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Chu YM, Rashid S, Akdemir AO, Khalid A, Baleanu D, Al-Sinan BR, Elzibar OAI. Predictive dynamical modeling and stability of the equilibria in a discrete fractional difference COVID-19 epidemic model. RESULTS IN PHYSICS 2023; 49:106467. [PMID: 37153140 PMCID: PMC10140436 DOI: 10.1016/j.rinp.2023.106467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/09/2023]
Abstract
The SARSCoV-2 virus, also known as the coronavirus-2, is the consequence of COVID-19, a severe acute respiratory syndrome. Droplets from an infectious individual are how the pathogen is transmitted from one individual to another and occasionally, these particles can contain toxic textures that could also serve as an entry point for the pathogen. We formed a discrete fractional-order COVID-19 framework for this investigation using information and inferences from Thailand. To combat the illnesses, the region has implemented mandatory vaccination, interpersonal stratification and mask distribution programs. As a result, we divided the vulnerable people into two groups: those who support the initiatives and those who do not take the influence regulations seriously. We analyze endemic problems and common data while demonstrating the threshold evolution defined by the fundamental reproductive quantity R 0 . Employing the mean general interval, we have evaluated the configuration value systems in our framework. Such a framework has been shown to be adaptable to changing pathogen populations over time. The Picard Lindelöf technique is applied to determine the existence-uniqueness of the solution for the proposed scheme. In light of the relationship between the R 0 and the consistency of the fixed points in this framework, several theoretical conclusions are made. Numerous numerical simulations are conducted to validate the outcome.
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Affiliation(s)
- Yu-Ming Chu
- Department of Mathematics, Huzhou University, Huzhou, 313000, China
| | - Saima Rashid
- Department of Mathematics, Government College University, Faisalabad 38000, Pakistan
| | - Ahmet Ocak Akdemir
- Department of Mathematics, Faculty of Science and Arts, Agri Ibrahim Cecen University, Agrı, Turkey
| | - Aasma Khalid
- Department of Mathematics, Government College women University, Faisalabad, Pakistan
| | - Dumitru Baleanu
- Department of Mathematics, Cankaya University, Ankara, Turkey
- Institute of Space Sciences, 06530 Bucharest, Romania
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut 11022801, Lebanon
| | - Bushra R Al-Sinan
- University of Hafr Al-Batin, Nairiyah College, Department of Administrative and Financial Sciences, Saudi Arabia
| | - O A I Elzibar
- Department of Mathematics, Turabah University College, Taif University, P.O. Box 1109, Taif 21944, Saudi Arabia
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Chen B, Zhao Y, Jin Z, He D, Li H. Twice evasions of Omicron variants explain the temporal patterns in six Asian and Oceanic countries. BMC Infect Dis 2023; 23:25. [PMID: 36639649 PMCID: PMC9839219 DOI: 10.1186/s12879-023-07984-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The ongoing coronavirus 2019 (COVID-19) pandemic has emerged and caused multiple pandemic waves in the following six countries: India, Indonesia, Nepal, Malaysia, Bangladesh and Myanmar. Some of the countries have been much less studied in this devastating pandemic. This study aims to assess the impact of the Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the reproduction number [Formula: see text] in these six South Asia, Southeast Asia and Oceania countries. METHODS We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate [Formula: see text] to fit the multiple waves of the COVID-19 pandemic and to estimate the IFR and [Formula: see text] in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of the Omicron variant are also considered in this model. RESULTS We fit our model to the reported deaths well. We estimate the IFR (in the range of 0.016 to 0.136%) and the reproduction number [Formula: see text] (in the range of 0 to 9) in the six countries. Multiple pandemic waves in each country were observed in our simulation results. CONCLUSIONS The invasion of the Omicron variant caused the new pandemic waves in the six countries. The higher [Formula: see text] suggests the intrinsic transmissibility advantage of the Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and vaccine coverage.
