Potential Public Health Impact of Updated COVID-19 Vaccination Strategies in Thailand: Epidemiological Data Update

Abstract

Introduction

This study evaluates the anticipated health and economic effects of multiple COVID-19 vaccination strategies using an updated vaccine in Thailand.

Methods

A previously published hybrid decision tree and Markov model, originally developed for the USA, was calibrated using Thailand-specific epidemiological, demographic, and economic data from 2024. The model assessed several age- and risk-based vaccination strategies assuming vaccine uptake ranging from 20% to 50%. Health outcomes (cases, hospitalizations, deaths, and long COVID cases) and economic outcomes (long COVID costs, direct medical costs, and productivity losses) were projected from payer and societal perspectives. Vaccine effectiveness was assumed to be 50% against infection, 60% against symptoms, and 70% against severe disease, with a 6-month duration of protection.

Results

Vaccinating individuals aged 60 years and above and high-risk individuals aged 6 months to 59 years was projected to prevent 318,700 infections, 9147 hospitalizations, and 1061 deaths in 1 year. This strategy was estimated to yield THB 3300 million in direct medical cost savings and THB 2695 million in productivity loss savings. Increasing coverage in this population to 50% could amplify these reductions by up to 150%.

Conclusions

With updated Thai data, analyses suggest that use of an adapted COVID-19 vaccine could continue to generate considerable public-health and economic gains, particularly when coverage among older adults and high-risk groups is expanded. These findings carry implications for sustaining preparedness and guiding national vaccination policy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41030-026-00358-x.

Key Summary Points

Why carry out this study?

COVID-19 continues to be a public health concern, but as it evolves, there is an ongoing need to reassess the effectiveness and value of vaccination programs to support public health decision-making.

This study used a decision-analytic model to project the public health and economic impact of alternative COVID-19 vaccination strategies in Thailand.

What was learned from this study?

In the base case, where the population eligible for vaccination was restricted to individuals aged 60 years and above and high-risk individuals aged 6 months to 59 years, vaccination was projected to lead to 318,700 infections averted, 9147 hospitalizations averted, 1061 deaths averted, THB 3300 million in direct medical cost savings, and THB 2695 million in productivity loss savings.

Updated modeling analysis based on recent Thai epidemiologic data suggests that vaccination strategies using an adapted COVID-19 vaccine would have substantial public health and economic benefits.

Introduction

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to impose meaningful public health and economic challenges worldwide. As of December 22, 2024, more than 777 million cases of COVID-19 and over 7 million deaths have been reported worldwide. In Thailand, as of April 2024, nearly 5 million cases and over 34,000 deaths have been recorded [1].

Thailand experienced several distinct waves of COVID-19 since 2020. Early surges were tied to the Alpha variant, followed by a prolonged Delta wave that peaked in mid-2021. The Omicron variant subsequently displaced earlier strains, which by early 2022 [2–4] accounted for all reported COVID-19 cases in Thailand [5].

Thailand’s national vaccination campaign began in early 2021 and led to substantial primary series coverage. By December 2022, 77.6% of the population had received two vaccine doses, 38.5% had received a third dose, and 9.4% had received four or more doses of monovalent vaccines [6]. Despite this progress, uptake of subsequent doses has remained relatively low. In 2026, the Thailand Ministry of Public Health recommends that individuals at high risk of severe disease receive annual vaccination for COVID-19. This includes individuals aged ≥ 60 years, individuals aged ≤ 60 years that have a comorbidity that puts them at high risk of severe COVID-19 (e.g., chronic respiratory diseases, cardiovascular disease, chronic kidney disease, stroke, diabetes, and immunocompromising conditions), pregnant women, healthcare workers, and caregivers of vulnerable persons [7].

A recent study evaluating the public health and economic impact of using an Omicron-adapted bivalent booster vaccine in Thailand demonstrated that vaccinations for elderly and high-risk populations alone could prevent 97,596 cases, 36,578 hospitalizations, and 903 deaths, while reducing direct medical costs by THB 3119 million. These benefits were further amplified by increasing vaccine coverage [8].

