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Abstract Understanding the impacts of climate change on oceanic carbon cycling is important from a carbon sequestration perspective. A sediment trap study focused on the biological carbon pump system in the Ulleung Basin (UB) in the southwestern part of the East Sea (Japan Sea) was conducted from 2011 to 2017. Particulate organic carbon (POC) flux significantly increased by 37, 56, and 43% from 2014 to 2016 during the El Niño phase. We examined data related to water current variability, such as sea surface height, current velocity, and eddy frequency to understand their roles in particle transport. In addition, a Martin curve was employed to analyze the rate of vertical attenuation of POC flux in the UB. Current variability could be the most important factor influencing the increase in sinking-particle flux during the El Niño phase.

Abstract Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5–95.5%), death cases (85.6–91.8%), and imported cases (77.7–89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics.

IntroductionIn this study, we used a mathematical epidemic model to explore the status of the HIV epidemic in the USA and Pakistan. In addition to studying the dynamics of the model, we fitted the model with recent data to estimate the parameters describing the epidemic in both countries.ResultsOur estimation shows that in the USA, the reproduction number is 0.9688 (0.9684, 0.9694); if the reproduction number is maintained at this level, it would take a long time to eradicate HIV entirely. Meanwhile, it is 2.2599 (2.2556, 2.2656) in Pakistan, which is due to a lack of awareness in the confirmed group and a lower rate of maintained treatment. We also estimated the rate of vertical transmission, which plays a significant role in Pakistan but not in the USA.DiscussionWe conclude that improving the screening rate and educating people would be effective for controlling HIV in Pakistan, whereas improved screening rate in the USA can eradicate HIV faster.

IntroductionAfrican swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses.MethodsWe observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities.ResultsAs a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022.DiscussionThis approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.

IntroductionThe COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions.MethodsProbability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination.ResultsThe reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity’s importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea.DiscussionThis research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.

IntroductionThis paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance.MethodsTo address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods.ResultsThe four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities.ConclusionThis study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

Abstract Background Predicting tumor responses to neoadjuvant chemotherapy (NAC) is critical for evaluating prognosis and designing treatment strategies for patients with breast cancer; however, there are no reliable biomarkers that can effectively assess tumor responses. Therefore, we aimed to evaluate the clinical feasibility of using extracellular vesicles (EVs) to predict tumor response after NAC. Methods Drug-resistant triple-negative breast cancer (TNBC) cell lines were successfully established, which developed specific morphologies and rapidly growing features. To detect resistance to chemotherapeutic drugs, EVs were isolated from cultured cells and plasma samples collected post-NAC from 36 patients with breast cancer. Results Among the differentially expressed gene profiles between parental and drug-resistant cell lines, drug efflux transporters such as MDR1, MRP1, and BCRP were highly expressed in resistant cell lines. Drug efflux transporters have been identified not only in cell lines but also in EVs released from parental cells using immunoaffinity-based EV isolation. The expression of drug resistance markers in EVs was relatively high in patients with residual disease compared to those with a pathological complete response. Conclusions The optimal combination of drug-resistant EV markers was significantly efficient in predicting resistance to NAC with 81.82% sensitivity and 92.86% specificity.

Abstract This study analyzes the impact of COVID-19 variants on cost-effectiveness across age groups, considering vaccination efforts and nonpharmaceutical interventions in Republic of Korea. We aim to assess the costs needed to reduce COVID-19 cases and deaths using age-structured model. The proposed age-structured model analyzes COVID-19 transmission dynamics, evaluates vaccination effectiveness, and assesses the impact of the Delta and Omicron variants. The model is fitted using data from the Republic of Korea between February 2021 and November 2022. The cost-effectiveness of interventions, medical costs, and the cost of death for different age groups are evaluated through analysis. The impact of different variants on cases and deaths is also analyzed, with the Omicron variant increasing transmission rates and decreasing case-fatality rates compared to the Delta variant. The cost of interventions and deaths is higher for older age groups during both outbreaks, with the Omicron outbreak resulting in a higher overall cost due to increased medical costs and interventions. This analysis shows that the daily cost per person for both the Delta and Omicron variants falls within a similar range of approximately $10–$35. This highlights the importance of conducting cost-effect analyses when evaluating the impact of COVID-19 variants.

