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ABSTRACTSouth Korea’s National Immunization Program administers the quadrivalent influenza vaccine (QIV) to manage seasonal influenza, with a particular focus on the elderly. After reviewing the safety and immune response triggered by the adjuvanted QIV (aQIV) in individuals aged 65 and older, the Ministry of Food and Drug Safety in Korea approved its use. However, the extensive impact of aQIV on public health is yet to be fully understood. This study assessed the cost-effectiveness of replacing QIV with aQIV in South Korean adults aged 65 years and older. A dynamic transmission model, calibrated with national influenza data, was applied to compare the influence of aQIV and QIV on older adults and the broader population throughout a single influenza season. This study considered both the direct and indirect effects of vaccination on the elderly. We derived the incremental cost-effectiveness ratios (ICERs) from quality-adjusted life-years (QALYs) and costs incurred, validated through a probabilistic sensitivity analysis with 5,000 simulations. Findings suggest that transitioning to aQIV from QIV in the elderly would be cost-effective, particularly if aQIV’s efficacy reaches or exceeds 56.1%. With an ICER of $29,267/QALY, considerably lower than the $34,998/QALY willingness-to-pay threshold, aQIV presents as a cost-effective option. Thus, implementing aQIV with at least 56.1% efficacy is beneficial from both financial and public health perspectives in mitigating seasonal influenza in South Korea.

Objectives: This study aims to estimate the transmission potential of mpox in East Asia, focusing on the hardest hit nations: Taiwan, China, Japan, and South Korea. Methods: We utilized six phenomenological dynamic growth models to fit the case incidence during the initial 30 epidemic days. The best-fit model was selected to calculate the reproduction number (Rt). Additionally, we used the latest case data and a Bayesian framework to compute the instantaneous effective Rt by applying the Cori et al. method. Results: During the early phase, China demonstrated the highest estimated Rt of 2.89 (95% CI: 1.44-3.33); followed by South Korea, 2.18 (95% CI: 0.96-3.57); Japan, 1.73 (95% CI: 0.66-3.94); and Taiwan, 1.36 (95% CI: 0.71-3.30). However, by June 30, 2023, estimated Rt dropped below 1.00 in all countries: China at 0.05 (95% credible interval [CrI]: 0.02-0.10), Japan at 0.32 (95% CrI: 0.15-0.59), South Korea at 0.23 (95% CrI: 0.11-0.42), and Taiwan at 0.41 (95% CrI: 0.31-0.53), indicating the potential decline of the outbreak. Conclusions: Our analysis shows effective containment by each country. It is crucial to sustain effective management to ensure the ultimate eradication of the outbreak.

IntroductionWe evaluated the cost-effectiveness of South Korea’s planned annual coronavirus disease 2019 (COVID-19) booster campaign scheduled for October 2023.Materials and methodsAn age-structured mathematical model was used to analyze the public impacts and cost-effectiveness of vaccination across three vaccination strategies: uniform allocation and prioritizing those over 65 or those over 50 years old. We calculated the incremental cost per quality-adjusted life year (QALY) from both healthcare and societal perspectives. The maximum vaccine cost for cost-effectiveness was also identified.ResultsOur analysis highlights the cost-effectiveness of South Korea’s annual COVID-19 vaccination program in mitigating health and economic impacts. The most cost-effective strategy is uniform vaccine allocation, offering the lowest incremental cost-effectiveness ratio (ICER) at US$ 25,787/QALY. However, with a relatively high attack rate, the strategy prioritizing individuals over 65 years emerges as more cost-effective, lowering the ICER to US$ 13,785/QALY. Prioritizing those over 50 was less cost-effective. All strategies were cost-saving from a societal perspective, with cost-effectiveness being more sensitive to vaccine price than to its effectiveness.DiscussionOur results imply a potential strategy shift in current vaccination plan, with uniform vaccine distribution being more cost-effective than prioritizing older adults. Early estimation of viral transmissibility and vaccine effectiveness is crucial in determining the most cost-effective vaccine allocation approach.

Objectives: In South Korea, 13 745 cases of coronavirus disease (COVID-19) had been reported as of 19 July, 2020. To examine spatiotemporal changes in the transmission potential, we aimed to present regional estimates of the doubling time and reproduction number (Rt) for COVID-19 in the country. Methods: Daily series of confirmed COVID-19 cases in the most affected regions were extracted from publicly available sources. We employed established mathematical and statistical methods to investigate the time-varying reproduction numbers and doubling time for COVID-19 in Korea. Results: At the regional level, Seoul and Gyeonggi Province experienced the first peak of COVID-19 in early March, followed by a second wave in early June, withRt exceeding 3.0 and mean doubling time ranging from 3.6 to 10.1 days. As of 19 July, 2020, Gyeongbuk Province and Daegu had yet to experience a second wave of the disease. During the first wave, mean Rt for these areas reached 3.5–4.4, and doubling time ranged from 2.8 to 4.6 days. Conclusions: Our findings support the effectiveness of control measures against COVID-19 in Korea. However, the easing of restrictions that had been imposed by the government in May 2020 facilitated a second wave in the greater Seoul area.