Although the COVID-19 is no longer classified as a global public health emergency by the World Health Organization, the disease continues to circulate globally with recurrent waves driven by viral evolution and waning immunity. Therefore, the disease still demands strategic management to mitigate future public health and economic impacts [9]. Experience with other endemic respiratory pathogens such as influenza demonstrates that sustained transmission can continue to generate clinically and economically meaningful impact, even outside of emergency settings. In this context, ongoing evaluation of prevention strategies is important to characterize potential health and resource implications under contemporary epidemiologic conditions. Such analyses support evidence-based decision-making as countries transition from acute pandemic response to longer-term management frameworks.

This study aimed to evaluate the impact of evolving epidemiological trends on COVID-19 vaccination strategies in Thailand. Building on the work of Thakkar et al., 2023 [8] this analysis incorporated updated epidemiological data, demographic profiles, vaccine uptake rates, cost inputs, and revised age-based eligibility for vaccination. The objective is to provide an up-to-date assessment of the public health and economic impact of vaccination strategies to support informed decision-making for future public health policies in Thailand.

Methods

Model Overview

A previously published combined decision tree–Markov model [10] was applied to estimate the public health impact of vaccination with the COVID-19 vaccine versus no vaccination in Thailand (Fig. 1). This model, originally developed for a US population, was adapted for Thailand in a 2023 study [8] and has been updated in the current analysis to integrate 2024 epidemiological, demographic, and vaccination data.

Fig. 1.

Model structure: a Markov component and b decision tree component

In this model, individuals aged 6 months and older either received no vaccination or a single dose of the COVID-19 vaccine. Health outcomes were tracked over a 1-year period. The model followed a susceptible-infected-recovered (SIR) structure to classify individuals on the basis of their immunity status and used age-dependent probabilities to guide transitions between health states. Immunity, whether derived from infection or vaccination, was assumed to wane over time, resulting in a return to susceptibility. Age-adjusted attack rates determined transitions from the susceptible to the infected state. For vaccinated individuals, the probability of infection was reduced on the basis of vaccine effectiveness. Infection was modeled with a decisions tree that included a range of COVID-19 outcomes, including symptomatic infection, outpatient care, hospitalization, ICU admission, mechanical ventilation, and long COVID.

The model was developed in Microsoft Excel and was constructed in accordance with guidelines from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM). All model inputs and outputs were reviewed by internal experts for face validity, and independent reviewers assessed the model’s structure, assumptions, and results to confirm internal consistency and technical validity.

Model Inputs

Model inputs that have been updated from the initial study [8] are documented below. Model inputs were sourced from public health surveillance data and published literature and were validated by subject matter experts.

Population Inputs

The age-specific distribution of the population derived from the National Statistical Office Thailand [11] has been updated to 2022 data. The population was stratified into eight age groups as follows: 6 months–4 years, 5–11 years, 12–17 years, 18–29 years, 30–54 years, 55–64 years, 65–74 years, and ≥ 75 years. To assess the impact of vaccination across different risk groups, the population was further categorized into two risk groups. The standard-risk population included individuals aged < 60 years with no comorbid conditions, while the high-risk population comprised individuals aged ≥ 60 years and those with at least one comorbid condition (Supplementary Table 1).

The high-risk subpopulation was updated from the previous analysis using estimates from Clark et al., 2020 [12], which provided global, regional, and national estimates of populations at increased risk of severe COVID-19 due to underlying health conditions (Supplementary Table 1).

Infection Inputs

At the start of the model horizon, it was assumed that 100% of the population was susceptible to COVID-19, based on the assumption that protection from prior infection or vaccination wanes before the beginning of the following season [13]. The 2024 yearly attack rate among the susceptible population was informed by data from the Ministry of Public Health of Thailand (Supplementary Table 2) [14].

Since the conclusion of the global public health emergency in May 2023, routine surveillance and testing for infections have significantly narrowed, resulting in numerous undetected SARS-CoV-2 cases and a notable rise in underreporting. A 2025 study by Mustapha et al. [15] estimated underreporting rates for 27 countries by comparing reported infection rates with modeled estimates of daily and cumulative SARS-CoV-2 infections through December 11, 2022. This analysis used national databases and seroprevalence surveys, adjusted for reporting biases and waning immunity [15]. For Thailand, the underreporting factor was estimated at 22.7, based on the median rate for East and Southeast Asia during the Omicron BA.4/BA.5 variant period [15]. Reported attack rates for COVID-19 infections were adjusted using this factor.

