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Background The Middle East respiratory syndrome (MERS) coronavirus has caused recurrent outbreaks in the Arabian Peninsula since 2012. Although MERS has low overall human-to-human transmission potential, there is occasional amplification in the healthcare setting, a pattern reminiscent of the dynamics of the severe acute respiratory syndrome (SARS) outbreaks in 2003. Here we provide a head-to-head comparison of exposure patterns and transmission dynamics of large hospital clusters of MERS and SARS, including the most recent South Korean outbreak of MERS in 2015. Methods To assess the unexpected nature of the recent South Korean nosocomial outbreak of MERS and estimate the probability of future large hospital clusters, we compared exposure and transmission patterns for previously reported hospital clusters of MERS and SARS, based on individual-level data and transmission tree information. We carried out simulations of nosocomial outbreaks of MERS and SARS using branching process models rooted in transmission tree data, and inferred the probability and characteristics of large outbreaks. Results A significant fraction of MERS cases were linked to the healthcare setting, ranging from 43.5 % for the nosocomial outbreak in Jeddah, Saudi Arabia, in 2014 to 100 % for both the outbreak in Al-Hasa, Saudi Arabia, in 2013 and the outbreak in South Korea in 2015. Both MERS and SARS nosocomial outbreaks are characterized by early nosocomial super-spreading events, with the reproduction number dropping below 1 within three to five disease generations. There was a systematic difference in the exposure patterns of MERS and SARS: a majority of MERS cases occurred among patients who sought care in the same facilities as the index case, whereas there was a greater concentration of SARS cases among healthcare workers throughout the outbreak. Exposure patterns differed slightly by disease generation, however, especially for SARS. Moreover, the distributions of secondary cases per single primary case varied highly across individual hospital outbreaks (Kruskal–Wallis test; P < 0.0001), with significantly higher transmission heterogeneity in the distribution of secondary cases for MERS than SARS. Simulations indicate a 2-fold higher probability of occurrence of large outbreaks (>100 cases) for SARS than MERS (2 % versus 1 %); however, owing to higher transmission heterogeneity, the largest outbreaks of MERS are characterized by sharper incidence peaks. The probability of occurrence of MERS outbreaks larger than the South Korean cluster (n = 186) is of the order of 1 %. Conclusions Our study suggests that the South Korean outbreak followed a similar progression to previously described hospital clusters involving coronaviruses, with early super-spreading events generating a disproportionately large number of secondary infections, and the transmission potential diminishing greatly in subsequent generations. Differences in relative exposure patterns and transmission heterogeneity of MERS and SARS could point to changes in hospital practices since 2003 or differences in transmission mechanisms of these coronaviruses.

Cancer cells, despite their shared origin, could be heterogeneous with respect to their stemness, plasticity, self-renewal, and oncogenicity. Recent findings indicate that a small proportion of the cancer cells oligopolize the capacity to produce diverse cancer subtypes and metastasize to other sites. Analogous to the apical hierarchy observed in adult stem cells, such versatile cancer cells were termed cancer stem cells. Meanwhile, hypertumors that exploit the cooperation of other cancer cells may disrupt the integrity of the tumor, prompting tumor regression. The biology of cancer stem cells and hypertumors has substantial clinical potential, but no study up to date has investigated the effect of cancer hierarchy on hypertumor progression. In this study, we developed biologically relevant models that elucidate the dynamics of hypertumor progression under different hierarchical structures. Our models align with previously observed data from human breast cancer subpopulations capable of state transitions. We tested and compared the progression dynamics of cancer clusters with different characteristics. Considering the trade-off between proliferation and mutation risk, our computational results suggest that existence of the cancer stem cells with high self-renewal and replication could be the prerequisite for attaining larger cancer size. In contrast, if a small cancer size is sufficient to induce lethality, a tumor composed of homogeneous cells would take less time to reach such a threshold size. Consequently, the hierarchical structure of cancer that reaches a lethal size may vary across species, representing a relevant mechanism of Peto’s paradox. The formulations presented in this study link the less attended aspects of cancer which would provide integrative insights for therapeutic strategies.

