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Pinto JPG, Magalhães PC, Figueiredo GM, Alves D, Angel DMS. Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020. CAD SAUDE PUBLICA 2023; 39:e00109522. [PMID: 38126417 PMCID: PMC10727033 DOI: 10.1590/0102-311xen109522] [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: 11/08/2022] [Revised: 08/07/2023] [Accepted: 09/13/2023] [Indexed: 12/23/2023] Open
Abstract
After four months of fighting the pandemic, the city of São Paulo, Brazil, entered a phase of relaxed social distancing measures in July 2020. Simultaneously, there was a decline in the social distancing rate and a reduction in the number of cases, fatalities, and hospital bed occupancy. To understand the pandemic dynamics in the city of São Paulo, we developed a multi-agent simulation model. Surprisingly, the counter-intuitive results of the model followed the city's reality. We argue that this phenomenon could be attributed to local bubbles of protection that emerged in the absence of contagion networks. These bubbles reduced the transmission rate of the virus, causing short and temporary reductions in the epidemic curve - but manifested as an unstable equilibrium. Our hypothesis aligns with the virus spread dynamics observed thus far, without the need for ad hoc assumptions regarding the natural thresholds of collective immunity or the heterogeneity of the population's transmission rate, which may lead to erroneous predictions. Our model was designed to be user-friendly and does not require any scientific or programming expertise to generate outcomes on virus transmission in a given location. Furthermore, as an input to start our simulation model, we developed the COVID-19 Protection Index as an alternative to the Human Development Index, which measures a given territory vulnerability to the coronavirus and includes characteristics of the health system and socioeconomic development, as well as the infrastructure of the city of São Paulo.
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Affiliation(s)
| | | | | | - Domingos Alves
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brasil
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2
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Li J, Jia K, Zhao W, Yuan B, Liu Y. Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1789-1802. [PMID: 37561207 DOI: 10.1007/s00484-023-02539-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Wenwu Zhao
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Bo Yuan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxu Liu
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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Shi H, Wang J, Cheng J, Qi X, Ji H, Struchiner CJ, Villela DAM, Karamov EV, Turgiev AS. Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak. INTELLIGENT MEDICINE 2023; 3:85-96. [PMID: 36694623 PMCID: PMC9851724 DOI: 10.1016/j.imed.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/06/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.
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Affiliation(s)
- Honghao Shi
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Jingyuan Wang
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Jiawei Cheng
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Xiaopeng Qi
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102211, China
| | - Hanran Ji
- Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102211, China
| | - Claudio J Struchiner
- Fundação Getúlio Vargas, Rio de Janeiro, Brazil
- Instituto de Medicina Social Hesio Cordeiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Daniel AM Villela
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Eduard V Karamov
- Gamaleya National Research Center for Epidemiology and Microbiology of the Russian Ministry of Health, Russia
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases of the Russian Ministry of Health, Russia
| | - Ali S Turgiev
- Gamaleya National Research Center for Epidemiology and Microbiology of the Russian Ministry of Health, Russia
- National Medical Research Center of Phthisiopulmonology and Infectious Diseases of the Russian Ministry of Health, Russia
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4
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Davarci OO, Yang EY, Viguerie A, Yankeelov TE, Lorenzo G. Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States. ENGINEERING WITH COMPUTERS 2023:1-25. [PMID: 37362241 PMCID: PMC10129322 DOI: 10.1007/s00366-023-01816-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/24/2023] [Indexed: 06/28/2023]
Abstract
The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies. Supplementary Information The online version contains supplementary material available at 10.1007/s00366-023-01816-9.
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Affiliation(s)
- Orhun O. Davarci
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712-1229 USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX USA
| | - Emily Y. Yang
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712-1229 USA
| | | | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712-1229 USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX USA
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX USA
- Department of Oncology, The University of Texas at Austin, Austin, TX USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712-1229 USA
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
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Yücel SG, Pereira RHM, Peixoto PS, Camargo CQ. Impact of network centrality and income on slowing infection spread after outbreaks. APPLIED NETWORK SCIENCE 2023; 8:16. [PMID: 36855413 PMCID: PMC9951146 DOI: 10.1007/s41109-023-00540-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both socio-economic conditions and the complex network properties of human mobility patterns interact, and how they influence outbreaks together. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate-a feature associated with socio-economic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after a lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.
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Affiliation(s)
- Shiv G. Yücel
- School of Geography and the Environment, University of Oxford, Oxford, UK
| | | | - Pedro S. Peixoto
- Applied Mathematics Department, University of São Paulo, São Paulo, Brazil
| | - Chico Q. Camargo
- Department of Computer Science, University of Exeter, Exeter, UK
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Li T, Guo Y. Optimal control and cost-effectiveness analysis of a new COVID-19 model for Omicron strain. PHYSICA A 2022; 606:128134. [PMID: 36039105 PMCID: PMC9404231 DOI: 10.1016/j.physa.2022.128134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Omicron, a mutant strain of COVID-19, has been sweeping the world since November 2021. A major characteristic of Omicron transmission is that it is less harmful to healthy adults, but more dangerous for people with underlying disease, the elderly, or children. To simulate the spread of Omicron in the population, we developed a new 9-dimensional mathematical model with high-risk and low-risk exposures. Then we analyzed its dynamic properties and obtain the basic reproduction numberR 0 . With the data of confirmed cases from March 1, 2022 published on the official website of Shanghai, China, we used the weighted nonlinear least square estimation method to estimate the parameters, and get the basic reproduction numberR 0 ≈ 1 . 5118 . Finally, we considered three control measures (isolation, detection and treatment), and studied the optimal control strategy and cost-effectiveness analysis of the model. The control strategy G is determined to be the optimal control strategy from the purpose of making fewer people infected. In strategy G, the three human control measures contain six control variables, and the control strength of these variables needs to be varied according to the pattern shown in Figure 11, so that the number of infections can be minimized and the percentage of reduction of infections can reach more than 95%.