Vaccine Inputs

Individuals in Thailand aged ≥ 6 months were considered eligible to receive a COVID-19 vaccine on the basis of age- and risk-specific strategies. The coverage of vaccination among the eligible population was assumed to be 20%, with no differentiation in coverage between age or risk groups (Supplementary Table 12).

The model used a vaccine effectiveness profile based on assumptions and real-world evidence [16–21]. Vaccine effectiveness was assumed to be 50% against infection, 60% against symptomatic infection, and 70% against severe disease. The duration of vaccine-induced protection was assumed to be 6 months, with vaccine effectiveness waning at a rate of 1/duration of protection per month.

Health Inputs

The probabilities of hospitalization, ICU admission, and mortality were informed by data from the Ministry of Public Health of Thailand for the period from October 2022 to October 2023. As data on mechanical ventilation rates were not available for Thailand, these were assumed to remain the same as those used in Thakkar et al., 2023 [8] (Supplementary Tables 3–5).

As a result of the lack of detailed long COVID data specific to Thailand (defined as symptoms lasting longer than 3 months), probabilities were derived from a study conducted in Singapore. A base probability for long COVID of 16.5% was used and adjusted using odds ratios based on vaccination status and hospitalization [22]. Since age-specific data were unavailable, the same long COVID rate was applied across all age groups. Asymptomatic individuals were assumed to have the same probability of developing long COVID as outpatients. (Supplementary Table 6).

Cost Inputs

The costs used in the model were updated from the values used in the previously published version [8] by inflating to 2024 prices using historical consumer price index data for Thailand [23]. This included direct medical costs associated with COVID-19, such as hospitalization, outpatient visits, GP visits, over-the-counter medication, testing, and long COVID management, as well as indirect costs related to productivity losses from illness and premature mortality (Supplementary Tables 7–11) [24].

In addition to productivity losses, the economic burden of school absenteeism due to COVID-19 illness was included in the model. In contrast to the earlier approach in Thakkar et al., 2023 [8], which applied age-specific education costs, this update used an average daily cost of 17.78 THB, based on updated World Bank estimates [25]. The number of school days lost was assumed to match the number of workdays lost (Supplementary Table 13).

Model Outcomes

The model applied a Thailand payer perspective and incorporated societal impact through the inclusion of indirect costs. It estimated health outcomes such as COVID-19 cases, hospitalizations, outpatient visits, and deaths, along with economic outcomes including COVID-19-related direct medical costs and productivity losses.

Although the model simulated outcomes over a 1-year time horizon, it also captured productivity loss due to premature mortality as a lump-sum societal cost per COVID-19-related death. In alignment with Thailand’s economic evaluation guidelines [26] and prior local cost-effectiveness research [27], a 3% annual discount rate was applied to these lifetime productivity losses.

Analysis

We evaluated the health and economic benefits of increasing COVID-19 vaccination coverage using an adapted vaccine in the Thai population. The analysis assessed the impact of targeted vaccination strategies across different age and risk groups.

The base-case analysis included five vaccination strategies targeting the following populations:

• Individuals aged ≥ 60 years and high-risk individuals aged 6 months to 59 years

• Individuals aged ≥ 60 years

• Individuals aged ≥ 50 years and high-risk individuals aged 6 months to 49 years

• Individuals aged ≥ 18 years and high-risk individuals aged 6 months to 17 years

• General population aged ≥ 6 months

For each target group, the model estimated the incremental benefits of increasing vaccination coverage from 20% to 30%, 40%, and 50%, in terms of reductions in COVID-19-related infections, hospitalizations, deaths, long COVID costs, and both direct and indirect costs.

A scenario analysis was conducted to examine the impact of different vaccine waning assumptions. Three scenarios were explored:

• Scenario 2a Extended duration of vaccine protection to 12 months across all outcomes

• Scenario 2b Extended protection to 12 months for hospitalization only

• Scenario 2c Reduced duration of vaccine protection to 4 months across all outcomes

These analyses provide insights into optimal COVID-19 vaccination strategies in Thailand, particularly in the context of waning immunity, underreporting, and ongoing disease burden following the end of the public health emergency.