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Affiliation(s)
- Boqiang Chen
- grid.16890.360000 0004 1764 6123Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Yanji Zhao
- grid.16890.360000 0004 1764 6123Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Zhen Jin
- grid.163032.50000 0004 1760 2008Complex Systems Research Center, Shanxi University, Taiyuan, China
| | - Daihai He
- grid.16890.360000 0004 1764 6123Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Huaichen Li
- grid.460018.b0000 0004 1769 9639Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Fisher A, Xu H, He D, Wang X. Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4816-4837. [PMID: 36896524 DOI: 10.3934/mbe.2023223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model [1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.
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Affiliation(s)
- Allison Fisher
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA
| | - Hainan Xu
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Daihai He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Xueying Wang
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA
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Thongtha A, Modnak C. Optimal COVID-19 epidemic strategy with vaccination control and infection prevention measures in Thailand. Infect Dis Model 2022; 7:835-855. [PMCID: PMC9678212 DOI: 10.1016/j.idm.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/14/2022] [Accepted: 11/03/2022] [Indexed: 11/23/2022] Open
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Fosdick BK, Bayham J, Dilliott J, Ebel GD, Ehrhart N. Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities. Infect Dis Model 2022; 7:463-472. [PMID: 35854786 PMCID: PMC9283126 DOI: 10.1016/j.idm.2022.07.003] [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: 03/27/2022] [Revised: 06/28/2022] [Accepted: 07/06/2022] [Indexed: 11/01/2022] Open
Abstract
The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide as of March 2021. As COVID-19 case rates declined and state and county restrictions were lifted in spring 2021, facility managers, local and state health agencies were challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern and variable vaccine uptake across facilities highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. This dashboard quantifies the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Key findings were that choice of COVID-19 diagnostic (ex. nasal swab qRT-PCR vs rapid antigen) and testing cadence has less impact on attack rate and staff workdays missed than does vaccination rates among staff and residents. Specifically, low vaccine uptake among staff at long-term care facilities puts staff and residents at risk of ongoing COVID-19 outbreaks. Here we present our model and dashboard as an exemplar of a tool for state public health officials and facility directors to gain insights from an infectious disease model that can directly inform policy decisions in the midst of a pandemic.
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Affiliation(s)
- Bailey K Fosdick
- Department of Statistics, Colorado State University, Ft. Collins, CO, 80523, USA
| | - Jude Bayham
- Department of Agricultural and Resource Economics, Colorado State University, Ft. Collins, CO, 80523, USA
| | - Jake Dilliott
- Department of Agricultural and Resource Economics, Colorado State University, Ft. Collins, CO, 80523, USA
| | - Gregory D Ebel
- Arthropod-Borne and Infectious Diseases Laboratory, Department of Microbiology, Immunology and Pathology, Colorado State University, Ft. Collins, CO, 80526, USA
| | - Nicole Ehrhart
- Columbine Health Systems Center for Healthy Aging and Department of Clinical Sciences, Colorado State University, Ft. Collins, CO, 80523, USA
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Yang T, Wang Y, Liu N, Abudurusuli G, Yang S, Yu S, Liu W, Yin X, Chen T. Modeling Cross-Regional Transmission and Assessing the Effectiveness of Restricting Inter-Regional Population Movements in Controlling COVID-19 - Xi'an City, Shaanxi Province, China, 2021. China CDC Wkly 2022; 4:685-692. [PMID: 36059792 PMCID: PMC9433766 DOI: 10.46234/ccdcw2022.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/19/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission.
