KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
The role of a vaccine booster for a fractional order model of the dynamic of COVID-19: a case study in Thailand
(2025-12-01)
Pongsumpun, Puntipa
;
Pongsumpun, Puntani
;
Tang, I. Ming
;
Lamwong, Jiraporn
This article addresses the critical need for understanding the dynamics of COVID-19 transmission and the role of booster vaccinations in managing the pandemic. Despite widespread vaccination efforts, the emergence of new variants and the waning of immunity over time necessitate more effective strategies. A fractional-order mathematical model using Caputo-Fabrizio derivatives was developed to analyze the impact of booster doses, symptomatic and asymptomatic infections, and quarantine measures. The model incorporates real epidemic data from Thailand and includes a sensitivity analysis of parameters influencing disease spread. Numerical results indicate that booster vaccinations significantly reduce transmission rates, and the model’s predictions align well with the observed data. The basic reproduction number was determined to evaluate disease control, showing that a sustained vaccination campaign, including booster doses, is essential to maintaining immunity and controlling future outbreaks. The findings underscore the importance of ongoing vaccination efforts and provide a robust framework for policymakers to design effective strategies for pandemic control.
Some of the metrics are blocked by your
consent settings
Assessing the impact of non-pharmaceutical interventions against COVID-19 on 64 notifiable infectious diseases in Australia: A Bayesian Structural Time Series model
(2025-03-01)
Haque, Shovanur
;
Lambert, Stephen B.
;
Mengersen, Kerrie
;
Barr, Ian G.
;
Wang, Liping
Background: Several studies have examined the effect of non-pharmaceutical interventions (NPIs) on COVID-19 and other infectious diseases in Australia and globally. However, to our knowledge none have sufficiently explored their impact on other infectious diseases with robust time series model. In this study, we aimed to use Bayesian Structural Time Series model (BSTS) to systematically assess the impact of NPIs on 64 National Notifiable Infectious Diseases (NNIDs) by conducting a comprehensive and comparative analysis across eight disease categories within each Australian state and territory, as well as nationally. Methods: Monthly data on 64 NNIDs from eight categories were obtained from the Australian National Notifiable Disease Surveillance System. The incidence rates for each infectious disease in 2020 were compared with the 2015–2019 average and then with the expected rates in 2020 using a BSTS model. The study investigated the causal effects of 2020 interventions and analysed the impact of government policy restrictions at the national level from January 2020 to December 2022. Results: During the COVID-19 pandemic interventions in Australia, there was a 38 % (95 % Credible Interval [CI] [9 %, 54 %]) overall relative reduction in incidence reported across all disease categories compared to the 2015–2019 average. Significant reductions were observed in bloodborne diseases: 20 % (95 % CI [10 %, 29 %]), respiratory diseases: 79 % (95 % CI [52 %, 91 %]), and zoonoses: 8 % (95 % CI [1 %, 17 %]). Conversely, vector-borne diseases increased by 9 % over the same period. Reductions and intervention effects varied by state and territory, with higher policy stringency linked to fewer cases for some diseases. Conclusions: COVID-19 NPIs also impacted the transmission of other infectious diseases, with varying effects across regions reflecting diverse outcomes in response strategies throughout Australia. The findings could inform public health strategies and provide scientific evidence to support the development of early warning systems for future disease outbreaks.
Some of the metrics are blocked by your
consent settings
Optimal Control Strategy of a Mathematical Model for the Fifth Wave of COVID-19 Outbreak (Omicron) in Thailand
(2024-01-01)
Lamwong, Jiraporn
;
Wongvanich, Napasool
;
Tang, I. Ming
;
Pongsumpun, Puntani
The world has been fighting against the COVID-19 Coronavirus which seems to be constantly mutating. The present wave of COVID-19 illness is caused by the Omicron variant of the coronavirus. The vaccines against the five variants (α, β, γ, δ, and ω) have been quickly developed using mRNA technology. The efficacy of the vaccine developed for one of the strains is not the same as the efficacy of the vaccine developed for the other strains. In this study, a mathematical model of the spread of COVID-19 was made by considering asymptomatic population, symptomatic population, two infected populations and quarantined population. An analysis of basic reproduction numbers was made using the next-generation matrix method. Global asymptotic stability analysis was made using the Lyapunov theory to measure stability, showing an equilibrium point’s stability, and examining the model with the fact of COVID-19 spread in Thailand. Moreover, an analysis of the sensitivity values of the basic reproduction numbers was made to verify the parameters affecting the spread. It was found that the most common parameter affecting the spread was the initial number in the population. Optimal control problems and social distancing strategies in conjunction with mask-wearing and vaccination control strategies were determined to find strategies to give better control of the spread of disease. Lagrangian and Hamiltonian functions were employed to determine the objective function. Pontryagin’s maximum principle was employed to verify the existence of the optimal control. According to the study, the use of social distancing in conjunction with mask-wearing and vaccination control strategies was able to achieve optimal control rather than controlling just one or another.
Some of the metrics are blocked by your
consent settings
Vaccination’s Role in Combating the Omicron Variant Outbreak in Thailand: An Optimal Control Approach
(2022-10-01)
Lamwong, Jiraporn
;
Pongsumpun, Puntani
;
Tang, I. Ming
;
Wongvanich, Napasool
COVID-19 is the name of the new infectious disease which has reached the pandemic stage and is named after the coronavirus (COVs) which causes it. COV is a single-stranded RNA virus which in humans leads to respiratory tract symptoms which can lead to death in those with low immunities, particularly older people. In this study, a standard dynamic model for COVID-19 was proposed by comparing a simple model and the optimal control model to reduce the number of infected people and become a guideline to control the outbreak. Control strategies are the vaccination rate and vaccine-induced immunity. An analysis was performed to find an equilibrium point, the basic reproduction number (Formula presented.), and conditions that generate stability by using Lyapunov functions to prove the stability of the solution at the equilibrium point. Pontryagin’s maximum principle was used to find the optimal control condition. Moreover, sensitivity analysis of the parameters was performed to learn about the parameters that might affect the outbreak in order to be able to control the outbreak. According to the analysis, it is seen that the efficacy of vaccines (Formula presented.) and the infection rate (Formula presented.) will affect the increased (decreased) incidence of the outbreak. Numerical analyses were performed on the Omicron variant outbreak data collected from the Thailand Ministry of Health, whose analyses then indicated that the optimal control strategy could lead to planning management and policy setting to control the COVID-19 outbreak.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results