Model Validation

Model validation was conducted according to ISPOR-SMDM guidelines [28]. All inputs and outputs underwent validity checks to ensure the face validity of the model. Additionally, experts who were not involved in model development conducted internal validation by assessing the consistency and accuracy of the model equations, inputs, and outputs.

Ethics Statement

This study was based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Results

Base-Case Analysis and Expansion of Vaccination Eligibility to Additional Age and Risk Groups

This study assessed the impact of vaccination strategies by increasing coverage among different age and risk groups. In the model base case, only individuals aged ≥ 60 years and high-risk individuals aged 6 months to 59 years were eligible for vaccination. Under this assumption, the model projected vaccination preventing 318,700 infections, 9147 hospitalizations, and 1061 deaths. Additionally, vaccination would lead to THB 3300 million in direct medical cost savings and THB 2695 million in indirect cost savings (Table 1). When compared to a no-vaccination scenario, this strategy was estimated to reduce infections by 15%, hospitalizations by 24%, and deaths by 31%. Direct medical costs and indirect costs were reduced by 21% and 18%, respectively.

Table 1.

Alternative base case results and expanding vaccination in the standard-risk population

	60+ & HRa	60+	50+ & HR	18+ & HR	6 months+
Vaccine doses administered (million)	4.79	2.63	5.94	10.76	13.15
Deaths averted	1061	1030	1094	1116	1118
Hospitalizations averted	9147	6955	10,804	14,395	15,788
Infections averted	318,700	177,168	381,370	739,083	840,419
Long COVID costs averted (million THB)	54.8	30.7	63.5	129.4	143.3
Direct medical cost averted (million THB)	3299.9	1967.6	3933.3	7175.8	8111.0
Indirect costs averted (million THB)	2695.2	1017.8	3417.8	7328.8	7360.8

THB Thai baht, HR high-risk

^aBase case

Expanding vaccination eligibility to include younger age groups in the standard-risk population led to an increase in the projected benefits of vaccination. For example, expanding eligibility to all individuals aged ≥ 50 years and high-risk individuals aged 6 months to 49 years led to an increases in the projected deaths averted of 11%, hospitalizations averted of 14%, and costs averted of 14%. Expanding eligibility to all individuals aged ≥ 18 years and high-risk individuals aged 6 months to 17 years led to an increases in the projected deaths averted of 17%, hospitalizations averted of 88%, and costs averted of 155% (Table 1).

Increasing Vaccination Coverage

The study also evaluated increasing assumptions around vaccination coverage in the base case where all individuals aged ≥ 60 years or high-risk individuals aged 6 months to 59 years were eligible for vaccination. Compared to the base case assumption of 20% coverage, increasing vaccine coverage to 30%, 40%, and 50% was projected to increase the public health benefits of vaccination (i.e., reductions in deaths, hospitalizations, infections, and costs) by 50%, 100%, and 150%, respectively (Table 2).

Table 2.

Scenario analysis results: increased coverage in those aged ≥ 60 years or high risk aged 6 months to 59 years

Coverage	20%a	30%	40%	50%
Vaccine doses administered (million)	4.8	7.2 (50%)	9.6 (100%)	12.0 (150%)
Deaths averted	1060.9	1591.4 (50%)	2121.8 (100%)	2652.3 (150%)
Hospitalizations averted	9147	13,720 (50%)	18,294 (100%)	22,867 (150%)
Infections averted	318,700	478,050 (50%)	637,400 (100%)	796,749 (150%)
Long COVID costs averted (million THB)	54.75	82.1 (50%)	109.5 (100%)	136.9 (150%)
Direct medical cost averted (million THB)	3300	4950 (50%)	6600 (100%)	8250 (150%)
Indirect costs averted (million THB)	2695	4043 (50%)	5390 (100%)	6738 (150%)

Percentages in parentheses represent relative changes compared to the 20% base case

