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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.

During the winter of 2016-2017, an epidemic of highly pathogenic avian influenza (HPAI) led to high mortality in poultry and put a serious burden on the poultry industry of the Republic of Korea. Effective control measures considering spatial heterogeneity to mitigate the HPAI epidemic is still a challenging issue. Here we develop a spatial-temporal compartmental model that incorporates the culling rate as a function of the reported farms and farm density in each town. The epidemiological and geographical data of two species, chickens and ducks, from the farms in the sixteen towns in Eumseong-gun and Jincheon-gun are used to find the best-fitted parameters of the metapopulation model. The best culling radius to maximize the final size of the susceptible farms and minimize the total number of culled farms is calculated from the model. The local reproductive number using the next generation method is calculated as an indicator of virus transmission in a given area. Simulation results indicate that this parameter is strongly influenced not only by epidemiological factors such as transmissibility and/or susceptibility of poultry species but also by geographical and demographical factors such as the distribution of poultry farms (or density) and connectivity (or distance) between farms. Based on this result, we suggest the best culling radius with respect to the local reproductive number in a targeted area.

Background Understanding social contact patterns is fundamental to the study of infectious disease transmission. However, in South Korea, detailed social contact data have not been publicly available. While global research on social contact patterns has expanded, there remains a critical need for more context-specific data in South Korea. Methods We conducted a social contact survey over two distinct weeks covering various time periods, including school vacations and national holidays. Participants provided details such as the location, duration, frequency, and type of close contact, as well as information on the contact person’s age, sex, residential area and relationship with the participant. We analyzed the data using summary statistics and the Bayesian linear mixed model. Results A total of 1,987 participants recorded 133,776 contacts over two weeks, averaging 4.81 contacts per participant per day. The average number of contacts per day varied by age, household size, and time period. Contacts were highest in the age group 5-19, lowest in the age group 20-29, and then gradually increased up to the age group 70+. Contacts also increased with household size. Weekdays during the school semester showed the highest number of contacts, followed by weekdays during vacations, the Lunar New Year holidays, and weekends. Contact patterns differed notably by period; during the Lunar New Year holidays, closed contacts with extended family members and, therefore, subnational social mixing were enhanced. Conclusion Our analyses across different time periods revealed significant and some unique variations of social contact patterns in South Korea. These findings can improve our understanding of infectious disease transmission in South Korea and will be useful for tailoring regional epidemiological models.

Pharmaceuticals and personal care products (PPCPs) are recognized as emerging contaminants of concern, even at ultra-trace concentrations. However, the current detection systems are prohibitively expensive and typically rely on labor-intensive, lab-based workflows that lack automation in sample pretreatment. In this study, we developed a robotic and on-flow solid-phase extraction (ROF-SPE) system, fully integrated with online liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the on-site and real-time monitoring of 16 PPCPs in wastewater effluent. The system automates the entire pretreatment workflow—including sample collection, filtration, pH adjustment, solid-phase extraction, and injection—prior to seamless coupling with LC–MS/MS analysis. The optimized pretreatment parameters (pH 7 and 10, 12 mL wash volume, 9 mL elution volume) were selected for analytical efficiency and cost-effectiveness. Compared with conventional offline SPE methods (~370 min), the total analysis time was reduced to 80 min (78.4% reduction), and parallel automation significantly enhanced the throughput. The system was capable of quantifying target analytes at concentrations as low as 0.1 ng/L. Among the 16 PPCPs monitored at a municipal wastewater treatment plant in South Korea, only sulfamethazine and ranitidine were not detected. Compounds such as iopromide, caffeine, and paraxanthine were detected at high concentrations, and seasonal variation patterns were also observed This study demonstrates the feasibility of a fully automated and on-site SPE pretreatment system for ultra-trace environmental analysis and presents a practical solution for the real-time monitoring of contaminants in remote areas.

Machine learning (ML) has increasingly been of interest in the design of new materials. However, it is still challenging to exploit an ML model in this field because its performance highly depends on the representation of materials, its properties, and the amount of data. In this study, for the cases of prediction of properties of crystalline materials, we explore a systematic comparison of two state-of-the-art frameworks: Crystal Graph Convolutional Neural Networks (CGCNNs) and the Sure Independence Screening and Sparsifying Operator (SISSO). The common key advantage of these two models is the fact that painstakingly handcrafted descriptors from simple material properties are not required. The main differences between the two models are (1) the use of structure information in the arbitrary size of compounds (CGCNN) and (2) limited interpretability (CGCNN) but simple and analytic relations between descriptor-property (SISSO). Using these two ML algorithms we evaluate the prediction performance on the target properties, which are band gap, formation energy, and elasticity of crystalline compounds in the database of Materials Project (MP). Moreover, to improve prediction of the properties of the materials without human bias in the selection of initial atomic features for the CGCNNs, we use Atom2Vec that provides atom representation obtained in an unsupervised manner from the materials. We also perform the predictions with the different sizes of training set to investigate the data-size dependency of the predictive models. According to the amount of dataset, the use of structural information, and the ability to identify the best descriptor with its interpretability, these algorithms showed different prediction performances. This result will enable researchers in materials discovery to gain appropriate choices and insights in various attempts to improve the prediction performance of crystalline materials' properties.

The SIR-model is a basic epidemic model that classifies a population into three subgroups: susceptible S , infected I and removed R . This model does not take into consideration the spatial distribution of each subgroup, but considers the total number of individuals belonging to each subgroup. There are many variants of the SIR-model. For studying the spatial distribution, stochastic processes have often been introduced to describe the dispersion of individuals. Such assumptions do not seem to be applicable to humans, because almost everyone moves within a small fixed radius in practice. Even if individuals do not disperse, the transmission of disease occurs. In this paper, we do not assume the dispersion of individuals, and instead use the infectious radius. Then, we propose simple continuous and discrete SIR-models that show spatial distributions. The results of our simulations show that the propagation speed and size of an epidemic depend on the population density and the infectious radius.

Nonpharmaceutical intervention has been one of the most important strategies to prevent the spread of the SARS-CoV-2 in the communities during the COVID-19 pandemic. Korea has a unique experience that we had the first large outbreak during the early pandemic and could flatten the epidemic curve without lockdown. In this study, the effective reproductive numbers were calculated for the entire nation and Seoul (the capital city) Metropolitan Area from February 16–15 July, where 60% of the population reside. We compared the changes in population mobility data and reproductive number trends according to the changes in the government’s nonpharmaceutical intervention strategy. The total daily mobility decreased when Korea had the first wave of a large outbreak in February–March 2020, which was mainly caused by the decrease of daily noncommuting mobility. However, daily commuting mobility from 16 February to 30 June 2020 was maintained at a similar level since there was no national lockdown for workers who commute between home and work. During the first half-year of 2020, Korea could control the outbreak to a manageable level without a significant decrease in daily public mobility. However, it may be only possible when the public follows personal hygiene principles and social distancing without crisis fatigue or reduced compliance.