upon introducing the extended SEIVR model into a limit-state function and defining the model parameters including transmission, recovery, and mortality rates as random variables, the problem is transformed into a reliability model and analyzed by the Monte Carlo sampling. The findings are subsequently given in the form of exceedance probabilities (EPs) of the three main outputs, namely, the maximum number of infected cases, the total number of recovered cases, and the total number of fatal cases. Afterwards, by incorporating time into the formulation of the reliability problem, the EPs are calculated over time and presented as 3D probability graphs, illustrating the relationship between the number of cases affected (i.e., infected, recovered, or dead), exceedance probability, and time. The results for the US demonstrate that, by the end of 2021, the number of the infected (active) cases decreases to 0.8 million and number of cases recovered and fatalities increases to 41.3 million and 0.6 million, respectively.
Collapse
Affiliation(s)
- Mahdi Shadabfar
- Center for Infrastructure Sustainability and Resilience Research, Department of Civil Engineering, Sharif University of Technology, Tehran 145888-9694, Iran
| | - Mojtaba Mahsuli
- Center for Infrastructure Sustainability and Resilience Research, Department of Civil Engineering, Sharif University of Technology, Tehran 145888-9694, Iran
| | - Arash Sioofy Khoojine
- Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China
- School of Mathematics and Statistics, Shanghai Jiao Tong University, Shanghai, China
| | | |
Collapse
|
14
|
Frank TD, Chiangga S. SEIR order parameters and eigenvectors of the three stages of completed COVID-19 epidemics: with an illustration for Thailand January to May 2020. Phys Biol 2021; 18. [PMID: 33789256 DOI: 10.1088/1478-3975/abf426] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/31/2021] [Indexed: 12/23/2022]
Abstract
By end of October 2020, the COVID-19 pandemic has taken a tragic toll of 1150 000 lives and this number is expected to increase. Despite the pandemic is raging in most parts of the world, in a few countries COVID-19 epidemics subsided due to successful implementations of intervention measures. A unifying perspective of the beginnings, middle stages, and endings of such completed COVID-19 epidemics is developed based on the order parameter and eigenvalue concepts of nonlinear physics, in general, and synergetics, in particular. To this end, a standard susceptible-exposed-infected-recovered (SEIR) epidemiological model is used. It is shown that COVID-19 epidemic outbreaks follow a suitably defined SEIR order parameter. Intervention measures switch the eigenvalue of the order parameter from a positive to a negative value, and in doing so, stabilize the COVID-19 disease-free state. The subsiding of COVID-19 epidemics eventually follows the remnant of the order parameter of the infection dynamical system. These considerations are illustrated for the COVID-19 epidemic in Thailand from January to May 2020. The decay of effective contact rates throughout the three epidemic stages is demonstrated. Evidence for the sign-switching of the dominant eigenvalue is given and the order parameter and its stage-3 remnant are identified. The presumed impacts of interventions measures implemented in Thailand are discussed in this context.
Collapse
Affiliation(s)
- T D Frank
- Department of Psychology and Department of Physics, University of Connecticut, Storrs, CT 06269, United States of America
| | - S Chiangga
- Department of Physics, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| |
Collapse
|
15
|
Purnomo CW, Kurniawan W, Aziz M. Technological review on thermochemical conversion of COVID-19-related medical wastes. Resour Conserv Recycl 2021; 167:105429. [PMID: 33519084 PMCID: PMC7832489 DOI: 10.1016/j.resconrec.2021.105429] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/22/2020] [Accepted: 01/11/2021] [Indexed: 05/12/2023]
Abstract
COVID-19 pandemic has brought tremendous environmental burden due to huge amount of medical wastes (about 54,000 t/d as of November 22, 2020), including face mask, gloves, clothes, goggles, and sanitizer/disinfectant containers. A proper waste management is urgently required to mitigate the spread of the disease, minimize the environmental impacts, and take their potential advantages for further utilization. This work provides a prospective review on the possible thermochemical treatments for those COVID-19 related medical wastes (CMW), as well as their possible conversion to fuels. The characteristics of each waste are initially analyzed and described, especially their potential as energy source. It is clear that most of CMWs are dominated by plastic polymers. Thermochemical processes, including incineration, torrefaction, pyrolysis, and gasification, are reviewed in terms of applicability for CMW. In addition, the mechanical treatment of CMW into sanitized refuse-derived fuel (SRDF) is also discussed as the preliminary stage before thermochemical conversion. In terms of material flexibility, incineration is practically applicable for all types of CMW, although it has the highest potential to emit the largest amount of CO2 and other harmful gasses. Furthermore, gasification and pyrolysis are considered promising in terms of energy conversion efficiency and environmental impacts. On the other hand, carbonization faces several technical problems following thermal degradation due to insufficient operating temperature.