THB Thai baht

^aBase case

Scenario Analysis: Alternative Vaccine Effectiveness Waning Assumptions

In the scenario where the durations of protection of vaccine effectiveness against infection and hospitalization were extended from 6 months (base case assumption) to 12 months, the projected public health impact of vaccination increased substantially (43% for infections averted, 42% for hospitalizations averted, 40% for deaths averted, and 43% for costs averted) (Table 3). In the scenario where only the duration of protection of vaccine effectiveness against hospitalization was extended to 12 months and the duration of protection of vaccine effectiveness against infection was kept at 6 months, the impact on projected infections averted remained the same but projected hospitalizations averted, deaths averted, and costs averted increased by 43%, 35%, and 18%, respectively (Table 3). In the scenario where the durations of protection of vaccine effectiveness against infection and hospitalization were reduced from 6 months (base case assumption) to 4 months, the projected public health impact of vaccination decrease (25% for infections averted, 25% for hospitalizations averted, 24% for deaths averted, and 25% for costs averted) (Table 3).

Table 3.

Scenario analysis results: alternative waning assumptions in the analysis of those aged ≥ 60 years or high risk aged 6 months to 59 years

Coverage	Base case	Base case VE	Adjusted VE	Base case VE
	DoP = 6 months	DoP = 12 months	DoP = 12 months	DoP = 4 months
Vaccine doses administered (million)	4.8	4.8 (0%)	4.8 (0%)	4.8 (0%)
Deaths averted	1061	1486.5 (40%)	1129.3 (6%)	804 (− 24%)
Hospitalizations averted	9147	12,985 (42%)	12,935 (41%)	6874 (− 25%)
Infections averted	318,700	453,220 (42%)	312,713 (− 2%)	239,186 (− 25%)
Long COVID costs averted (million THB)	54.8	74.5 (36%)	377.4 (589%)	42.4 (− 23%)
Direct medical cost averted (million THB)	3299.9	4700 (42%)	3114 (− 6%)	2473.6 (− 25%)
Indirect costs averted (million THB)	2695.2	3839 (42%)	2818 (5%)	2068.9 (− 23%)

Percentages in parentheses represent relative changes compared to the base case DoP = 6 months

THB Thai baht, DoP duration of protection, VE vaccine effectiveness

Discussion

As a result of the evolving dynamics of COVID-19, vaccines adapted to the latest circulating strains remain a key public health strategy. The results of this study illustrate that, considering updated Thai epidemiological data, annual vaccination with an adapted vaccine can have a meaningful public health and economic impact in Thailand, particularly among older adults (i.e., 50+) and individuals at high risk of severe COVID-19 outcomes.

An overview of the results shows that a targeted vaccination strategy covering individuals aged ≥ 60 years and high-risk individuals aged 6 months to 59 years, at 20% coverage, could avert approximately 318,700 infections, 9147 hospitalizations, and 1061 deaths. This translates into THB 3300 million in direct medical cost savings and THB 2695 million in indirect cost savings. Expanding vaccine coverage from 20% to 50% among these groups could reduce disease burden by up to 150%, and widening eligibility to younger age groups could yield even greater health and economic benefits. Scenario analyses also demonstrated that longer vaccine protection durations further enhanced the projected public health and economic benefits.

Compared with the previously published analysis by Thakkar et al. (2023) [8], the current findings reflect the influence of updated inputs, including updated epidemiological trends, updated Thai demographic data, modifications to the target age and risk groups, and notably changes in vaccine coverage. These updates help contextualize the effectiveness of current vaccination strategies in Thailand’s evolving landscape.

While the modeled coverage scenarios (20% to 50%) demonstrate the potential range of clinical and economic outcomes from the vaccination program, real-world coverage will depend on a range of implementation factors including vaccine hesitancy, reduced perceived risk, and operational factors such as procurement planning, healthcare workforce constraints, and alignment with seasonal respiratory vaccination campaigns. Implementation feasibility has direct implications for public health and economic impact. Lower-than-anticipated coverage may reduce total program costs but also diminish projected health gains, thereby affecting overall value. Conversely, efforts to increase uptake (e.g., expanded outreach, communication campaigns, or delivery through alternative platforms) may require additional upfront investment but will improve impact and possibly improve cost-effectiveness, especially if concentrated among higher-risk populations. Therefore, these estimates should be interpreted within the context of real-world delivery constraints, and coverage assumptions should be aligned with achievable implementation strategies when informing policy decisions.