Objectives: Since the first case of 2019 novel coronavirus (COVID-19) identified on Jan 20, 2020, in South Korea, the number of cases rapidly increased, resulting in 6284 cases including 42 deaths as of Mar 6, 2020. To examine the growth rate of the outbreak, we present the first study to report the reproduction number of COVID-19 in South Korea. Methods: The daily confirmed cases of COVID-19 in South Korea were extracted from publicly available sources. By using the empirical reporting delay distribution and simulating the generalized growth model, we estimated the effective reproduction number based on the discretized probability distribution of the generation interval. Results: We identified four major clusters and estimated the reproduction number at 1.5 (95% CI: 1.4–1.6). In addition, the intrinsic growth rate was estimated at 0.6 (95% CI: 0.6, 0.7), and the scaling of growth parameter was estimated at 0.8 (95% CI: 0.7, 0.8), indicating sub-exponential growth dynamics of COVID-19. The crude case fatality rate is higher among males (1.1%) compared to females (0.4%) and increases with older age. Conclusions: Our results indicate an early sustained transmission of COVID-19 in South Korea and support the implementation of social distancing measures to rapidly control the outbreak. Keywords: Coronavirus, COVID-19, Korea, Reproduction number

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt

BackgroundGiven the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction. ObjectiveThe aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods. MethodsWe used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics. ResultsGLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models. ConclusionsNAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Background: Broadly protective, long-lasting universal influenza vaccines are under development in response to low-moderate seasonal vaccine effectiveness, frequent genetic changes in circulating viruses and extended turnaround for vaccine manufacture. Because a long-lasting vaccine might be less effective than a seasonal vaccine that has been matched to current circulating strains, the public health impact of its introduction should be evaluated. Methods: A modified agent-based model (ABM) examined multi-year effects of a universal vaccine among 18 to 49-year-olds, given in Year 1 only. The proportion of vaccinated 18 to 49-year-olds who received universal vaccine was varied from 0% to 100%. Model parameters were drawn from US databases and the medical literature. Outcomes were 4-year cumulative and annual influenza cases as well as annual cases averted/100,000 population for 3 age groups, 0–17 years, 18–49 years and 50+ years. Results: In Year 1 when universal vaccine was given to 50% or 100% of all vaccinated 18 to 49-year-olds, more influenza cases occurred, compared to no universal vaccine, but fewer cases occurred in Years 2–4 as overall protection increased. Cumulative averted cases over 4 years in 18 to 49-year-olds were 892/100,000 and 1,687/100,000 population for the 50% and 100% universal vaccine for 18 to 49-year-olds scenarios, respectively, with additional benefits to children and older adults through indirect effects. Conclusions: In ABM, the universal vaccine with a conservative VE estimate given once to 18 to 49-year-olds reduced influenza cases among all age groups in Years 2–4 following its introduction. Reduced influenza burden may occur sooner if VE of universal vaccines exceeds that assumed in these models.

The dengue vaccine, CYD-TDV (Dengvaxia), has been licensed in 20 countries in Latin America and Southeast Asia beginning in 2015. In April 2018, the World Health Organization (WHO) advised that CYD-TDV should only be administered to individuals with a history of previous dengue virus infection. Using literature-based parameters, a mathematical model of dengue transmission and vaccination was developed to determine the optimal vaccination strategy while considering the effect of antibody-dependent enhancement (ADE). We computed the optimal vaccination rates under various vaccination costs and serological profiles. We observe that the optimal dengue vaccination rates for seropositive individuals are highest at the initial phase of a vaccination program, requiring intense effort at the early phase of an epidemic. The model shows that even in the presence of ADE, vaccination could reduce dengue incidence and provide population benefits. Specifically, optimal vaccination rates increase with a higher proportion of monotypic seropositive individuals, resulting in a higher impact of vaccination. Even in the presence of ADE and with limited vaccine efficacy, our work provides a population-level perspective on the potential merits of dengue vaccination.