Collapse
Affiliation(s)
- Chandra Wahyu Purnomo
- Chemical Engineering Department, Engineering Faculty, Gadjah Mada University, Jl. Grafika no 2, Bulaksumur,Yogyakarta 55281, Indonesia
- Agrotechnology Innovation Center PIAT UGM, Berbah Sleman Yogyakarta, Indonesia
| | - Winarto Kurniawan
- Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, 2-12-1 Oookayama Meguro-ku Tokyo 152-8550, Japan
| | - Muhammad Aziz
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| |
Collapse
|
16
|
Ma T, Huang Y, Li W, Zhong J, Yang H, Zhou Y, Li M, Zhong W, Cao Y, Lu S, Hu Y. The Number of Patients with Acute Myocardial Infarction Decreased and Door-to-Balloon Time Delayed in COVID-19. Cardiol Res Pract 2021; 2021:6673313. [PMID: 33791126 DOI: 10.1155/2021/6673313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/21/2021] [Accepted: 02/20/2021] [Indexed: 12/19/2022] Open
Abstract
Background At present, COVID-19 is sweeping the world, and all countries are actively responding. During the COVID-19 epidemic, the treatment of patients with acute myocardial infarction (AMI) may be affected. Methods We reviewed data of patients with AMI from January 23 to April 23, 2020 (2020), and January 23 to April 23, 2019 (2019), who were admitted to two hospitals from Southern China. We collected clinical characteristics, comorbidities, treatment, prognosis, and key time segments to analyze. Results The total number of patients that had been diagnosed with AMI in the two hospitals was 218 in 2020 and 260 in 2019. The number of AMI patients that were admitted to hospitals per day decreased in 2020. The percentage of patients with AMI who refused hospitalization in 2020 was significantly higher than that in 2019 (5.0% vs 1.5%, p=0.028). There is no statistical difference in symptoms of the first medical contact (S2FMC) time between 2020 and 2019 (p=0.552). Door-to-balloon (D2B) time of ST-elevation myocardial infarction (STEMI) patients who were treated with a primary percutaneous coronary intervention (pPCI) in 2020 was 79 (63.75-105.25) mins, while D2B time in 2019 was 57.5 (41.5-76.5) mins, which was statistically different from the two groups. Conclusions COVID-19 had an impact on the number of AMI patients who were admitted to hospitals and the time of treatment. During the COVID-19 epidemic, the number of AMI patients that were admitted to hospitals per day was decreased, while the percentage of AMI patients that refused therapy in these two hospitals increased, and the D2B time of STEMI patients was also delayed.
Collapse
|
17
|
Abstract
INTRODUCTION Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively. METHODS A systematic review guided by PRISMA guidelines has been performed to obtain the key elements. RESULTS This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data. CONCLUSIONS The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.