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Affiliation(s)
- Tingting Li
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
| | - Youming Guo
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
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7
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Silva DM, Secchi AR. Recursive state and parameter estimation of COVID-19 circulating variants dynamics. Sci Rep 2022; 12:15879. [PMID: 36151226 PMCID: PMC9508243 DOI: 10.1038/s41598-022-18208-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/08/2022] [Indexed: 11/29/2022] Open
Abstract
COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments weigh social distancing policies to avoid overload in the health system without significant economic impact. The mutability of the SARS-CoV-2 virus, vaccination coverage, and mobility restriction measures change epidemic dynamics over time. A model-based control strategy requires reliable predictions to be efficient on a long-term basis. In this paper, a SEIR-based model is proposed considering dynamic feedback estimation. State and parameter estimations are performed on state estimators using augmented states. Three methods were implemented: constrained extended Kalman filter (CEKF), CEKF and smoother (CEKF & S), and moving horizon estimator (MHE). The parameters estimation was based on vaccine efficacy studies regarding transmissibility, severity of the disease, and lethality. Social distancing was assumed as a measured disturbance calculated using Google mobility data. Data from six federative units from Brazil were used to evaluate the proposed strategy. State and parameter estimations were performed from 1 October 2020 to 1 July 2021, during which Zeta and Gamma variants emerged. Simulation results showed that lethality increased between 11 and 30% for Zeta mutations and between 44 and 107% for Gamma mutations. In addition, transmissibility increased between 10 and 37% for the Zeta variant and between 43 and 119% for the Gamma variant. Furthermore, parameter estimation indicated temporal underreporting changes in hospitalized and deceased individuals. Overall, the estimation strategy showed to be suitable for dynamic feedback as simulation results presented an efficient detection and dynamic characterization of circulating variants.
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Affiliation(s)
- Daniel Martins Silva
- Chemical Engineering Program/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-942, Brazil.
| | - Argimiro Resende Secchi
- Chemical Engineering Program/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-942, Brazil
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Guo Y, Li T. Modeling the transmission of second-wave COVID-19 caused by imported cases: A case study. MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2022; 45:8096-8114. [PMID: 35464831 PMCID: PMC9015312 DOI: 10.1002/mma.8041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
As the first-wave COVID-19 has passed in 2020, people's awareness of self-protection began to decline gradually. How to prevent and control the second-wave COVID-19 has become an important issue in many countries and regions. By analyzing the transmission of the second-wave COVID-19 caused by an imported case in Tonghua City, Jilin Province, China, in January 2021, we establish a new mathematical COVID-19 model to simulate the transmission characteristics of the second-wave COVID-19. First, we analyze the basic properties of the model, prove the existence of the equilibrium point, and obtain the expression of the basic reproduction number with important biological significance. Secondly, we use the weighted nonlinear least square estimation method to fit the cases in Tonghua City of Jilin Province in January 2021, and get the estimated value of the parameters. The basic reproduction number of the second-wave COVID-19 in Tonghua City isR 0 = 1 . 0695 , which is much smaller than that of the first-wave COVID-19 in Wuhan in 2020. Finally, in the optimal control part, we consider two control methods (keeping social distance and nucleic acid detection of all people in the city) to simulate the control of the disease. The results show that the control intensity of the two control methods needs to be dynamically changed and adjusted, so that the cost can be minimized with the least infection. The results of this paper can not only provide suggestions for health management departments, but also provide a reference for the analysis of the second-wave COVID-19 in other countries or regions.
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Affiliation(s)
- Youming Guo
- College of ScienceGuilin University of TechnologyGuilinChina
- Guangxi Colleges and Universities Key Laboratory of Applied StatisticsGuilin University of TechnologyGuilinChina
| | - Tingting Li
- College of ScienceGuilin University of TechnologyGuilinChina
- Guangxi Colleges and Universities Key Laboratory of Applied StatisticsGuilin University of TechnologyGuilinChina
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9
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Kim JE, Choi H, Choi Y, Lee CH. The economic impact of COVID-19 interventions: A mathematical modeling approach. Front Public Health 2022; 10:993745. [PMID: 36172208 PMCID: PMC9512395 DOI: 10.3389/fpubh.2022.993745] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/23/2022] [Indexed: 01/26/2023] Open
Abstract
Prior to vaccination or drug treatment, non-pharmaceutical interventions were almost the only way to control the coronavirus disease 2019 (COVID-19) epidemic. After vaccines were developed, effective vaccination strategies became important. The prolonged COVID-19 pandemic has caused enormous economic losses worldwide. As such, it is necessary to estimate the economic effects of control policies, including non-pharmaceutical interventions and vaccination strategies. We estimated the costs associated with COVID-19 according to different vaccination rollout speeds and social distancing levels and investigated effective control strategies for cost minimization. Age-structured mathematical models were developed and used to study disease transmission epidemiology. Using these models, we estimated the actual costs due to COVID-19, considering costs associated with medical care, lost wages, death, vaccination, and gross domestic product (GDP) losses due to social distancing. The lower the social distancing (SD) level, the more important the vaccination rollout speed. SD level 1 was cost-effective under fast rollout speeds, but SD level 2 was more effective for slow rollout speeds. If the vaccine rollout rate is fast enough, even implementing SD level 1 will be cost effective and can control the number of critically ill patients and deaths. If social distancing is maintained at level 2 at the beginning and then relaxed when sufficient vaccinations have been administered, economic costs can be reduced while maintaining the number of patients with severe symptoms below the intensive care unit (ICU) capacity. Korea has wellequipped medical facilities and infrastructure for rapid vaccination, and the public's desire for vaccination is high. In this case, the speed of vaccine supply is an important factor in controlling the COVID-19 epidemic. If the speed of vaccination is fast, it is possible to maintain a low level of social distancing without a significant increase in the number of deaths and hospitalized patients with severe symptoms, and the corresponding costs can be reduced.
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Affiliation(s)
- Jung Eun Kim
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Heejin Choi
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yongin Choi
- Busan Center for Medical Mathematics, National Institute of Mathematical Sciences, Daejeon, South Korea
| | - Chang Hyeong Lee
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, South Korea,*Correspondence: Chang Hyeong Lee
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Hu C, Pan W, Pan W, Dai WQ, Huang G. The association of COVID-19 nexus on China's economy: A financial crisis or a health crisis? PLoS One 2022; 17:e0272024. [PMID: 36070293 PMCID: PMC9451089 DOI: 10.1371/journal.pone.0272024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/11/2022] [Indexed: 11/19/2022] Open
Abstract
This paper analyses the interaction between the novel coronavirus pandemic (COVID-19), unemployment rate, stock market, consumer confidence index (CCI), and economic policy uncertainty (EPU) index in China within a time-frequency framework. We compare the changes in economic indicators during the global financial crisis (GFC) and study the different impacts of the two events on China's economy. An unprecedented impact of COVID-19 shocks on the unemployment rate, CCI, EPU index, and stock market volatility over the low frequency bands is uncovered by applying the coherence wavelet method to China monthly data. The COVID-19 effect on the stock market volatility and the EPU index is substantially higher than on the unemployment rate and the CCI. On the contrary, the GFC's impact on the unemployment rate is much greater than that on the EPU index and CCI. Additionally, the impact of the GFC on the economy is more cyclical in the long-term, while the COVID-19 pandemic is a short-term shock with a relatively short oscillation cycle. This study concludes that the economic impact of COVID-19 will not spread into a financial crisis for China and believe that the COVID-19 pandemic is more of a health event than an economic crisis for Chinese economy.
