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Khan MMUR, Tanimoto J. Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy. Infect Dis Model 2024; 9:657-672. [PMID: 38628352 PMCID: PMC11017064 DOI: 10.1016/j.idm.2024.03.004] [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: 01/04/2024] [Revised: 03/12/2024] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.
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Affiliation(s)
- Md. Mamun-Ur-Rashid Khan
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka, 816-8580, Japan
- Department of Mathematics, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka, 816-8580, Japan
- Faculty of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka, 816-8580, Japan
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Li Z, Pei L, Duan G, Chen S. A non-autonomous time-delayed SIR model for COVID-19 epidemics prediction in China during the transmission of Omicron variant. ELECTRONIC RESEARCH ARCHIVE 2024; 32:2203-2228. [DOI: 10.3934/era.2024100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
<abstract><p>With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate $ \beta(t) $ was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, $ \beta(t) $ was approximately a piecewise quadratic function. In most regions, the removed rate $ \gamma(t) $ was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, $ \gamma(t) $ displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production.</p></abstract>
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Affiliation(s)
- Zhiliang Li
- School of Mathematics and Statistics, Zhengzhou University, Henan 450001, China
| | - Lijun Pei
- School of Mathematics and Statistics, Zhengzhou University, Henan 450001, China
| | - Guangcai Duan
- School of Public Health, Zhengzhou University, Henan 450001, China
| | - Shuaiyin Chen
- School of Public Health, Zhengzhou University, Henan 450001, China
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Liu Y, Chai YH, Wu YF, Zhang YW, Wang L, Yang L, Shi YH, Wang LL, Zhang LS, Chen Y, Fan R, Wen YH, Yang H, Li L, Liu YH, Zheng HZ, Jiang JJ, Qian H, Tao RJ, Qian YC, Wang LW, Chen RC, Xu JF, Wang C. Risk factors associated with indoor transmission during home quarantine of COVID-19 patients. Front Public Health 2023; 11:1170085. [PMID: 37250088 PMCID: PMC10213781 DOI: 10.3389/fpubh.2023.1170085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/13/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose The study aimed to identify potential risk factors for family transmission and to provide precautionary guidelines for the general public during novel Coronavirus disease 2019 (COVID-19) waves. Methods A retrospective cohort study with numerous COVID-19 patients recruited was conducted in Shanghai. Epidemiological data including transmission details, demographics, vaccination status, symptoms, comorbidities, antigen test, living environment, residential ventilation, disinfection and medical treatment of each participant were collected and risk factors for family transmission were determined. Results A total of 2,334 COVID-19 patients participated. Compared with non-cohabitation infected patients, cohabitated ones were younger (p = 0.019), more commonly unvaccinated (p = 0.048) or exposed to infections (p < 0.001), and had higher rates of symptoms (p = 0.003) or shared living room (p < 0.001). Risk factors analysis showed that the 2019-nCov antigen positive (OR = 1.86, 95%CI 1.40-2.48, p < 0.001), symptoms development (OR = 1.86, 95%CI 1.34-2.58, p < 0.001), direct contact exposure (OR = 1.47, 95%CI 1.09-1.96, p = 0.010) were independent risk factors for the cohabitant transmission of COVID-19, and a separate room with a separate toilet could reduce the risk of family transmission (OR = 0.62, 95%CI 0.41-0.92, p = 0.018). Conclusion Patients showing negative 2019-nCov antigen tests, being asymptomatic, living in a separate room with a separate toilet, or actively avoiding direct contact with cohabitants were at low risk of family transmission, and the study recommended that avoiding direct contact and residential disinfection could reduce the risk of all cohabitants within the same house being infected with COVID-19.
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Affiliation(s)
- Yang Liu
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yan-Hua Chai
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yi-Fan Wu
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yu-Wei Zhang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Ling Wang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Ling Yang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yi-Han Shi
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Le-Le Wang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Li-Sha Zhang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yan Chen
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Rui Fan
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Yu-Hua Wen
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Heng Yang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Li Li
- Department of Respiratory Medicine, Baoshan District Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Yi-Han Liu
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Hui-Zhen Zheng
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Ji-Jin Jiang
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Hao Qian
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Ru-Jia Tao
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Ye-Chang Qian
- Department of Respiratory Medicine, Baoshan District Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Ling-Wei Wang
- Shenzhen Institute of Respiratory Disease, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Shenzhen Clinical Research Centre for Respirology, Shenzhen People’s Hospital, Shenzhen, China
| | - Rong-Chang Chen
- Shenzhen Institute of Respiratory Disease, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
- Shenzhen Clinical Research Centre for Respirology, Shenzhen People’s Hospital, Shenzhen, China
| | - Jin-Fu Xu
- Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, China
| | - Chen Wang
- National Center for Respiratory Medicine, Beijing, China
- National Clinical Research Center for Respiratory Disease, Beijing, China
- Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Kanda E, Suzuki A, Makino M, Tsubota H, Kanemata S, Shirakawa K, Yajima T. Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients. Sci Rep 2022; 12:20012. [PMID: 36411366 PMCID: PMC9678863 DOI: 10.1038/s41598-022-24562-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022] Open
Abstract
Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan-Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.
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Affiliation(s)
- Eiichiro Kanda
- grid.415086.e0000 0001 1014 2000Medical Science, Kawasaki Medical University, Okayama, Japan
| | - Atsushi Suzuki
- grid.256115.40000 0004 1761 798XDepartment of Endocrinology, Diabetes and Metabolism, Fujita Health University, Toyoake, Aichi Japan
| | - Masaki Makino
- grid.256115.40000 0004 1761 798XDepartment of Endocrinology, Diabetes and Metabolism, Fujita Health University, Toyoake, Aichi Japan
| | - Hiroo Tsubota
- grid.476017.30000 0004 0376 5631AstraZeneca K.K., Osaka, Japan
| | - Satomi Kanemata
- grid.459873.40000 0004 0376 2510Ono Pharmaceutical Co., Ltd., Osaka, Japan
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Qi H, Zhao W. Stability and bifurcation control analysis of a delayed fractional-order eco-epidemiological system. EUROPEAN PHYSICAL JOURNAL PLUS 2022; 137:934. [PMID: 35996429 PMCID: PMC9385103 DOI: 10.1140/epjp/s13360-022-03154-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Considering the factor of artificial intervention in biological control, a delayed fractional eco-epidemiological system with an extended feedback controller is proposed. By using the digestion delay as bifurcation parameter, the stability and Hopf bifurcation are investigated, and the branching conditions are given. The system undergoes Hopf bifurcation, when the parameter τ passes through the critical value. In addition, it can be pointed out that the negative feedback gain and the feedback delay could affect the bifurcation critical value of the system. Therefore, the Hopf bifurcation can also be induced by taking the feedback delay as a bifurcation parameter. Finally, by plotting the solution curve of the system, the significance of the controller to the stability of the eco-epidemiological system is verified.
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Affiliation(s)
- Hao Qi
- College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, 266590 People’s Republic of China
| | - Wencai Zhao
- College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, 266590 People’s Republic of China
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