This study has several limitations. First, the model utilizes a static decision tree Markov structure rather than a dynamic transmission model. While dynamic models are often used to capture herd effects in infectious disease modeling, static models are widely accepted and commonly used for vaccine evaluations, particularly from a payer perspective. This simpler framework, using surveillance-derived attack rates, was considered appropriate given uncertainties in indirect transmission effects and reinfection dynamics. However, it may underestimate the full impact of vaccination by excluding indirect benefits such as herd immunity.

Second, although this model was customized to reflect Thailand’s population and healthcare system, several inputs, such as costs related to AEs and healthcare resource use for long COVID, were informed by international data because of limited availability of local sources. AE costs were based on US data and converted to local currency, potentially overestimating their economic burden. In addition, estimating the cost of managing long COVID remains challenging, as treatment often addresses acute symptoms without a specific long COVID diagnosis, possibly underestimating resource utilization.

Third, ICU admission and mechanical ventilation probabilities were not drawn from the same time period as hospitalization rates, which may introduce slight inconsistencies in severity modeling. The model was populated with the most recent data available at the time of the study, but this can be challenging as governments stop collecting surveillance data. Infection data came from 2024, but the latest available hospitalization data was from 2022–2023. Therefore, results reflect shifts in infection rates, which will also lead to a decrease in hospitalizations, but it was assumed that the probability of hospitalization conditional on infection remained the same as in 2022–2023. This emphasizes the importance of continued support for public health surveillance. Additionally, the results are sensitive to assumptions around vaccine effectiveness, duration of protection, and the underlying burden of disease, which are subject to change as new variants emerge and immunity profiles shift. Results are not generalizable to other countries, but in future research, the model can be adapted to other countries using local population, epidemiological, and cost data.

Despite these limitations, the findings provide strong evidence that annual vaccination strategies using adapted vaccines in Thailand can significantly reduce the national disease burden, alleviate pressure on healthcare systems, and generate substantial economic savings. As the virus continues to evolve, updated vaccines may be required, and similar benefits may be observed depending on future efficacy and durability profiles. Policymakers should consider expanding vaccine coverage among standard-risk populations to further decrease infections, hospitalizations, direct medical costs, and productivity losses. Proactive vaccination strategies can help Thailand manage future COVID-19 waves while optimizing both public health and economic outcomes.

Conclusion

When considering updated epidemiological and demographic data for Thailand, the introduction and expansion of a vaccination campaign using an adapted COVID-19 vaccine is projected to deliver meaningful public health and economic benefits, particularly when targeting older adults and high-risk individuals, with additional gains observed when coverage increased. The results of these analyses provide updated evidence that can help decision makers refine and optimize COVID-19 vaccination strategies in Thailand.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

Karan Thakkar, Ruangwit Thamaree, Moe H. Kyaw, Iustina Chirila, Carlos Fernando Mendoza, Josie Dodd, Ben Yarnoff, and Sasisopin Kiertiburanakul all meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article. Karan Thakkar, Ruangwit Thamaree, Moe H. Kyaw, Iustina Chirila, Carlos Fernando Mendoza, Josie Dodd, Ben Yarnoff, and Sasisopin Kiertiburanakul all authors contributed to study conception and design, data acquisition, analysis, and interpretation, drafting and revising of the manuscript.

Funding

This study was sponsored by Pfizer. Pfizer is also funding the journal’s Rapid Service Fees.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Declarations

Conflict of Interest

Karan Thakkar, Ruangwit Thamaree, Moe H Kyaw, Iustina Chirila, and Carlos Fernando Mendoza are employees of Pfizer and may hold stock or stock options of Pfizer. Ben Yarnoff and Josie Dodd are employees of PPD Evidera Health Economics & Market Access, Thermo Fisher Scientific, which received financial support from Pfizer in connection with the study and the development of this manuscript. Sasisopin Kiertiburanakul has no conflicts of interest.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.