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Affiliation(s)
- Cheng Hu
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Wei Pan
- School of Applied Economics, Renmin University of China, Beijing, China
| | - Wulin Pan
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Wan-qiang Dai
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Ge Huang
- School of Economic and Management, Wuhan University, Wuhan, China
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11
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Giovanetti M, Slavov SN, Fonseca V, Wilkinson E, Tegally H, Patané JSL, Viala VL, San EJ, Rodrigues ES, Santos EV, Aburjaile F, Xavier J, Fritsch H, Adelino TER, Pereira F, Leal A, Iani FCDM, de Carvalho Pereira G, Vazquez C, Sanabria GME, Oliveira ECD, Demarchi L, Croda J, Dos Santos Bezerra R, Paola Oliveira de Lima L, Martins AJ, Renata Dos Santos Barros C, Marqueze EC, de Souza Todao Bernardino J, Moretti DB, Brassaloti RA, de Lello Rocha Campos Cassano R, Mariani PDSC, Kitajima JP, Santos B, Proto-Siqueira R, Cantarelli VV, Tosta S, Nardy VB, Reboredo de Oliveira da Silva L, Gómez MKA, Lima JG, Ribeiro AA, Guimarães NR, Watanabe LT, Barbosa Da Silva L, da Silva Ferreira R, da Penha MPF, Ortega MJ, de la Fuente AG, Villalba S, Torales J, Gamarra ML, Aquino C, Figueredo GPM, Fava WS, Motta-Castro ARC, Venturini J, do Vale Leone de Oliveira SM, Gonçalves CCM, do Carmo Debur Rossa M, Becker GN, Giacomini MP, Marques NQ, Riediger IN, Raboni S, Mattoso G, Cataneo AD, Zanluca C, Duarte Dos Santos CN, Assato PA, Allan da Silva da Costa F, Poleti MD, Lesbon JCC, Mattos EC, Banho CA, Sacchetto L, Moraes MM, Grotto RMT, Souza-Neto JA, Nogueira ML, Fukumasu H, Coutinho LL, Calado RT, Neto RM, Bispo de Filippis AM, Venancio da Cunha R, Freitas C, Peterka CRL, de Fátima Rangel Fernandes C, Navegantes W, do Carmo Said RF, Campelo de A E Melo CF, Almiron M, Lourenço J, de Oliveira T, Holmes EC, Haddad R, Sampaio SC, Elias MC, Kashima S, Junior de Alcantara LC, Covas DT. Genomic epidemiology of the SARS-CoV-2 epidemic in Brazil. Nat Microbiol 2022; 7:1490-1500. [PMID: 35982313 PMCID: PMC9417986 DOI: 10.1038/s41564-022-01191-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 06/28/2022] [Indexed: 01/01/2023]
Abstract
The high numbers of COVID-19 cases and deaths in Brazil have made Latin America an epicentre of the pandemic. SARS-CoV-2 established sustained transmission in Brazil early in the pandemic, but important gaps remain in our understanding of virus transmission dynamics at a national scale. We use 17,135 near-complete genomes sampled from 27 Brazilian states and bordering country Paraguay. From March to November 2020, we detected co-circulation of multiple viral lineages that were linked to multiple importations (predominantly from Europe). After November 2020, we detected large, local transmission clusters within the country. In the absence of effective restriction measures, the epidemic progressed, and in January 2021 there was emergence and onward spread, both within and abroad, of variants of concern and variants under monitoring, including Gamma (P.1) and Zeta (P.2). We also characterized a genomic overview of the epidemic in Paraguay and detected evidence of importation of SARS-CoV-2 ancestor lineages and variants of concern from Brazil. Our findings show that genomic surveillance in Brazil enabled assessment of the real-time spread of emerging SARS-CoV-2 variants.
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Affiliation(s)
- Marta Giovanetti
- Laboratório de Flavivirus, Fundacao Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, Italy
| | - Svetoslav Nanev Slavov
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Butantan Institute, São Paulo, Brazil
| | - Vagner Fonseca
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Pan American Health Organization (PAHO)/World Health Organization (WHO), Brasilia, Distrito Federal, Brazil
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Eduan Wilkinson
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Houriiyah Tegally
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | | | - Emmanuel James San
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Evandra Strazza Rodrigues
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Elaine Vieira Santos
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Flavia Aburjaile
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Joilson Xavier
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Hegger Fritsch
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Talita Emile Ribeiro Adelino
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Felicidade Pereira
- Laboratório Central de Saúde Pública do Estado da Bahia (LACEN-BA), Salvador, Bahia, Brazil
| | - Arabela Leal
- Laboratório Central de Saúde Pública do Estado da Bahia (LACEN-BA), Salvador, Bahia, Brazil
| | - Felipe Campos de Melo Iani
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Glauco de Carvalho Pereira
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | | | - Gladys Mercedes Estigarribia Sanabria
- Laboratório Central de Salud Pública, Asunción, Paraguay
- Instituto Regional de Investigación em Salud, Universidad Nacional del Caaguazú, Caaguazú, Paraguay
- Laboratório de Biología Molecular, Hospital Regional de Coronel Oviedo, Ministerio de Salud Pública y Bienestar Social, Asunción, Paraguay
| | | | - Luiz Demarchi
- Laboratório Central de Saúde Pública do Estado de Mato Grosso do Sul (LACEN-MS), Campo Grande, Mato Grosso do Sul, Brazil
| | - Julio Croda
- Universidade Federal do Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
| | - Rafael Dos Santos Bezerra
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | | | | | | | | | | | | | | | | | | | | | | | - Vlademir Vicente Cantarelli
- Universidade Federal de Ciencias da Saúde de Porto Alegre (UFCSPA), Universidade Feevale, Grupo Exame Laboratórios, Rio Grande do Sul, Brazil
| | - Stephane Tosta
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório Central de Saúde Pública do Estado da Bahia (LACEN-BA), Salvador, Bahia, Brazil
| | - Vanessa Brandão Nardy
- Laboratório Central de Saúde Pública do Estado da Bahia (LACEN-BA), Salvador, Bahia, Brazil
| | | | | | - Jaqueline Gomes Lima
- Laboratório Central de Saúde Pública do Estado da Bahia (LACEN-BA), Salvador, Bahia, Brazil
| | - Adriana Aparecida Ribeiro