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Affiliation(s)
- Tianlong Yang
- School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen City, Fujian Province, China
| | - Nankun Liu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | | | - Shiting Yang
- School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shanshan Yu
- School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Weikang Liu
- School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Xuecheng Yin
- School of Public Health, Yale University, New Haven, Connecticut, US
| | - Tianmu Chen
- School of Public Health, Xiamen University, Xiamen City, Fujian Province, China,State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen City, Fujian Province, China,Tianmu Chen,
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7
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Liu Y, Yu Y, Zhao Y, He D. Reduction in the infection fatality rate of Omicron variant compared with previous variants in South Africa. Int J Infect Dis 2022; 120:146-149. [PMID: 35462038 PMCID: PMC9022446 DOI: 10.1016/j.ijid.2022.04.029] [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: 01/18/2022] [Revised: 04/04/2022] [Accepted: 04/17/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE The SARS-CoV-2 Omicron (B.1.1.529) variant has caused global concern. Previous studies have shown that the variant has enhanced immune evasion ability and transmissibility and reduced severity. METHODS In this study, we developed a mathematical model with time-varying transmission rate, vaccination, and immune evasion. We fit the model to reported case and death data up to February 6, 2022 to estimate the transmissibility and infection fatality ratio of the Omicron variant in South Africa. RESULTS We found that the high relative transmissibility of the Omicron variant was mainly due to its immune evasion ability, whereas its infection fatality rate substantially decreased by approximately 78.7% (95% confidence interval: 66.9%, 85.0%) with respect to previous variants. CONCLUSION On the basis of data from South Africa and mathematical modeling, we found that the Omicron variant is highly transmissible but with significantly lower infection fatality rates than those of previous variants of SARS-CoV-2.
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Affiliation(s)
- Yuan Liu
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yangyang Yu
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China,State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yanji Zhao
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China,Research Institute for Future Food, The Hong Kong Polytechnic University, Hong Kong SAR, China,Correspondence author: Daihai He, Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
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8
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Edholm CJ, Levy B, Spence L, Agusto FB, Chirove F, Chukwu CW, Goldsman D, Kgosimore M, Maposa I, Jane White KA, Lenhart S. A vaccination model for COVID-19 in Gauteng, South Africa. Infect Dis Model 2022; 7:333-345. [PMID: 35702698 PMCID: PMC9181832 DOI: 10.1016/j.idm.2022.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 12/02/2022] Open
Abstract
The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.
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Affiliation(s)
| | - Benjamin Levy
- Mathematics Department, Fitchburg State University, Fitchburg, MA, USA
| | - Lee Spence
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Folashade B Agusto
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA
| | - Faraimunashe Chirove
- Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa
| | - C Williams Chukwu
- Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa
| | - David Goldsman
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Moatlhodi Kgosimore
- Biometry and Mathematics Department, Botswana University of Agriculture and Natural Resources, Gaborone, Botswana
| | - Innocent Maposa
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - K A Jane White
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
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Zhao S, Cao P, Gao D, Zhuang Z, Wang W, Ran J, Wang K, Yang L, Einollahi MR, Lou Y, He D, Wang MH. Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data. Infect Dis Model 2022; 7:189-195. [PMID: 35637656 PMCID: PMC9132685 DOI: 10.1016/j.idm.2022.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 05/20/2022] [Accepted: 05/22/2022] [Indexed: 11/29/2022] Open
Abstract
The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7–7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.
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Fan G, Song H, Yip S, Zhang T, He D. Impact of low vaccine coverage on the resurgence of COVID-19 in Central and Eastern Europe. One Health 2022; 14:100402. [PMID: 35611185 PMCID: PMC9119166 DOI: 10.1016/j.onehlt.2022.100402] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/14/2022] [Accepted: 05/14/2022] [Indexed: 01/19/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused a tremendous global impact both socially and economically. The mechanisms behind the disparity in the severity, vaccine coverage, and variant replacement patterns across European countries are unclear. In this work, we aim to reveal the possible reasons via data visualization and model fitting. We developed a model with a vaccination component to simulate the mortality waves in these countries. Deaths averted by the vaccination campaign were estimated. Finally, we discuss the potential reasons behind the differences in vaccine coverage across European countries. Contemporary transportation and global trade bring significant convenience to our daily life but also facilitate the spread of the novel virus COVID-19 to anywhere globally within a short time. The observations and results in this work highlight the importance of the global campaign to mitigate the COVID-19 pandemic and future pandemics under the One Health approach. We reveal disparity in COVID-19 vaccine coverage across European counties. We reveal different patterns of COVID-19 variants across European countries. Using a mathematical model, we calculate deaths averted by the vaccine in Europe. We discuss the reasons behind the disparity in vaccine coverage in Europe.
