Mathematical modeling to study the interactions of two risk populations in COVID-19 spread in Thailand

被引:1
|
作者
Ritraksa, Siriprapa [1 ]
Photphanloet, Chadaphim [1 ]
Shuaib, Sherif Eneye [2 ]
Intarasit, Arthit [1 ]
Riyapan, Pakwan [1 ]
机构
[1] Prince Songkla Univ, Fac Sci & Technol, Dept Math & Comp Sci, Pattani Campus, Pattani 94000, Thailand
[2] York Univ, Dept Math & Stat, 4700 Keele St, Toronto, ON M3J 1P3, Canada
来源
AIMS MATHEMATICS | 2022年 / 8卷 / 01期
关键词
COVID-19; disease; effective reproduction number; mathematical model; preventive measures; risk population; vaccination;
D O I
10.3934/math.2023105
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The use of vaccines has always been controversial. Individuals in society may have different opinions about the benefits of vaccines. As a result, some people decide to get vaccinated, while others decide otherwise. The conflicting opinions about vaccinations have a significant impact on the spread of a disease and the dynamics of an epidemic. This study proposes a mathematical model of COVID-19 to understand the interactions of two populations: the low risk population and the high risk population, with two preventive measures. Unvaccinated individuals with chronic diseases are classified as high risk population while the rest are a low risk population. Preventive measures used by low risk group include vaccination (pharmaceutical way), while for the high risk population they include wearing masks, social distancing and regular hand washing (non-pharmaceutical ways). The susceptible and infected sub-populations in both the low risk and the high risk groups were studied in detail through calculations of the effective reproduction number, model analysis, and numerical simulations. Our results show that the introduction of vaccination in the low risk population will significantly reduce infections in both subgroups.
引用
收藏
页码:2044 / 2061
页数:18
相关论文
共 50 条
  • [1] Mathematical Modeling for Spread and Control of COVID-19
    Yang B.
    Yu Z.
    Cai Y.
    [J]. Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2021, 55 (11): : 162 - 172
  • [2] Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study
    Goldsztejn, Uri
    Schwartzman, David
    Nehorai, Arye
    [J]. PLOS ONE, 2020, 15 (12):
  • [3] Challenging the spread of COVID-19 in Thailand
    Tantrakarnapa, Kraichat
    Bhopdhornangkul, Bhophkrit
    [J]. ONE HEALTH, 2020, 11
  • [4] A study on the spread of COVID 19 outbreak by using mathematical modeling
    Mishra, Jyoti
    [J]. RESULTS IN PHYSICS, 2020, 19
  • [5] System identification and mathematical modeling of the pandemic spread COVID-19 in Serbia
    Sajic, Jasmina Lozanovic
    Langthaler, Sonja
    Schroettner, Joerg
    Baumgartner, Christian
    [J]. IFAC PAPERSONLINE, 2022, 55 (04): : 19 - 24
  • [6] A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
    Johnston, Matthew D.
    Pell, Bruce
    Nelson, Patrick
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (03):
  • [7] Modeling the COVID-19 spread, a case study of Egypt
    Assem S. Deif
    Sahar A. El-Naggar
    [J]. Journal of the Egyptian Mathematical Society, 29 (1)
  • [8] Spread and control of COVID-19: A mathematical model
    Misra, O. P.
    Sisodiya, Omprakash Singh
    [J]. INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING, 2022, 13 (03)
  • [9] Covid-19 SEIQR Spread Mathematical Model
    Akman, Caglar
    Demir, Okan
    Sonmez, Tolga
    [J]. 29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021), 2021,
  • [10] Modeling the Spread and Control of COVID-19
    Trivedi, Ashutosh
    Sreenivas, Nanda Kishore
    Rao, Shrisha
    [J]. SYSTEMS, 2021, 9 (03):