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Natália Rocha Guimarães
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Luiz Takao Watanabe
- Laboratório Central de Saúde Pública do Estado de Mato Grosso (LACEN-MT), Cuiabá, Mato Grosso, Brazil
| | - Luana Barbosa Da Silva
- Laboratório Central de Saúde Pública do Estado de Mato Grosso (LACEN-MT), Cuiabá, Mato Grosso, Brazil
| | - Raquel da Silva Ferreira
- Laboratório Central de Saúde Pública do Estado de Mato Grosso (LACEN-MT), Cuiabá, Mato Grosso, Brazil
| | | | | | | | | | - Juan Torales
- Laboratório Central de Salud Pública, Asunción, Paraguay
| | | | | | - Gloria Patricia Martínez Figueredo
- Laboratório Central de Salud Pública, Asunción, Paraguay
- Instituto Regional de Investigación em Salud, Universidad Nacional del Caaguazú, Caaguazú, Paraguay
- Laboratório de Biología Molecular, Hospital Regional de Coronel Oviedo, Ministerio de Salud Pública y Bienestar Social, Asunción, Paraguay
| | | | | | - James Venturini
- Universidade Federal do Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
| | | | | | | | - Guilherme Nardi Becker
- Laboratório Central de Saúde Pública do Estado do Paraná (Lacen-PR), Curitiba, Paraná, Brazil
| | | | - Nelson Quallio Marques
- Laboratório Central de Saúde Pública do Estado do Paraná (Lacen-PR), Curitiba, Paraná, Brazil
| | | | - Sonia Raboni
- Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Gabriela Mattoso
- Laboratório de Virologia Molecular, Instituto Carlos Chagas/Fiocruz-PR, Curitiba, Paraná, Brazil
| | - Allan D Cataneo
- Laboratório de Virologia Molecular, Instituto Carlos Chagas/Fiocruz-PR, Curitiba, Paraná, Brazil
| | - Camila Zanluca
- Laboratório de Virologia Molecular, Instituto Carlos Chagas/Fiocruz-PR, Curitiba, Paraná, Brazil
| | | | - Patricia Akemi Assato
- Department of Bioprocesses and Biotechnology, School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
| | - Felipe Allan da Silva da Costa
- Department of Bioprocesses and Biotechnology, School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
| | - Mirele Daiana Poleti
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Jessika Cristina Chagas Lesbon
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Elisangela Chicaroni Mattos
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Cecilia Artico Banho
- Medicine School of São José do Rio Preto (FAMERP), São José do Rio Preto, São Paulo, Brazil
| | - Lívia Sacchetto
- Medicine School of São José do Rio Preto (FAMERP), São José do Rio Preto, São Paulo, Brazil
| | - Marília Mazzi Moraes
- Medicine School of São José do Rio Preto (FAMERP), São José do Rio Preto, São Paulo, Brazil
| | - Rejane Maria Tommasini Grotto
- Department of Bioprocesses and Biotechnology, School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
- Molecular Biology Laboratory, Applied Biotechnology Laboratory, Clinical Hospital of the Botucatu Medical School, São Paulo, Brazil
| | - Jayme A Souza-Neto
- Department of Bioprocesses and Biotechnology, School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
| | | | - Heidge Fukumasu
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Luiz Lehmann Coutinho
- Centro de Genômica Funcional da ESALQ, University of São Paulo, Piracicaba, São Paulo, Brazil
| | - Rodrigo Tocantins Calado
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | | | | | - Carla Freitas
- Coordenação Geral dos Laboratórios de Saúde Pública/Secretaria de Vigilância em Saúde, Ministério da Saúde, (CGLAB/SVS-MS), Brasília, Distrito Federal, Brazil
| | - Cassio Roberto Leonel Peterka
- Coordenação Geral das Arboviroses, Secretaria de Vigilância em Saúde/Ministério da Saúde (CGARB/SVS-MS), Brasília, Distrito Federal, Brazil
| | - Cássia de Fátima Rangel Fernandes
- Departamento de Imunização e Doenças Transmissíveisa/Secretaria de Vigilancia em Saude, Ministerio da Saude, Brasılia, Distrito Federal, Brazil
| | - Wildo Navegantes
- Pan American Health Organization (PAHO)/World Health Organization (WHO), Brasilia, Distrito Federal, Brazil
| | | | | | - Maria Almiron
- Pan American Health Organization (PAHO)/World Health Organization (WHO), Brasilia, Distrito Federal, Brazil
| | - José Lourenço
- Department of Zoology, University of Oxford, Oxford, UK
- Biosystems and Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Laboratório Central de Saúde Pública do Estado de Minas Gerais (LACEN-MG), Fundação Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences and School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia
| | | | | | | | - Simone Kashima
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
| | - Luiz Carlos Junior de Alcantara
- Laboratório de Flavivirus, Fundacao Oswaldo Cruz, Rio de Janeiro, Brazil.
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
| | - Dimas Tadeu Covas
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
- Butantan Institute, São Paulo, Brazil.
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12
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Sebbagh A, Kechida S. EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics. Sci Rep 2022; 12:13415. [PMID: 35927443 PMCID: PMC9352705 DOI: 10.1038/s41598-022-16496-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 07/11/2022] [Indexed: 11/09/2022] Open
Abstract
In this paper, we study the Covid 19 disease profile in the Algerian territory since February 25, 2020 to February 13, 2021. The idea is to develop a decision support system allowing public health decision and policy-makers to have future statistics (the daily prediction of parameters) of the pandemic; and also encourage citizens for conducting health protocols. Many studies applied traditional epidemic models or machine learning models to forecast the evolution of coronavirus epidemic, but the use of such models alone to make the prediction will be less precise. For this purpose, we assume that the spread of the coronavirus is a moving target described by an epidemic model. On the basis of a SIRD model (Susceptible-Infection-Recovery- Death), we applied the EKF algorithm to predict daily all parameters. These predicted parameters will be much beneficial to hospital managers for updating the available means of hospitalization (beds, oxygen concentrator, etc.) in order to reduce the mortality rate and the infected. Simulations carried out reveal that the EKF seems to be more efficient according to the obtained results.