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Musa SS, Tariq A, Yuan L, Haozhen W, He D. Infection fatality rate and infection attack rate of COVID-19 in South American countries. Infect Dis Poverty 2022. [PMID: 35382879 DOI: 10.21203/rs.3.rs-1126392/v1] [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] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries. METHODS We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries. RESULTS We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. CONCLUSIONS This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.
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Affiliation(s)
- Salihu Sabiu Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Liu Yuan
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Haozhen
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
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12
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Musa SS, Tariq A, Yuan L, Haozhen W, He D. Infection fatality rate and infection attack rate of COVID-19 in South American countries. Infect Dis Poverty 2022; 11:40. [PMID: 35382879 PMCID: PMC8983329 DOI: 10.1186/s40249-022-00961-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ([Formula: see text]) for twelve most affected South American countries. METHODS We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and [Formula: see text] of COVID-19 for the South American countries. RESULTS We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and [Formula: see text] (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. CONCLUSIONS This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.
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Affiliation(s)
- Salihu Sabiu Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA USA
| | - Liu Yuan
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Haozhen
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
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13
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Wei H, Musa SS, Zhao Y, He D. Modelling of Waning of Immunity and Reinfection Induced Antibody Boosting of SARS-CoV-2 in Manaus, Brazil. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:1729. [PMID: 35162752 PMCID: PMC8835474 DOI: 10.3390/ijerph19031729] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
It was reported that the Brazilian city, Manaus, likely exceeded the herd immunity threshold (presumably 60-70%) in November 2020 after the first wave of COVID-19, based on the serological data of a routine blood donor. However, a second wave started in November 2020, when an even higher magnitude of deaths hit the city. The arrival of the second wave coincided with the emergence of the Gamma (P.1) variant of SARS-CoV-2, with higher transmissibility, a younger age profile of cases, and a higher hospitalization rate. Prete et al. (2020 MedRxiv 21256644) found that 8 to 33 of 238 (3.4-13.9%) repeated blood donors likely were infected twice in Manaus between March 2020 and March 2021. It is unclear how this finding can be used to explain the second wave. We propose a simple model which allows reinfection to explain the two-wave pattern in Manaus. We find that the two waves with 30% and 40% infection attack rates, respectively, and a reinfection ratio at 3.4-13.9%, can explain the two waves well. We argue that the second wave was likely because the city had not exceeded the herd immunity level after the first wave. The reinfection likely played a weak role in causing the two waves.
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Affiliation(s)
| | | | | | - Daihai He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China; (H.W.); (S.S.M.); (Y.Z.)