Collapse
Affiliation(s)
- Yang Lv
- School of Public Administration, Sichuan University, China
| | - Chenwei Ma
- School of Public Administration, Sichuan University, China
| | - Xiaohan Li
- School of Public Administration, Sichuan University, China
| | - Min Wu
- School of Public Administration, Sichuan University, China
| |
Collapse
|
18
|
Xin X, Li SF, Cheng L, Liu CY, Xin YJ, Huang HL, Beejadhursing R, Wang SS, Feng L. Government Intervention Measures Effectively Control COVID-19 Epidemic in Wuhan, China. Curr Med Sci 2021; 41:77-83. [PMID: 33582909 DOI: 10.1007/s11596-021-2321-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/03/2020] [Indexed: 01/22/2023]
Abstract
The Coronavirus disease 2019 (COVID-19) outbreak has been brought under control through a nationwide effort, and now it has become a global pandemic and the situation seems grim. We summarized the measures taken in Wuhan and analyzed the effects to comprehensively describe the factors involved in controlling the COVID-19 in China. In China, several measures such as the lockdown of Wuhan, restriction of traffic and communities, increasing hospital beds, nationwide support from medical staff, epidemic prevention equipment and supplies, and establishment of makeshift shelter hospitals have been taken. The lockdown of Wuhan reduced the propagation of cases to other cities in Hubei province and throughout China, traffic and community restrictions reduced the flow of population and the spread of disease, increasing wards and beds and medical personnel reduced the incidence of severe cases and mortality, the establishment of the Fangcang shelter hospitals provided a good isolation and monitoring environment, and further reduced the spread and fatality of the disease. The fact that China was able to control the spread of COVID-19 within three months without a specific drug or vaccine suggests that these measures are more adequate and effective.
Collapse
|
19
|
Liu X. A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan. Results Phys 2021; 20:103712. [PMID: 33391987 PMCID: PMC7759094 DOI: 10.1016/j.rinp.2020.103712] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 05/30/2023]
Abstract
In this study, an individual-based epidemic model, considering latent-infectious-recovery periods, is presented. The analytic solution of the model in the form of recursive formulae with a time-dependent transmission coefficient is derived and implanted in Excel. The simulated epidemic curves from the model fit very well with the daily reported cases of COVID-19 in Wuhan, China and New York City (NYC), USA. These simulations show that the transmission rate of NYC's COVID-19 is nearly 30% greater than the transmission rate of Wuhan's COVID-19, and that the actual number of cumulative infected people in NYC is around 9 times the reported number of cumulative COVID-19 cases in NYC. Results from this study also provide important information about latent period, infectious period and lockdown efficiency.
Collapse
Affiliation(s)
- Xiaoping Liu
- Department of Medicine, Department of Neuroscience, West Virginia University Health Science Center, Morgantown, WV 26506, United States
| |
Collapse
|
20
|
Abstract
INTRODUCTION Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza. METHODS The article is the author's original research hypothesis. RESULTS Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells. CONCLUSIONS A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.
Collapse
Affiliation(s)
- Jaroslaw J. Fedorowski
- Polish Hospital Federation, Poland
- Collegium Humanum Warsaw Management University, Warsaw, Poland
- College of Medicine and Health Network, University of Vermont, Vermont, United States
- Warsaw Maria Curie-Sklodowska Medical University, Warsaw, Poland
| |
Collapse
|
21
|
Hasnain M, Pasha MF, Ghani I. Combined measures to control the COVID-19 pandemic in Wuhan, Hubei, China: A narrative review. J Biosaf Biosecur 2020; 2:51-57. [PMID: 33521592 PMCID: PMC7834379 DOI: 10.1016/j.jobb.2020.10.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 09/21/2020] [Accepted: 10/15/2020] [Indexed: 01/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is an emerging disease caused by the coronavirus, SARS-CoV-2, which leads to severe respiratory infections in humans. COVID-19 was first reported in December 2019 in Wuhan city, a populated area of the Hubei province in China. As of now, Wuhan and other cities nearby have become safe places for locals. The rapid control of the spread of COVID-19 infection was made possible due to several interventions and measures that were undertaken in Wuhan. This narrative review study was designed to evaluate the emerging literature on the combined measures taken to control the COVID-19 pandemic in Wuhan city. Science Direct, Springer, Web of Science, and the PubMed data repositories were searched for studies published between December 1, 2019, and June 07, 2020. The referred "preferred reporting items for systematic reviews and meta-analyses" (PRISMA) protocol was used to conduct this narrative review. A total of 330 research studies were found as a result of the initial search based on exclusion and inclusion criteria, and 30 articles were chosen on final evaluation. It was discovered that the combined measures to control the spread of COVID-19 in Wuhan included cordon sanitaire, social distancing, universal symptom surveys, quarantine strategies, and transport restrictions. Based on the recommendations presented in this review study, existing policies with regard to combined measures and public health policies can be enforced by other countries to implement a rapid control procedure to control the spread of the COVID-19 pandemic.