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Affiliation(s)
- Abdennour Sebbagh
- Laboratoire d'Automatique et Informatique de Guelma (LAIG), Université 8 mai 1945 Guelma, Bp: 401, 24000, Guelma, Algeria.
| | - Sihem Kechida
- Laboratoire d'Automatique et Informatique de Guelma (LAIG), Université 8 mai 1945 Guelma, Bp: 401, 24000, Guelma, Algeria
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13
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Mycobacterium tuberculosis Diversity Exploration: A Way to Serve the Three Main Weapons against Epidemics, Hygiene, Vaccine Development and Chemotherapy. Microorganisms 2022; 10:microorganisms10081492. [PMID: 35893550 PMCID: PMC9331089 DOI: 10.3390/microorganisms10081492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 11/26/2022] Open
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14
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Ma B, Qi J, Wu Y, Wang P, Li D, Liu S. Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm. DIGITAL SIGNAL PROCESSING 2022; 127:103577. [PMID: 35529477 PMCID: PMC9067002 DOI: 10.1016/j.dsp.2022.103577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptible-exposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures.
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Affiliation(s)
- Baoshan Ma
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Jishuang Qi
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Yiming Wu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Pengcheng Wang
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Di Li
- Department of Neuro Intervention, Dalian Medical University affiliated Dalian Municipal Central Hospital, Dalian, 116033, China
| | - Shuxin Liu
- Department of Nephrology, Dalian Medical University affiliated Dalian Municipal Central Hospital, Dalian, 116033, China
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15
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Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements. Viruses 2022; 14:v14071414. [PMID: 35891394 PMCID: PMC9317659 DOI: 10.3390/v14071414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/20/2022] [Accepted: 06/20/2022] [Indexed: 11/19/2022] Open
Abstract
The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.
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16
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Kong L, Duan M, Shi J, Hong J, Chang Z, Zhang Z. Compartmental structures used in modeling COVID-19: a scoping review. Infect Dis Poverty 2022; 11:72. [PMID: 35729655 PMCID: PMC9209832 DOI: 10.1186/s40249-022-01001-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 06/10/2022] [Indexed: 12/23/2022] Open
Abstract
Background The coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic’s development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. Main text A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted. Conclusion There has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases. Graphical Abstract
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-01001-y.
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Affiliation(s)
- Lingcai Kong
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China
| | - Mengwei Duan
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China
| | - Jin Shi
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jie Hong
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Zhaorui Chang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhijie Zhang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
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17
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Giovanetti M, Slavov SN, Fonseca V, Wilkinson E, Tegally H, Patané JSL, Viala VL, San JE, Rodrigues ES, Santos EV, Aburjaile F, Xavier J, Fritsch H, Adelino TER, Pereira F, Leal A, de Melo Iani FC, de Carvalho Pereira G, Vazquez C, Mercedes Estigarribia Sanabria G, de Oliveira EC, Demarchi L, Croda J, dos Santos Bezerra R, de Lima LPO, Martins AJ, dos Santos Barros CR, Marqueze EC, de Souza Todao Bernardino J, Moretti DB, Brassaloti RA, de Lello Rocha Campos Cassano R, Mariani PDSC, Kitajima JP, Santos B, Proto-Siqueira R, Cantarelli VV, Tosta S, Nardy VB, de Oliveira da Silva LR, Kelly Astete Gómez M, Lima JG, Ribeiro AA, Guimarães NR, Watanabe LT, Da Silva LB, da Silva Ferreira R, da Penha MPF, Ortega MJ, de la Fuente AG, Villalba S, Torales J, Gamarra ML, Aquino C, Martínez Figueredo GP, Fava WS, Motta-Castro ARC, Venturini J, de Oliveira SMDVL, Gonçalves CCM, do Carmo Debur Rossa M, Becker GN, Presibella MM, Marques NQ, Riediger IN, Raboni S, Coelho GM, Cataneo AHD, Zanluca C, dos Santos CND, Assato PA, da Costa FADS, Poleti MD, Lesbon JCC, Mattos EC, Banho CA, Sacchetto L, Moraes MM, Grotto RMT, Souza-Neto JA, Nogueira ML, Fukumasu H, Coutinho LL, Calado RT, Neto RM, de Filippis AMB, da Cunha RV, Freitas C, Peterka CRL, de Fátima Rangel Fernandes C, de Araújo WN, do Carmo Said RF, Almiron M, de Albuquerque e Melo CFC, Lourenço J, de Oliveira T, Holmes EC, Haddad R, Sampaio SC, Elias MC, Kashima S, de Alcantara LCJ, Covas DT. Genomic epidemiology reveals the impact of national and international restrictions measures on the SARS-CoV-2 epidemic in Brazil. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2021.10.07.21264644. [PMID: 35378755 PMCID: PMC8978948 DOI: 10.1101/2021.10.07.21264644] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Brazil has experienced some of the highest numbers of COVID-19 cases and deaths globally and from May 2021 made Latin America a pandemic epicenter. Although SARS-CoV-2 established sustained transmission in Brazil early in the pandemic, important gaps remain in our understanding of virus transmission dynamics at the national scale. Here, we describe the genomic epidemiology of SARS-CoV-2 using near-full genomes sampled from 27 Brazilian states and a bordering country - Paraguay. We show that the early stage of the pandemic in Brazil was characterised by the co-circulation of multiple viral lineages, linked to multiple importations predominantly from Europe, and subsequently characterized by large local transmission clusters. As the epidemic progressed under an absence of effective restriction measures, there was a local emergence and onward international spread of Variants of Concern (VOC) and Variants Under Monitoring (VUM), including Gamma (P.1) and Zeta (P.2). In addition, we provide a preliminary genomic overview of the epidemic in Paraguay, showing evidence of importation from Brazil. These data reinforce the usefulness and need for the implementation of widespread genomic surveillance in South America as a toolkit for pandemic monitoring that provides a means to follow the real-time spread of emerging SARS-CoV-2 variants with possible implications for public health and immunization strategies.