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Feng A, Obolski U, Stone L, He D. Modelling COVID-19 vaccine breakthrough infections in highly vaccinated Israel-The effects of waning immunity and third vaccination dose. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0001211. [PMID: 36962648 PMCID: PMC10021336 DOI: 10.1371/journal.pgph.0001211] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 10/09/2022] [Indexed: 11/11/2022]
Abstract
In August 2021, a major wave of the SARS-CoV-2 Delta variant erupted in the highly vaccinated population of Israel. The transmission advantage of the Delta variant enabled it to replace the Alpha variant in approximately two months. The outbreak led to an unexpectedly large proportion of breakthrough infections (BTI)-a phenomenon that received worldwide attention. Most of the Israeli population, especially those aged 60+, received their second dose of the vaccination four months before the invasion of the Delta variant. Hence, either the vaccine induced immunity dropped significantly or the Delta variant possesses immunity escaping abilities, or both. In this work, we model data obtained from the Israeli Ministry of Health, to help understand the epidemiological factors involved in the outbreak. We propose a mathematical model that captures a multitude of factors, including age structure, the time varying vaccine efficacy, time varying transmission rate, BTIs, reduced susceptibility and infectivity of vaccinated individuals, protection duration of the vaccine induced immunity, and the vaccine distribution. We fitted our model to COVID-19 cases among the vaccinated and unvaccinated, for <60 and 60+ age groups, and quantified the transmission rate, the vaccine efficacy over time and the impact of the third dose booster vaccine. The peak transmission rate of the Delta variant was found to be 2.14 times higher than that of the Alpha variant. The two-dose vaccine efficacy against infection dropped significantly from >90% to ~40% over 6 months. We further performed model simulations and quantified counterfactual scenarios examining what would happen if the booster had not been rolled out. We estimated that approximately 4.03 million infective cases (95%CI 3.19, 4.86) were prevented by vaccination overall, and 1.22 million infective cases (95%CI 0.89, 1.62) averted by the booster.
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Affiliation(s)
- Anyin Feng
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Uri Obolski
- School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Lewi Stone
- Mathematical Sciences, School of Science, RMIT University, Melbourne, Australia
- Biomathematics Unit, School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Daihai He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Future Food, The Hong Kong Polytechnic University, Hong Kong, China
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15
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Song H, Fan G, Liu Y, Wang X, He D. The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination. Front Med (Lausanne) 2021; 8:773110. [PMID: 34970562 PMCID: PMC8712656 DOI: 10.3389/fmed.2021.773110] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/11/2021] [Indexed: 01/18/2023] Open
Abstract
Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention. Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate. Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves. Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.
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Affiliation(s)
- Haitao Song
- Complex Systems Research Center, Shanxi University, Taiyuan, China
| | - Guihong Fan
- Department of Mathematics, Columbus State University, Columbus, OH, United States
| | - Yuan Liu
- Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Xueying Wang
- Department of Mathematics and Statistics, Washington State University, Pullman, WA, United States
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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16
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Song H, Fan G, Zhao S, Li H, Huang Q, He D. Forecast of the COVID-19 trend in India: A simple modelling approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:9775-9786. [PMID: 34814368 DOI: 10.3934/mbe.2021479] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. Multiple factors (e.g., Delta variants with increased transmissibility) could have driven the rapid growth of the epidemic in India and led to a large number of deaths within a short period. We aim to reconstruct the transmission rate, estimate the infection fatality rate and forecast the epidemic size. We download the reported COVID-19 mortality data in India and formulate a simple mathematical model with a flexible transmission rate. We use iterated filtering to fit our model to deaths data. We forecast the infection attack rate in a month ahead. Our model simulation matched the reported deaths well and is reasonably close to the results of the serological study. We forecast that the infection attack rate (IAR) could have reached 43% by July 24, 2021, under the current trend. Our estimated infection fatality rate is about 0.07%. Under the current trend, the IAR will likely reach a level of 43% by July 24, 2021. Our estimated infection fatality rate appears unusually low, which could be due to a low case to infection ratio reported in previous study. Our approach is readily applicable in other countries and with other types of data (e.g., excess deaths).