Optimal control strategies for controlling seasonal influenza transmission in the US are of high interest, because of the significant epidemiological and economic burden of influenza. To evaluate optimal strategies of vaccination and social distancing, we used an age-structured dynamic model of seasonal influenza. We applied optimal control theory to identify the best way of reducing morbidity and mortality at a minimal cost. In combination with the Pontryagins maximum principle, we calculated time-dependent optimal policies of vaccination and social distancing to minimize the epidemiological and economic burden associated with seasonal influenza. We computed optimal age-specific intervention strategies and analyze them under various costs of interventions and disease transmissibility. Our results show that combined strategies have a stronger impact on the reduction of the final epidemic size. Our results also suggest that the optimal vaccination can be achieved by allocating most vaccines to preschool-age children (age under five) followed by young adults (age 20-39) and school age children (age 6-19). We find that the optimal vaccination rates for all age groups are highest at the beginning of the outbreak, requiring intense effort at the early phase of an epidemic. On the other hand, optimal social distancing of clinical cases tends to last the entire duration of an outbreak, and its intensity is relatively equal for all age groups. Furthermore, with higher transmissibility of the influenza virus (i.e. higher R0), the optimal control strategy needs to include more efforts to increase vaccination rates rather than efforts to encourage social distancing. Taken together, public health agencies need to consider both the transmissibility of the virus and ways to encourage early vaccination as well as voluntary social distancing of symptomatic cases in order to determine optimal intervention strategies against seasonal influenza.

BACKGROUND:The incidence of dengue fever (DF) is steadily increasing in Mexico, burdening health systems with consequent morbidities and mortalities. On December 9th, 2015, Mexico became the first country for which the dengue vaccine was approved for use. In anticipation of a vaccine rollout, analysis of the cost-effectiveness of the dengue vaccination program that quantifies the dynamics of disease transmission is essential. METHODS:We developed a dynamic transmission model of dengue in Yucatán, Mexico and its proposed vaccination program to incorporate herd immunity into our analysis of cost-effectiveness analysis. Our model also incorporates important characteristics of dengue epidemiology, such as clinical cross-immunity and susceptibility enhancement upon secondary infection. Using our model, we evaluated the cost-effectiveness and economic impact of an imperfect dengue vaccine in Yucatán, Mexico. CONCLUSIONS:Our study indicates that a dengue vaccination program would prevent 90% of cases of symptomatic DF incidence as well as 90% of dengue hemorrhagic fever (DHF) incidence and dengue-related deaths annually. We conclude that a dengue vaccine program in Yucatán, Mexico would be very cost-effective as long as the vaccination cost per individual is less than $140 and $214 from health care and societal perspectives, respectively. Furthermore, at an exemplary vaccination cost of $250 USD per individual on average, dengue vaccination is likely to be cost-effective 43% and 88% of the time from health care and societal perspectives, respectively.

Individuals who carry the sickle cell trait ($S$-gene) have a greatly reduced risk of experiencing symptomatic malaria infections. However, previous studies suggestthat the sickle cell trait does not protect against acquiring asymptomatic malaria infections, although the proportion of symptomatic infections is up to $50\%$ in areas where malaria is endemic. To examine the differential impact of the sickle cell trait on symptomatic and asymptomatic malaria, we developed a mathematical model of malaria transmission that incorporates the evolutionary dynamics of $S$-gene frequency. Our model indicates that the fitness of sickle cell trait is likely to increase with the proportion of symptomatic malaria infections. Our model also shows that control efforts aimed at diminishing the burden of symptomatic malaria are not likely to eradicate malaria in endemic areas, due to the increase in the relative prevalence of asymptomatic infection, the reservoir of malaria. Furthermore, when the prevalence of symptomatic malaria is reduced, both the fitness and frequency of the $S$-gene may decrease. In turn, a decreased frequency of the $S$-gene may eventually increase the overall prevalence of both symptomatic and asymptomatic malaria. Therefore, the control of symptomatic malaria might result in evolutionary repercussions, despite short-term epidemiological benefits.

Limited production capacity and delays in vaccine development aremajor obstacles to vaccination programs that are designed tomitigate a pandemic influenza. In order to evaluate and compare theimpact of various vaccination strategies during a pandemic influenza, we developed an age/risk-structured model of influenzatransmission, and parameterized it with epidemiological data fromthe 2009 H1N1 influenza A pandemic. Our model predicts that theimpact of vaccination would be considerably diminished by delays invaccination and staggered vaccine supply. Nonetheless, prioritizing limited H1N1 vaccine toindividuals with a high risk of complications, followed byschool-age children, and then preschool-age children, would minimizean overall attack rate as well as hospitalizations anddeaths. This vaccination scheme would maximize the benefits ofvaccination by protecting the high-risk people directly, andgenerating indirect protection by vaccinating children who are mostlikely to transmit the disease.