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Affiliation(s)
- Marta Giovanetti
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Svetoslav Nanev Slavov
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
- Butantan Institute, São Paulo, Brazil
| | - Vagner Fonseca
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Coordenação Geral de Laboratórios de Saúde Pública/Secretaria de Vigilância em Saúde, Ministério da Saúde (CGLAB/SVS-MS) Brasília, Distrito Federal, Brazil
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University; Stellenbosch, South Africa
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University; Stellenbosch, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University; Stellenbosch, South Africa
| | | | | | - James Emmanuel San
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University; Stellenbosch, South Africa
| | - Evandra Strazza Rodrigues
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | - Elaine Vieira Santos
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | - Flavia Aburjaile
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Joilson Xavier
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Hegger Fritsch
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Talita Emile Ribeiro Adelino
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Felicidade Pereira
- Laboratorio Central de Saude Publica da Bahia–LACEN-BA, Salvador, Bahia, Brazil
| | - Arabela Leal
- Laboratorio Central de Saude Publica da Bahia–LACEN-BA, Salvador, Bahia, Brazil
| | - Felipe Campos de Melo Iani
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Glauco de Carvalho Pereira
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | | | - Gladys Mercedes Estigarribia Sanabria
- Universidad Nacional del Caaguazú, Instituto Regional de Investigación en Salud
- Laboratorio de Biología Molecular, Hospital Regional de Coronel Oviedo
- Ministerio de Salud Pública y Bienestar Social
| | | | - Luiz Demarchi
- Laboratório Central de Saúde Pública do Estado de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
| | | | - Rafael dos Santos Bezerra
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | | | | | | | | | | | | | | | | | | | | | | | | | - Vlademir Vicente Cantarelli
- Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Universidade Feevale, Grupo Exame Laboratórios, Rio Grande do Sul, Brazil
| | - Stephane Tosta
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Laboratorio Central de Saude Publica da Bahia–LACEN-BA, Salvador, Bahia, Brazil
| | | | | | | | | | - Adriana Aparecida Ribeiro
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Natália Rocha Guimarães
- Laboratório Central de Saúde Pública do Estado de Minas Gerais, Fundac ão Ezequiel Dias, Belo Horizonte, Minas Gerais, Brazil
| | - Luiz Takao Watanabe
- Laboratório Central de Saúde Pública do Estado de Mato Grosso, Cuiabá, Brazil
| | | | | | | | | | | | | | - Juan Torales
- Laboratorio Central de Salud Pública, Asunción, Paraguay
| | | | | | - Gloria Patricia Martínez Figueredo
- Universidad Nacional del Caaguazú, Instituto Regional de Investigación en Salud
- Laboratorio de Biología Molecular, Hospital Regional de Coronel Oviedo
- Ministerio de Salud Pública y Bienestar Social
| | | | | | | | | | | | | | | | | | | | | | - Sonia Raboni
- Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, PR
| | | | | | - Camila Zanluca
- Laboratório de Virologia Molecular - Instituto Carlos Chagas/Fiocruz PR, Curitiba, PR
| | | | - Patricia Akemi Assato
- São Paulo State University (UNESP), School of Agricultural Sciences, Department of Bioprocesses and Biotechnology, Botucatu, Brazil
| | - Felipe Allan da Silva da Costa
- São Paulo State University (UNESP), School of Agricultural Sciences, Department of Bioprocesses and Biotechnology, Botucatu, Brazil
| | - Mirele Daiana Poleti
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, São Paulo, Brazil
| | - Jessika Cristina Chagas Lesbon
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, São Paulo, Brazil
| | - Elisangela Chicaroni Mattos
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, São Paulo, Brazil
| | - Cecilia Artico Banho
- Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto
| | - Lívia Sacchetto
- Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto
| | - Marília Mazzi Moraes
- Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto
| | - Rejane Maria Tommasini Grotto
- São Paulo State University (UNESP), School of Agricultural Sciences, Department of Bioprocesses and Biotechnology, Botucatu, Brazil
- Molecular Biology Laboratory, Applied Biotechnology Laboratory, Clinical Hospital of the Botucatu Medical School, Brazil
| | - Jayme A. Souza-Neto
- São Paulo State University (UNESP), School of Agricultural Sciences, Department of Bioprocesses and Biotechnology, Botucatu, Brazil
| | - Maurício Lacerda Nogueira
- Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto
| | - Heidge Fukumasu
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, São Paulo, Brazil
| | - Luiz Lehmann Coutinho
- University of São Paulo, Centro de Genômica Funcional da ESALQ, Piracicaba, SP, Brazil
| | - Rodrigo Tocantins Calado
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | | | | | | | - Carla Freitas
- Coordenação Geral de Laboratórios de Saúde Pública/Secretaria de Vigilância em Saúde, Ministério da Saúde (CGLAB/SVS-MS) Brasília, Distrito Federal, Brazil
| | - Cassio Roberto Leonel Peterka
- Coordenação Geral das Arboviroses, Secretaria de Vigilaçncia em Saúde/Ministério da Saúde (CGARB/SVS-MS), Brasília, Distrito Federal, Brazil
| | - Cássia de Fátima Rangel Fernandes
- Departamento de Imunização e Doenças Transmissíveisa/Secretaria de Vigilância em Saúde, Ministério da Saúde, Brasília, Distrito Federal, Brazil
| | | | | | - Maria Almiron
- Organização Pan-Americana da Saúde/Organização Mundial da Saúde, Brasília, Distrito Federal, Brazil
| | | | - José Lourenço
- Department of Zoology, Peter Medawar Building, University of Oxford, Oxford, UK
- Biosystems and Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University; Stellenbosch, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Edward C. Holmes
- Sydney Institute for Infectious Diseases, School of Life and Environmental Sciences and School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
| | | | | | | | - Simone Kashima
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
| | - Luiz Carlos Junior de Alcantara
- Laboratório de Flavivírus, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Dimas Tadeu Covas
- University of São Paulo, Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, Ribeirão Preto, SP, Brazil
- Butantan Institute, São Paulo, Brazil
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18
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Li T, Guo Y. Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination. CHAOS, SOLITONS, AND FRACTALS 2022; 156:111825. [PMID: 35125677 PMCID: PMC8801310 DOI: 10.1016/j.chaos.2022.111825] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/08/2022] [Accepted: 01/17/2022] [Indexed: 05/06/2023]
Abstract
As people around the world work to stop the COVID-19 pandemic, mutated COVID-19 (Delta strain) that are more contagious are emerging in many places. How to develop effective and reasonable plans to prevent the spread of mutated COVID-19 is an important issue. In order to simulate the transmission of mutated COVID-19 (Delta strain) in China with a certain proportion of vaccination, we selected the epidemic situation in Jiangsu Province as a case study. To solve this problem, we develop a novel epidemic model with a vaccinated population. The basic properties of the model is analyzed, and the expression of the basic reproduction number R 0 is obtained. We collect data on the Delta strain epidemic in Jiangsu Province, China from July 20, to August 5, 2021. The weighted nonlinear least square estimation method is used to fit the daily asymptomatic infected people, common infected people and severe infected people. The estimated parameter values are obtained, the approximate values of the basic reproduction number are calculated R 0 ≈ 1.378 . Through the global sensitivity analysis, we identify some parameters that have a greater impact on the prevalence of the disease. Finally, according to the evaluation results of parameter influence, we consider three control measures (vaccination, isolation and nucleic acid testing) to control the spread of the disease. The results of the study found that the optimal control measure is to dynamically adjust the three control measures to achieve the lowest number of infections at the lowest cost. The research in this paper can not only enrich theoretical research on the transmission of COVID-19, but also provide reliable control suggestions for countries and regions experiencing mutated COVID-19 epidemics.