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

The Coronavirus disease of 2019 (COVID-19) is an ongoing public health concern worldwide. COVID-19 infections continue to occur and thus, it is important to assess the effects of various public health measures. This study aims to forecast COVID-19 cases by geographical area in Korea, based on the effects of different control-intervention intensities (CII). Methods involved estimating the effective reproduction number (Rt) by Korean geographical area using the SEIHR model, and the instantaneous reproduction number using statistical model, comparing the epidemic curves and high-, intermediate-, and low-intensity control interventions. Here, short-term four-week forecasts by geographical area were conducted. The mean of delayed instantaneous reproduction number was estimated at 1.36, 1.03, and 0.93 for the low-, intermediate-, and high-intensity control interventions, respectively, in the capital area of Korea from July 16, 2020, to March 4, 2021. The COVID-19 cases were forecasted with an accuracy rate of 11.28%, 13.62%, and 20.19% MAPE in Korea, including both the capital and non-capital areas. High-intensity control measures significantly reduced the reproduction number to be less than one. The proposed model forecasted COVID-19 transmission dynamics with good accuracy and interpretability. High-intensity control intervention, active case detection, and isolation efforts should be maintained to control the pandemic.

Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of Rt, which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease in China, Japan, and Korea. This study aimed to estimate the monthly SFTS occurrence and the monthly number of SFTS cases in the geographical area in Korea using epidemiological data including demographic, geographic, and meteorological factors. Important features were chosen through univariate feature selection. Two models using machine learning methods were analyzed: the classification model in machine learning (CMML) and regression model in machine learning (RMML). We developed a novel model incorporating the CMML results into RMML, defined as modified-RMML. Feature importance was computed to assess the contribution of estimating the number of SFTS cases using modified-RMML. Aspect to the accuracy of the novel model, the performance of modified-RMML was improved by reducing the MSE for the test data as 12.6–52.2%, compared to the RMML using five machine learning methods. During the period of increasing the SFTS cases from May to October, the modified-RMML could give more accurate estimation. Computing the feature importance, it is clearly observed that climate factors such as average maximum temperature, precipitation as well as mountain visitors, and the estimation of SFTS occurrence obtained from CMML had high Gini importance. The novel model incorporating CMML and RMML models improves the accuracy of the estimation of SFTS cases. Using the model, climate factors, including temperature, relative humidity, and mountain visitors play important roles in transmitting SFTS in Korea. Our findings highlighted that the guidelines for mountain visitors to prevent SFTS transmissions should be addressed. Moreover, it provides important insights for establishing control interventions that predict early identification of SFTS cases.

Objective: Even with relatively high vaccination coverage, Japan experienced rubella epidemics in 2012–2014 and 2018–2019, which were fueled by untraced imported cases. We aimed to develop a risk map for rubella epidemics in Japan by geographic location via analysis of seroepidemiological data and accounting for the abundance of foreign visitors. Methods: Geographic age distribution and seroprevalence were used to compute the age- and sex-dependent next-generation matrix in each region. We computed the probability of a major epidemic using the assumed number of untraced imported rubella cases proportionally modeled to the number of foreign travelers. Results: Risks of a major epidemic were high in areas with capital cities, while areas with a greater fraction of older people yielded smaller effective reproduction numbers, a lower volume of foreign travelers, and thus a lower probability of a major epidemic. The volume of susceptible adult males was larger in urban geographic regions, having a greater number of foreign travelers than remote areas. Conclusions: Our findings are consistent with the observation of multiple large clusters of rubella cases in urban areas during 2012–2014 and 2018–2019. Should a future rubella epidemic occur, it will likely be in geographic areas with capital cities.

Liver disease: Collagen fragment spurs liver inflammation and fibrosis Localized buildup in the liver of a collagen-derived cleavage fragment plays a critical role in accelerating the development of tissue scarring and liver cancer in mice. Using a transgenic mouse model of fatty liver disease, a team led by Philipp Scherer from the University of Texas Southwestern Medical Center, Dallas, USA, and Jiyoung Park of Ulsan National Institute of Science and Technology, South Korea, showed that liver-specific accumulation of a collagen fragment known as endotrophin leads to activation of inflammatory and fibrotic pathways. The mice subsequently develop metabolic dysfunction, insulin resistance and, with time, cancerous nodules in the liver. The findings suggest that endotrophin accumulation, when it occurs in liver tissue that already contains large depositions of fat, can overwhelm normal liver physiology and accelerate organ damage.

Rapid transmission of coronavirus disease 2019 (COVID-19) was observed in the Shincheonji Church of Jesus, a religious sect in South Korea. The index case was confirmed on February 18, 2020 in Daegu City, and within two weeks, 3081 connected cases were identified. Doubling times during these initial stages (i.e., February 18 – March 2) of the outbreak were less than 2 days. A stochastic model fitted to the time series of confirmed cases suggests that the basic reproduction number (R0) of COVID-19 was 8.5 [95% credible interval (CrI): 6.3, 10.9] among the church members, whereas (R0 = 1.9 [95% CrI: 0.4, 4.4]) in the rest of the population of Daegu City. The model also suggests that there were already 4 [95% CrI: 2, 11] undetected cases of COVID-19 on February 7 when the index case reportedly presented symptoms. The Shincheonji Church cluster is likely to be emblematic of other outbreak-prone populations where R0 of COVID-19 is higher. Understanding and subsequently limiting the risk of transmission in such high-risk places is key to effective control.