Rubella is a highly contagious childhood disease that causesrelatively mild symptoms. However, rubella can result in severecongenital defects, known as congenital rubella syndrome (CRS), iftransmitted from a mother to a fetus. Consequently, women havehigher incentive to vaccinate against rubella than men do. Withinthe population vaccination reduces transmission but also increasesthe average age of infection and possibly the risk of CRS amongunvaccinated females. To evaluate how the balance among thesefactors results in optimal coverage of vaccination, we developed agame theoretic age-structured epidemiological model of rubellatransmission and vaccination. We found that high levels ofvaccination for both genders are most effective in maximizingaverage utility across the population by decreasing the risk of CRSand reducing transmission of rubella. By contrast, the demands forvaccines driven by self-interest among males and females are $0\%$and $100\%$ acceptance, respectively, if the cost of vaccination isrelatively low. Our results suggest that the rubella vaccination bymales that is likely to be achieved on voluntary basis withoutadditional incentives would have been far lower than the populationoptimum, if rubella vaccine were offered separately instead ofcombined with measles and mumps vaccination as the MMR vaccine.

The roles of immigration and vaccination on disease dynamics are explored in a simple setting that considers the possibility of conferred immunity. We focus on SIR and SIS models with a vaccinated class. Conditions for the existence of multiple endemic steady states and a fold bifurcation are discussed.

Early observations from countries that have introduced rotavirus vaccination suggest that there may be indirect protection for unvaccinated individuals, but it is unclear whether these benefits will extend to the long term. Transmission dynamic models have attempted to quantify the indirect protection that might be expected from rotavirus vaccination in developed countries, but results have varied. To better understand the magnitude and sources of variability in model projections, we undertook a comparative analysis of transmission dynamic models for rotavirus. We fit five models to reported rotavirus gastroenteritis (RVGE) data from England and Wales, and evaluated outcomes for short- and long-term vaccination effects. All of our models reproduced the important features of rotavirus epidemics in England and Wales. Models predicted that during the initial year after vaccine introduction, incidence of severe RVGE would be reduced 1.8-2.9 times more than expected from the direct effects of the vaccine alone (28-50% at 90% coverage), but over a 5-year period following vaccine introduction severe RVGE would be reduced only by 1.1-1.7 times more than expected from the direct effects (54-90% at 90% coverage). Projections for the long-term reduction of severe RVGE ranged from a 55% reduction at full coverage to elimination with at least 80% coverage. Our models predicted short-term reductions in the incidence of RVGE that exceeded estimates of the direct effects, consistent with observations from the United States and other countries. Some of the models predicted that the short-term indirect benefits may be offset by a partial shifting of the burden of RVGE to older unvaccinated individuals. Nonetheless, even when such a shift occurs, the overall reduction in severe RVGE is considerable. Discrepancies among model predictions reflect uncertainties about age variation in the risk and reporting of RVGE, and the duration of natural and vaccine-induced immunity, highlighting important questions for future research.

Over-prescribing of antibiotics is considered to result in increased morbidity and mortality from drug-resistant organisms. A resulting common wisdom is that it would be better for society if physicians would restrain their prescription of antibiotics. In this view, self-interest and societal interest are at odds, making antibiotic use a classic "tragedy of the commons".We developed two mathematical models of transmission of antibiotic resistance, featuring de novo development of resistance and transmission of resistant organisms. We analyzed the decision to prescribe antibiotics as a mathematical game, by analyzing individual incentives and community outcomes.A conflict of interest may indeed result, though not in all cases. Increased use of antibiotics by individuals benefits society under certain circumstances, despite the amplification of drug-resistant strains or organisms. In situations where increased use of antibiotics leads to less favorable outcomes for society, antibiotics may be harmful for the individual as well. For other scenarios, where a conflict between self-interest and society exists, restricting antibody use would benefit society. Thus, a case-by-case assessment of appropriate use of antibiotics may be warranted.

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior.

Keeping pandemic influenza at bay is a global health priority. Of particular concern is the continued spread of the influenza subtype H5N1 in avian populations and the increasing frequency of transmission to humans. To decrease this threat, mass culling is the principal strategy for eradicating influenza in avian populations. Although culling has a crucial short-term epidemiological benefit, evolutionary repercussions on reservoir hosts and on the viral population have not been considered.To explore the epidemiological and evolutionary repercussions of mass avian culling, we combine population genetics and epidemiological influenza dynamics in a mathematical model parameterized by clinical, epidemiological, and poultry data. We model the virulence level of influenza and the selection on a dominant allele that confers resistance against influenza [1, 2] in a poultry population. Our findings indicate that culling impedes the evolution of avian host resistance against influenza. On the pathogen side of the coevolutionary race between pathogen and host, culling selects for heightened virulence and transmissibility of influenza.Mass culling achieves a short-term benefit at the expense of long-term detriments: a more genetically susceptible host population, ultimately greater mortality, and elevated influenza virulence.