Collapse
Affiliation(s)
- Muhammad Hasnain
- School of Information Technology Malaysia, Monash University, Malaysia
| | | | - Imran Ghani
- Department of Mathematics and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, USA
| |
Collapse
|
22
|
Wang K, Ding L, Yan Y, Dai C, Qu M, Jiayi D, Hao X. Modelling the initial epidemic trends of COVID-19 in Italy, Spain, Germany, and France. PLoS One 2020; 15:e0241743. [PMID: 33166344 PMCID: PMC7652319 DOI: 10.1371/journal.pone.0241743] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/20/2020] [Indexed: 02/07/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) has fast spread to over 200 countries and regions worldwide since its outbreak, while in March, Europe became the emerging epicentre. In this study, we aimed to model the epidemic trends and estimate the essential epidemic features of COVID-19 in Italy, Spain, Germany, and France at the initial stage. The numbers of daily confirmed cases and total confirmed cases were extracted from the Coronavirus disease (COVID-19) situation reports of WHO. We applied an extended Susceptible-Exposed-Infectious-Removed (SEIR) model to fit the epidemic trend and estimated corresponding epidemic features. The transmission rate estimates were 1.67 (95% credible interval (CrI), 1.64-1.71), 2.83 (2.72-2.85), 1.91 (1.84-1.98), and 1.89 (1.82-1.96) for Italy, Spain, Germany, and France, corresponding to the basic reproduction numbers (R0) 3.44 (3.35-3.54), 6.25 (5.97-6.55), 4.03 (3.84-4.23), and 4.00 (3.82-4.19), respectively. We found Spain had the lowest ascertainment rate of 0.22 (0.19-0.25), followed by France, Germany, and Italy of 0.45 (0.40-0.50), 0.46 (0.40-0.52), and 0.59 (0.55-0.64). The peaks of daily new confirmed cases would reach on April 16, April 5, April 21, and April 19 for Italy, Spain, Germany, and France if no action was taken by the authorities. Given the high transmissibility and high covertness of COVID-19, strict countermeasures, such as national lockdown and social distancing, were essential to be implemented to reduce the spread of the disease.
Collapse
Affiliation(s)
- Kai Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lin Ding
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yu Yan
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chengguqiu Dai
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Minghan Qu
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dong Jiayi
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xingjie Hao
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
23
|
Lobato FS, Libotte GB, Platt GM. Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search. Comput Math Methods Med 2020; 2020:9214159. [PMID: 33082843 DOI: 10.1155/2020/9214159] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/03/2020] [Accepted: 09/29/2020] [Indexed: 11/17/2022]
Abstract
Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
Collapse
|
24
|
Zhang W, Govindavari JP, Davis BD, Chen SS, Kim JT, Song J, Lopategui J, Plummer JT, Vail E. Analysis of Genomic Characteristics and Transmission Routes of Patients With Confirmed SARS-CoV-2 in Southern California During the Early Stage of the US COVID-19 Pandemic. JAMA Netw Open 2020; 3:e2024191. [PMID: 33026453 PMCID: PMC7542329 DOI: 10.1001/jamanetworkopen.2020.24191] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 08/23/2020] [Indexed: 12/15/2022] Open
Abstract
Importance In late December 2019, an outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China. Data on the routes of transmission to Los Angeles, California, the US West Coast epicenter for coronavirus disease 2019 (COVID-19), and subsequent community spread are limited. Objective To determine the transmission routes of SARS-CoV-2 to Southern California and elucidate local community spread within the Los Angeles metropolitan area. Design, Setting, and Participants This case series included 192 consecutive patients with reverse transcription-polymerase chain reaction (RT-PCR) test results positive for SARS-CoV-2 who were evaluated at Cedars-Sinai Medical Center in Los Angeles, California, from March 22 to April 15, 2020. Data analysis was performed from April to May 2020. Main Outcomes and Measures SARS-CoV-2 viral genomes were sequenced. Los Angeles isolates were compared with genomes from global subsampling and from New York, New York; Washington state; and China to determine potential sources of viral dissemination. Demographic data and outcomes were collected. Results The cohort included 192 patients (median [interquartile range] age, 59.5 [43-75] years; 110 [57.3%] men). The genetic characterization of SARS-CoV-2 isolates in the Los Angeles population pinpointed community transmission of 13 patients within a 3.81 km2 radius. Variation landscapes of this case series also revealed a cluster of 10 patients that contained 5 residents at a skilled nursing facility, 1 resident of a nearby skilled nursing facility, 3 health care workers, and a family member of a resident of one of the skilled nursing facilities. Person-to-person transmission was detected in a cluster of 5 patients who shared the same single-nucleotide variation in their SARS-CoV-2 genomes. High viral genomic diversity was identified: 20 Los Angeles isolates (15.0%) resembled SARS-CoV-2 genomes from Asia, while 109 Los Angeles isolates (82.0%) were similar to isolates originating from Europe. Analysis of other common respiratory viral pathogens did not reveal coinfection in the cohort. Conclusions and Relevance These findings highlight the precision of detecting person-to-person transmission and accurate contact tracing directly through SARS-CoV-2 genome isolation and sequencing. Development and application of phylogenetic analyses from the Los Angeles population established connections between COVID-19 clusters locally and throughout the US.