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Affiliation(s)
- Tingting Li
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
| | - Youming Guo
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
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19
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Shao L, Cao Y, Jones T, Santosh M, Silva LFO, Ge S, da Boit K, Feng X, Zhang M, BéruBé K. COVID-19 mortality and exposure to airborne PM 2.5: A lag time correlation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:151286. [PMID: 34743816 PMCID: PMC8553633 DOI: 10.1016/j.scitotenv.2021.151286] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/07/2021] [Accepted: 10/23/2021] [Indexed: 05/05/2023]
Abstract
COVID-19 has escalated into one of the most serious crises in the 21st Century. Given the rapid spread of SARS-CoV-2 and its high mortality rate, here we investigate the impact and relationship of airborne PM2.5 to COVID-19 mortality. Previous studies have indicated that PM2.5 has a positive relationship with the spread of COVID-19. To gain insights into the delayed effect of PM2.5 concentration (μgm-3) on mortality, we focused on the role of PM2.5 in Wuhan City in China and COVID-19 during the period December 27, 2019 to April 7, 2020. We also considered the possible impact of various meteorological factors such as temperature, precipitation, wind speed, atmospheric pressure and precipitation on pollutant levels. The results from the Pearson's correlation coefficient analyses reveal that the population exposed to higher levels of PM2.5 pollution are susceptible to COVID-19 mortality with a lag time of >18 days. By establishing a generalized additive model, the delayed effect of PM2.5 on the death toll of COVID-19 was verified. A negative correction was identified between temperature and number of COVID-19 deaths, whereas atmospheric pressure exhibits a positive correlation with deaths, both with a significant lag effect. The results from our study suggest that these epidemiological relationships may contribute to the understanding of the COVID-19 pandemic and provide insights for public health strategies.
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Affiliation(s)
- Longyi Shao
- State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
| | - Yaxin Cao
- State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Tim Jones
- School of Earth and Environmental Sciences, Cardiff University, Park Place, Cardiff CF10 3AT, UK
| | - M Santosh
- School of Earth Sciences and Resources, China University of Geoscience Beijing, Beijing 100083, China; Department of Earth Science, University of Adelaide, Adelaide, SA 5005, Australia
| | - Luis F O Silva
- Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
| | - Shuoyi Ge
- State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Kátia da Boit
- Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
| | - Xiaolei Feng
- State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Mengyuan Zhang
- State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Kelly BéruBé
- School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, Wales, UK
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20
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Chen X, Huang H, Ju J, Sun R, Zhang J. Impact of vaccination on the COVID-19 pandemic in U.S. states. Sci Rep 2022; 12:1554. [PMID: 35091640 PMCID: PMC8799714 DOI: 10.1038/s41598-022-05498-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 01/13/2022] [Indexed: 11/09/2022] Open
Abstract
Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies.
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Affiliation(s)
- Xiao Chen
- School of International Trade and Economics, University of International Business and Economics, Beijing, China
| | - Hanwei Huang
- Department of Economics and Finance, City University of Hong Kong, Hong Kong, China. .,Centre for Economic Performance, London School of Economics, London, United Kingdom.
| | - Jiandong Ju
- PBC School of Finance, Tsinghua University, Beijing, China
| | - Ruoyan Sun
- Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, USA.
| | - Jialiang Zhang
- PBC School of Finance, Tsinghua University, Beijing, China
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21
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Nian F, Shi Y, Cao J. COVID-19 Propagation Model Based on Economic Development and Interventions. WIRELESS PERSONAL COMMUNICATIONS 2022; 122:2355-2365. [PMID: 34421225 PMCID: PMC8371428 DOI: 10.1007/s11277-021-08998-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/08/2021] [Indexed: 05/03/2023]
Abstract
In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.
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Affiliation(s)
- Fuzhong Nian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Yayong Shi
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Cao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China
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22
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Reguly IZ, Csercsik D, Juhász J, Tornai K, Bujtár Z, Horváth G, Keömley-Horváth B, Kós T, Cserey G, Iván K, Pongor S, Szederkényi G, Röst G, Csikász-Nagy A. Microsimulation based quantitative analysis of COVID-19 management strategies. PLoS Comput Biol 2022; 18:e1009693. [PMID: 34982766 PMCID: PMC8759654 DOI: 10.1371/journal.pcbi.1009693] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 01/14/2022] [Accepted: 11/29/2021] [Indexed: 12/11/2022] Open
Abstract
Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.
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Affiliation(s)
- István Z. Reguly
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Cytocast Kft., Vecsés, Hungary
| | - Dávid Csercsik
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - János Juhász
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Medical Microbiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Kálmán Tornai
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Zsófia Bujtár
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Horváth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Bence Keömley-Horváth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Cytocast Kft., Vecsés, Hungary
| | - Tamás Kós
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - György Cserey
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Kristóf Iván
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Sándor Pongor
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gábor Szederkényi
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Röst
- Bolyai Institute, University of Szeged, Szeged, Hungary
| | - Attila Csikász-Nagy
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Cytocast Kft., Vecsés, Hungary
- Randall Centre for Cell and Molecular Biophysics, King’s College London, London, United Kingdom
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23
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A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19. MATHEMATICS 2021. [DOI: 10.3390/math9233075] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This study aims to identify the key factors affecting individuals’ behavioral vaccination intention against COVID-19 in Vietnam through an online questionnaire survey. Differing from previous studies, a novel three-staged approach combining Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Partial Least Squares-Structural Equation Model (PLS-SEM), and Artificial Neural Network (ANN) is proposed. Five factors associated with individuals’ behavioral vaccination intention (INT) based on 15 experts’ opinions are considered in SF-AHP analysis, including Perceived Severity of COVID-19 (PSC), Perceived COVID-19 vaccines (PVC), Trust in government intervention strategies (TRS), Social Influence (SOI), and Social media (SOM). First, the results of SF-AHP indicated that all proposed factors correlate with INT. Second, the data of 474 valid respondents were collected and analyzed using PLS-SEM. The PLS-SEM results reported that INT was directly influenced by PVC and TRS. In contrast, SOI had no direct effect on INT. Further, PSC and SOM moderated the relationship between PVC, TRS and INT, respectively. The ANN was deployed to validate the previous stages and found that the best predictors of COVID-19 vaccination intention were PVC, TRS, and SOM. These results were consistent with the SF-AHP and PLS-SEM models. This research provides an innovative new approach employing quantitative and qualitative techniques to understand individuals’ vaccination intention during the global pandemic. Furthermore, the proposed method can be used and expanded to assess the perceived efficacy of COVID-19 measures in other nations currently battling the COVID-19 outbreak.