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Affiliation(s)
- Haitao Song
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, China
| | - Guihong Fan
- Department of Mathematics, Columbus State University, Columbus 31907, USA
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
| | - Huaichen Li
- Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Qihua Huang
- School of Mathematical and Statistical Sciences, Southwest University, Chongqing 400715, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
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17
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Qian Y, Xie W, Zhao J, Xue M, Liu S, Wang L, Li W, Dai L, Cai Y. Investigating the effectiveness of re-opening policies before vaccination during a pandemic: SD modelling research based on COVID-19 in Wuhan. BMC Public Health 2021; 21:1638. [PMID: 34493226 PMCID: PMC8423339 DOI: 10.1186/s12889-021-11631-w] [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: 03/31/2021] [Accepted: 08/16/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. Early re-opening is preferable, but it introduces the risk of a resurgence of the epidemic. Although the World Health Organization has outlined criteria for re-opening, decisions on re-opening are mainly based on epidemiologic criteria. To date, the effectiveness of re-opening policies remains unclear. METHODS A system dynamics COVID-19 model, SEIHR(Q), was constructed by integrating infection prevention and control measures implemented in Wuhan into the classic SEIR epidemiological model and was validated with real-world data. The input data were obtained from official websites and the published literature. RESULTS The simulation results showed that track-and-trace measures had significant effects on the level of risk associated with re-opening. In the case of Wuhan, where comprehensive contact tracing was implemented, there would have been almost no risk associated with re-opening. With partial contact tracing, re-opening would have led to a minor second wave of the epidemic. However, if only limited contact tracing had been implemented, a more severe second outbreak of the epidemic would have occurred, overwhelming the available medical resources. If the ability to implement a track-trace-quarantine policy is fixed, the epidemiological criteria need to be further taken into account. The model simulation revealed different levels of risk associated with re-opening under different levels of track-and-trace ability and various epidemiological criteria. A matrix was developed to evaluate the effectiveness of the re-opening policies. CONCLUSIONS The SEIHR(Q) model designed in this study can quantify the impact of various re-opening policies on the spread of COVID-19. Integrating epidemiologic criteria, the contact tracing policy, and medical resources, the model simulation predicts whether the re-opening policy is likely to lead to a further outbreak of the epidemic and provides evidence-based support for decisions regarding safe re-opening during an ongoing epidemic. KEYORDS COVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.
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Affiliation(s)
- Ying Qian
- Business School, University of Shanghai for Science & Technology, Shanghai, People’s Republic of China
| | - Wei Xie
- School of Public Administration, Faculty of Economics and Management, East China Normal University, Shanghai, People’s Republic of China
| | - Jidi Zhao
- School of Public Administration, Faculty of Economics and Management, East China Normal University, Shanghai, People’s Republic of China
| | - Ming Xue
- School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, People’s Republic of China
| | - Shiyong Liu
- Center for Governance Studies, Beijing Normal University, Zhuhai, 519087 People’s Republic of China
| | - Lei Wang
- School of Public Administration, Faculty of Economics and Management, East China Normal University, Shanghai, People’s Republic of China
| | - Wanglai Li
- Department of Information, Technology and Innovation, Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Luojia Dai
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Yuyang Cai
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
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18
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Albi G, Pareschi L, Zanella M. Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7161-7190. [PMID: 34814244 DOI: 10.3934/mbe.2021355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a SEIRD compartmental model with a social structure based on the age of individuals and stochastic inputs that account for data uncertainty, the effects of containment measures are introduced via an optimal control problem dependent on specific social activities, such as home, work, school, etc. Through a short time horizon approximation, we derive models with multiple feedback controls depending on social activities that allow us to assess the impact of selective relaxation of containment measures in the presence of uncertain data. After analyzing the effects of the various controls, results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed. Specific contact patterns in the home, work, school and other locations have been considered for each country. Numerical simulations show that a careful strategy of progressive relaxation of containment measures, such as that adopted by some governments, may be able to keep the epidemic under control by restarting various productive activities.
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Affiliation(s)
- Giacomo Albi
- Department of Computer Science, University of Verona, Str. Le Grazie 15, 37100 Verona, Italy
| | - Lorenzo Pareschi
- Department of Mathematics and Computer Science, University of Ferrara, Via Machiavelli 35, 37131 Ferrara, Italy
| | - Mattia Zanella
- Department of Mathematics, University of Pavia, Via Ferrata, 5, 27100 Pavia, Italy
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