Dengue fever is a major international public health concern, with more than 55% of the world population at risk of infection. Recent climate changes related to global warming have increased the potential risk of domestic outbreaks of dengue in Korea. In this study, we develop a two-strain dengue model associated with climate-dependent parameters based on Representative Concentration Pathway (RCP) scenarios provided by the Korea Meteorological Administration. We assess the potential risks of dengue outbreaks by means of the vector capacity and intensity under various RCP scenarios. A sensitivity analysis of the temperature-dependent parameters is performed to explore the effects of climate change on dengue transmission dynamics. Our results demonstrate that a higher temperature significantly enhances the potential threat of domestic dengue outbreaks in Korea. Furthermore, we investigate the effects of countermeasures on the cumulative incidence of humans and vectors. The current main control measures (comprising only travel restrictions) for infected humans in Korea are not as effective as combined control measures (travel restrictions and vector control), dramatically reducing the possibilities of dengue outbreaks.

Recurrent outbreaks of the influenza virus continue to pose a serious health threat all over the world. The role of mass media becomes increasingly important in modeling infectious disease transmission dynamics since it can provide public health information that influences risk perception and health behaviors. Motivated by the recent 2009 H1N1 influenza pandemic outbreak in South Korea, a mathematical model has been developed. In this work, a previous influenza transmission model is modified by incorporating two distinct media effect terms in the transmission rate function; (1) a theory-based media effect term is defined as a function of the number of infected people and its rage of change and (2) a data-based media effect term employs the real-world media coverage data during the same period of the 2009 influenza outbreak. The transmission rate and the media parameters are estimated through the least-squares fitting of the influenza model with two media effect terms to the 2009 H1N1 cumulative number of confirmed cases. The impacts of media effect terms are investigated in terms of incidence and cumulative incidence. Our results highlight that the theory-based and data-based media effect terms have almost the same influence on the influenza dynamics under the parameters obtained in this study. Numerical simulations suggest that the media can have a positive influence on influenza dynamics; more media coverage leads to a reduced peak size and final epidemic size of influenza.

Super-spreading events have been observed in the transmission dynamics of many infectious diseases. The 2015 MERS-CoV outbreak in the Republic of Korea has also shown super-spreading events with a significantly high level of heterogeneity in generating secondary cases. It becomes critical to understand the mechanism for this high level of heterogeneity to develop effective intervention strategies and preventive plans for future emerging infectious diseases. In this regard, agent-based modeling is a useful tool for incorporating individual heterogeneity into the epidemic model. In the present work, a stochastic agent-based framework is developed in order to understand the underlying mechanism of heterogeneity. Clinical (i.e., an infectivity level) and social or environmental (i.e., a contact level) heterogeneity are modeled. These factors are incorporated in the transmission rate functions under assumptions that super-spreaders have stronger transmission and/or higher links. Our agent-based model has employed real MERS-CoV epidemic features based on the 2015 MERS-CoV epidemiological data. Monte Carlo simulations are carried out under various epidemic scenarios. Our findings highlight the roles of super-spreaders in a high level of heterogeneity, underscoring that the number of contacts combined with a higher level of infectivity are the most critical factors for substantial heterogeneity in generating secondary cases of the 2015 MERS-CoV transmission.

The omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the predominant variant in South Korea from late January 2022. In this study, we aimed to report the early estimates of the serial interval distribution and reproduction number to quantify the transmissibility of the omicron variant in South Korea between 25 November 2021 and 31 December 2021. We analyzed 427 local omicron cases and reconstructed 73 transmission pairs. We used a maximum likelihood estimation to assess serial interval distribution from transmission pair data and reproduction numbers from 74 local cases in the first local outbreak. We estimated that the mean serial interval was 3.78 (standard deviation, 0.76) days, which was significantly shorter in child infectors (3.0 days) compared to adult infectors (5.0 days) (p < 0.01). We estimated the mean reproduction number was 1.72 (95% CrI, 1.60–1.85) for the omicron variant during the first local outbreak. Strict adherence to public health measures, particularly in children, should be in place to reduce the transmission risk of the highly transmissible omicron variant in the community.