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24
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Ejigu BA, Asfaw MD, Cavalerie L, Abebaw T, Nanyingi M, Baylis M. Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modelling study of the case of Ethiopia. PLoS One 2021; 16:e0259874. [PMID: 34784379 PMCID: PMC8594814 DOI: 10.1371/journal.pone.0259874] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 10/28/2021] [Indexed: 12/23/2022] Open
Abstract
The World Health Organization (WHO) declared COVID-19 a pandemic on March 11, 2020 and by November 14, 2020 there were 53.3M confirmed cases and 1.3M reported deaths in the world. In the same period, Ethiopia reported 102K cases and 1.5K deaths. Effective public health preparedness and response to COVID-19 requires timely projections of the time and size of the peak of the outbreak. Currently, Ethiopia under the COVAX facility has begun vaccinating high risk populations but due to vaccine supply shortages and the absence of an effective treatment, the implementation of NPIs (non-pharmaceutical interventions), like hand washing, wearing face coverings or social distancing, still remain the most effective methods of controlling the pandemic as recommended by WHO. This study proposes a modified Susceptible Exposed Infected and Recovered (SEIR) model to predict the number of COVID-19 cases at different stages of the disease under the implementation of NPIs at different adherence levels in both urban and rural settings of Ethiopia. To estimate the number of cases and their peak time, 30 different scenarios were simulated. The results indicated that the peak time of the pandemic is different in urban and rural populations of Ethiopia. In the urban population, under moderate implementation of three NPIs the pandemic will be expected to reach its peak in December, 2020 with 147,972 cases, of which 18,100 are symptomatic and 957 will require admission to an Intensive Care Unit (ICU). Among the implemented NPIs, increasing the coverage of wearing masks by 10% could reduce the number of new cases on average by one-fifth in urban-populations. Varying the coverage of wearing masks in rural populations minimally reduces the number of cases. In conclusion, the models indicate that the projected number of hospital cases during the peak time is higher than the Ethiopian health system capacity. To contain symptomatic and ICU cases within the health system capacity, the government should pay attention to the strict implementation of the existing NPIs or impose additional public health measures.
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Affiliation(s)
- Bedilu Alamirie Ejigu
- Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Manalebish Debalike Asfaw
- Department of Mathematics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Lisa Cavalerie
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
- International Livestock Research Institute, Addis Ababa, Ethiopia
| | - Tilahun Abebaw
- Department of Mathematics, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Mark Nanyingi
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
- Department of Epidemiology and Public Health, School of Public Health, University of Nairobi, Nairobi, Kenya
| | - Matthew Baylis
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom
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25
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A Hybrid Spherical Fuzzy MCDM Approach to Prioritize Governmental Intervention Strategies against the COVID-19 Pandemic: A Case Study from Vietnam. MATHEMATICS 2021. [DOI: 10.3390/math9202626] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The unprecedented coronavirus pandemic (COVID-19) is fluctuating worldwide. Since the COVID-19 epidemic has a negative impact on all countries and has become a significant threat, it is necessary to determine the most effective strategy for governments by considering a variety of criteria; however, few studies in the literature can assist governments in this topic. Selective governmental intervention during the COVID-19 outbreak is considered a Multi-Criteria Decision-Making (MCDM) problem under a vague and uncertain environment when governments and medical communities adjust their priorities in response to rising issues and the efficacy of interventions applied in various nations. In this study, a novel hybrid Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS-F) model is proposed to help stakeholders such as governors and policymakers to prioritize governmental interventions for dealing with the COVID-19 outbreak. The SF-AHP is implemented to measure the significance of the criteria, while the WASPAS-F approach is deployed to rank intervention alternatives. An empirical case study is conducted in Vietnam. From the SF-AHP findings, the criteria of “effectiveness in preventing the spread of COVID-19”, “ease of implementation”, and “high acceptability to citizens” were recognized as the most important criteria. As for the ranking of strategies, “vaccinations”, “enhanced control of the country’s health resources”, “common health testing”, “formation of an emergency response team”, and “quarantining patients and those suspected of infection” are the top five strategies. Aside from that, the robustness of the approach was tested by performing a comparative analysis. The results illustrate that the applied methods reach the general best strategy rankings. The applied methodology and its analysis will provide insight to authorities for fighting against the severe pandemic in the long run. It may aid in solving many complicated challenges in government strategy selection and assessment. It is also a flexible design model for considering the evaluation criteria. Finally, this research provides valuable guidance for policymakers in other nations.
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Jordan E, Shin DE, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:130072-130093. [PMID: 35781925 PMCID: PMC8768956 DOI: 10.1109/access.2021.3113812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/10/2021] [Indexed: 05/08/2023]
Abstract
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
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Affiliation(s)
- Elizabeth Jordan
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Delia E. Shin
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Surbhi Leekha
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shapour Azarm
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
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The evolution of Brazilian Health Sciences and the present situation. LANCET REGIONAL HEALTH. AMERICAS 2021; 3:100044. [PMID: 36777403 PMCID: PMC9903842 DOI: 10.1016/j.lana.2021.100044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/28/2021] [Accepted: 08/05/2021] [Indexed: 11/20/2022]
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Estadilla CDS, Uyheng J, de Lara-Tuprio EP, Teng TR, Macalalag JMR, Estuar MRJE. Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines. Infect Dis Poverty 2021; 10:107. [PMID: 34372929 PMCID: PMC8352160 DOI: 10.1186/s40249-021-00886-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/15/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions. METHODS Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government's pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin's minimum principle and numerically solved using the forward-backward sweep method. RESULTS Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in [Formula: see text] reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by [Formula: see text], while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an [Formula: see text] increase in the cost of the optimal strategy. CONCLUSION We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures.
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Affiliation(s)
- Carlo Delfin S Estadilla
- Department of Mathematics, Ateneo de Manila University, Katipunan Ave., Brgy. Loyola Heights, 1102, Quezon City, Philippines.
| | - Joshua Uyheng
- Department of Psychology, Ateneo de Manila University, Quezon City, Philippines
| | - Elvira P de Lara-Tuprio
- Department of Mathematics, Ateneo de Manila University, Katipunan Ave., Brgy. Loyola Heights, 1102, Quezon City, Philippines
| | - Timothy Robin Teng
- Department of Mathematics, Ateneo de Manila University, Katipunan Ave., Brgy. Loyola Heights, 1102, Quezon City, Philippines
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