Collapse
Affiliation(s)
- Wenjuan Zhang
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - John Paul Govindavari
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Brian D. Davis
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Applied Genomics, Computation and Translational Core, Cedars-Sinai Cancer Center, Los Angeles, California
| | - Stephanie S. Chen
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Applied Genomics, Computation and Translational Core, Cedars-Sinai Cancer Center, Los Angeles, California
| | - Jong Taek Kim
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jianbo Song
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jean Lopategui
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jasmine T. Plummer
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Applied Genomics, Computation and Translational Core, Cedars-Sinai Cancer Center, Los Angeles, California
| | - Eric Vail
- Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| |
Collapse
|
25
|
Iqbal N, Fareed Z, Shahzad F, He X, Shahzad U, Lina M. The nexus between COVID-19, temperature and exchange rate in Wuhan city: New findings from partial and multiple wavelet coherence. Sci Total Environ 2020; 729:138916. [PMID: 32388129 PMCID: PMC7194511 DOI: 10.1016/j.scitotenv.2020.138916] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 04/13/2023]
Abstract
This study attempts to document the nexus between weather, COVID-19 outbreak in Wuhan and the Chinese economy. We used daily average temperature (hourly data), daily new confirmed cases of COVID-19 in Wuhan, and RMB (Chinese currency) exchange rate to represent the weather, COVID-19 outbreak and the Chinese economy, respectively. The methodology of Wavelet Transform Coherence (WTC), Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) is employed to analyze the daily data collected from 21st January 2020 to 31st March 2020. The results have revealed a significant coherence between the series at different time-frequency combinations. The overall results suggest the insignificance of an increase in temperature to contain or slow down the new COVID-19 infections. The RMB exchange rate and the COVID-19 showed an out phase coherence at specific time-frequency spots suggesting a negative but limited impact of the COVID-19 outbreak in Wuhan on the Chinese export economy. Our results are contrary to many earlier studies which suggest a significant role of temperature in slowing down the COVID-19 spread. These results can have important policy implications for the containment of COVID-19 spread and macro-economic management with respect to changes in the weather.
Collapse
Affiliation(s)
- Najaf Iqbal
- College of Economics and Management, Hunan University of Arts and Science, Changde, China; Wuhan University of Technology, Wuhan, Hubei, China
| | - Zeeshan Fareed
- School of Business, Huzhou University, Huzhou, Zhejiang, China
| | - Farrukh Shahzad
- School of Economics and Management, Guangdong University of Petrochemical Technology, Guangdong, China.
| | - Xin He
- College of Economics and Management, Hunan University of Arts and Science, Changde, China
| | - Umer Shahzad
- Institute of Guangdong Economy & Social Development, Guangdong University of Finance and Economics (GDUFE), 510320, Guangzhou, P.R. China; School of Economics, Shandong University, Jinan, Shandong Province, China.
| | - Ma Lina
- Wuhan University of Technology, Wuhan, Hubei, China
| |
Collapse
|