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Pandey HR, Phaijoo GR, Gurung DB. Dengue dynamics in Nepal: A Caputo fractional model with optimal control strategies. Heliyon 2024; 10:e33822. [PMID: 39670225 PMCID: PMC11637085 DOI: 10.1016/j.heliyon.2024.e33822] [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: 04/04/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 12/14/2024] Open
Abstract
An infectious disease called dengue is a significant health concern nowadays. The dengue outbreak occurred with a single serotype all over Nepal in 2023. In the tropical and subtropical regions, dengue fever is a leading cause of sickness and death. Currently, there is no specified treatment for dengue fever. Avoiding mosquito bites is strongly advised to reduce the likelihood of controlling this disease. In underdeveloped countries like Nepal, the implementation of appropriate control measures is the most important factor in preventing and controlling the spread of dengue illness. The Caputo fractional dengue model with optimum control variables, including mosquito repellent and insecticide use, investigates the impact of alternative control strategies to minimize dengue prevalence. Using the fixed point theorem, the existence and uniqueness of a solution will be demonstrated for the problem. Ulam-Hyers stability, disease-free equilibrium point stability, and basic reproduction number are studied for the proposed model. The model is simulated using a two-step Lagrange interpolation technique, and the least squares method is used to estimate parameter values using real monthly infected data. We then analyze the sensitivity analysis to determine influencing parameters and the control measure effects on the basic reproduction number. The Pontryagin Maximum Principle is used to determine the optimal control variable in the dengue model for control strategies. The present study suggests that the deployment of control measures is extremely successful in lowering infectious disease incidences. Which facilitates the decision-makers to practice rigorous evaluation of such an epidemiological scenario while implementing appropriate control measures to prevent dengue disease transmission in Nepal.
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Affiliation(s)
- Hem Raj Pandey
- School of Engineering, Faculty of Science and Technology, Pokhara University, Nepal
- Department of Mathematics, School of Science, Kathmandu University, Nepal
| | - Ganga Ram Phaijoo
- Department of Mathematics, School of Science, Kathmandu University, Nepal
| | - Dil Bahadur Gurung
- Department of Mathematics, School of Science, Kathmandu University, Nepal
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Zhang M, Huang JF, Kang M, Liu XC, Lin HY, Zhao ZY, Ye GQ, Lin SN, Rui J, Xu JW, Zhu YZ, Wang Y, Yang M, Tang SX, Cheng Q, Chen TM. Epidemiological Characteristics and the Dynamic Transmission Model of Dengue Fever in Zhanjiang City, Guangdong Province in 2018. Trop Med Infect Dis 2022; 7:tropicalmed7090209. [PMID: 36136620 PMCID: PMC9501079 DOI: 10.3390/tropicalmed7090209] [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: 07/22/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: With the progress of urbanization, the mobility of people has gradually increased, which has led to the further spread of dengue fever. This study evaluated the transmissibility of dengue fever within districts and between different districts in Zhanjiang City to provide corresponding advice for cross-regional prevention and control. Methods: A mathematical model of transmission dynamics was developed to explore the transmissibility of the disease and to compare that between different regions. Results: A total of 467 DF cases (6.38 per 100,000 people) were reported in Zhanjiang City in 2018. In the model, without any intervention, the number of simulated cases in this epidemic reached about 950. The dengue fever transmissions between districts varied within and between regions. When the spread of dengue fever from Chikan Districts to other districts was cut off, the number of cases in other districts dropped significantly or even to zero. When the density of mosquitoes in Xiashan District was controlled, the dengue fever epidemic in Xiashan District was found to be significantly alleviated. Conclusions: When there is a dengue outbreak, timely measures can effectively control it from developing into an epidemic. Different prevention and control measures in different districts could efficiently reduce the risk of disease transmission.
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Affiliation(s)
- Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jie-Feng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
- School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Xing-Chun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Hong-Yan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Ze-Yu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Guo-Qiang Ye
- Zhanjiang Municipal Center for Disease Control and Prevention, Zhanjiang 524037, China
| | - Sheng-Nan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jing-Wen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yuan-Zhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Shi-Xing Tang
- School of Public Health, Southern Medical University, Guangzhou 510515, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Qu Cheng
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA 94704, USA
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Tian-Mu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
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