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial–temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial–temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

The COVID-19 pandemic has placed immense pressure on global healthcare systems, underscoring the urgent need for early and accurate prediction of disease severity to improve patient care and optimize resource allocation. Failure in ward allocation can lead to wasted hospital resources and inadequate treatment. This study analyzes data from 806 COVID-19 patients admitted to the emergency room of Chungbuk National University Hospital, Korea, between January 2021 and December 2022, to develop machine learning models that predict which patients should be prioritized for intensive care unit (ICU) placement based on initial clinical information. Additionally, two different severity criteria were considered based on actual ICU level interventions (Criterion I) and based on national policy definitions (Criterion II). Single models of logistic regression, random forest, support vector machine, light gradient boosting, and extreme gradient boosting, as well as ensemble learning models using voting classifiers, were used. The ensemble model achieved the best performance, with recall rates of 96.2% and 88.2% for each criterion, respectively. Key features such as glucose level, neutrophil count, high sensitivity C-reactive protein (hsCRP) level, and albumin level were identified, improving model interpretability. This study provides valuable insights for healthcare professionals to support effective early ward allocation and treatment strategies.

The COVID-19 pandemic has been spreading worldwide with more than 246 million confirmed cases and 5 million deaths across more than 200 countries as of October 2021. There have been multiple disease clusters, and transmission in South Korea continues. We aim to analyze COVID-19 clusters in Seoul from 4 March to 4 December 2020. A branching process model is employed to investigate the strength and heterogeneity of cluster-induced transmissions. We estimate the cluster-specific effective reproduction number Reff and the dispersion parameter κ using a maximum likelihood method. We also compute Rm as the mean secondary daily cases during the infection period with a cluster size m. As a result, a total of 61 clusters with 3088 cases are elucidated. The clusters are categorized into six groups, including religious groups, convalescent homes, and hospitals. The values of Reff and κ of all clusters are estimated to be 2.26 (95% CI: 2.02–2.53) and 0.20 (95% CI: 0.14–0.28), respectively. This indicates strong evidence for the occurrence of superspreading events in Seoul. The religious groups cluster has the largest value of Reff among all clusters, followed by workplaces, schools, and convalescent home clusters. Our results allow us to infer the presence or absence of superspreading events and to understand the cluster-specific characteristics of COVID-19 outbreaks. Therefore, more effective suppression strategies can be implemented to halt the ongoing or future cluster transmissions caused by small and sporadic clusters as well as large superspreading events.

Background The global spread of infectious diseases like COVID-19, accelerated by globalization and frequent international interactions, poses a serious threat to public health. Robust epidemiological data on imported cases is essential for managing cross-border disease transmission, enabling targeted public health responses through international cooperation and enhanced surveillance. This study investigates the impact of imported COVID-19 cases on South Korea’s pandemic response from January 2020 to May 2023. Methods We analyzed 78,495 imported COVID-19 cases reported by the Korea Disease Control and Prevention Agency (KDCA) from January 2020 to May 2023. The dataset included demographic information, countries of origin, entry points, and information on dominant viral variants during the specified period. Temporal and spatial analyses examined how variant transitions, particularly from Delta to Omicron, and quarantine policy changes influenced imported case patterns. We calculated the “confirmation lag,” the time between entry and confirmation, and applied Kernel Density Estimation (KDE) to evaluate how detection speed was affected by policy shifts and variant changes. In addition, least squares regression was used to explore the relationship between regional population size and the number of imported cases. Results The findings reveal a significant increase in imported cases during the transition from the Delta to Omicron variants, highlighting the increased transmissibility of Omicron and its impact on imported case numbers. Consequently, testing strategies were improved for faster detection and quarantine adaptability, which was confirmed by the observed reduction in confirmation lag. Regions with major entry points, such as Incheon, had higher imported case ratios. Population size was the strongest predictor, followed by first importation timing. This trend underscores the importance of tailored measures, such as region-based surveillance and country-targeted entry policies, to effectively manage virus importation. Conclusion By systematically analyzing a large-scale dataset of imported COVID-19 cases, we demonstrate that targeted border measures—accounting for travelers’ countries of origin and regional vulnerabilities—are essential for effective containment. Our findings underscore the value of variant-specific strategies, reinforced by real-time surveillance of imported cases, as a critical component of South Korea’s public health infrastructure. This approach not only enhances current response capacity but also strengthens preparedness for future cross-border infectious disease threats.