OBJECTIVES During the coronavirus disease 2019 (COVID-19) pandemic, crude incidence and mortality rates have been widely reported; however, age-standardized rates are more suitable for comparisons. In this study, we estimated and compared the age-standardized incidence, mortality, and case fatality rates (CFRs) among countries and investigated the relationship between these rates and factors associated with healthcare resources: gross domestic product per capita, number of hospital beds per population, and number of doctors per population. METHODS The incidence, mortality, and CFRs of 79 countries were age-standardized using the World Health Organization standard population. The rates for persons 60 years or older were also calculated. The relationships among the rates were analysed using trend lines and coefficients of determination (R2). Pearson correlation coefficients between the rates and the healthcare resource-related factors were calculated. RESULTS The countries with the highest age-standardized incidence, mortality, and CFRs were Czechia (14,253 cases/100,000), Mexico (182 deaths/100,000), and Mexico (6.7%), respectively. The R2 between the incidence and mortality rates was 0.852 for all ages and 0.945 for those 60 years or older. The healthcare resources-related factors were associated positively with incidence rates and negatively with CFRs, with weaker correlations among the elderly. CONCLUSIONS Compared to age-standardized rates, crude rates showed greater variation among countries. Medical resources may be important in preventing COVID-19-related deaths; however, considering the small variation in fatality among the elderly, preventive measures such as vaccination are more important, especially for the elderly population, to minimize the mortality rates.

During an Ebola virus disease (EVD) outbreak, the analysis and forecasting of the incidence in real time is challenged by reporting of cases, especially the reporting delay. It should be remembered that the latest count of cases is likely underestimated in real time, and moreover, the effective reproduction number, i.e. the average number of secondary cases generated by a single primary case at a given point in time, is also underestimated without proper adjustment. The present study aimed to adjust the reporting delay to appropriately estimate the latest incidence and obtain short-term forecasts from weekly reporting data of EVD in North Kivu, the Democratic Republic of the Congo (DRC). A semi-structured modeling approach was taken, accounting for reporting delay which can depend on time. The mean reporting delay was estimated at 11.6 days (95% CI: 11.3, 11.9) and the standard deviation was estimated to have changed from 26 November 2019 from 8.5–6.0 days. Nowcasting was successfully implemented by account for the time-dependent reporting delay: it mostly contained future observed values within the 95% confidence intervals, but there were failures when the reported incidence abruptly changed over time. Forecasting was also exercised in a similar manner to the nowcasting, while we imposed an extrapolation approach to the effective reproduction number for two future weeks. Moving average of the reproduction numbers for a few weeks prior the latest time of observation outperformed other extrapolations. The information that we can gain from real time (i.e. sequential) update of “situation report” can be considerably improved by integrating the proposed nowcasting and forecasting to the surveillance system. Keywords: Reporting delay, Nowcasting, Forecasting, Prediction, Effective reproduction number

Objectives: A rubella epidemic has been ongoing in Japan since August 2018. In the present study, we aimed to predict the likely size of a congenital rubella syndrome (CRS) epidemic during 2018–19. Methods: The expected number of CRS cases was estimated using an integral equation based on age-specific incidence of rubella among adult women, the time delay from gestational age of infection to diagnosis of CRS, and distribution of the mothers’ age at delivery. We used epidemic data during 2012–14 to parameterize the model and applied this in the prediction for 2018–19. Results: In analyzing the 2012–14 epidemic data, the mean delay from the mother’s infection to diagnosis was estimated at 24.2 weeks (95% confidence interval (CI): 20.7, 28.1). Applying the parameterized model, together with the more than 480 rubella cases in women in 2018 as well as delayed mother’s age at delivery in 2017, we determined that the expected number of CRS cases would be 9.7 (95% CI: 6.5, 12.5) cases. As the epidemic is ongoing, the cumulative number of CRS cases could potentially reach 96.8 (95% CI: 65.3, 125.5) cases, if rubella cases in adult women rose to 10 times the number by week 49 in 2018. Conclusions: CRS is expected to occur an average of 24 weeks following the mother’s infection with rubella virus. Accounting for an increase to 650 cases in women by week 5 in 2019, the expected number of CRS cases during 2018–19 has already exceeded 13 cases, as of week 5 in 2019. Keywords: Rubella virus, Statistical model, Forecasting, Outbreak, Epidemiology, Prediction, Japan

The international spread of Zika virus (ZIKV) began in Brazil in 2015. To estimate the risk of observing imported ZIKV cases, we calculated effective distance, typically an excellent predictor of arrival time, from airline network data. However, we eventually concluded that, for ZIKV, effective distance alone is not an adequate predictor of arrival time, which we partly attributed to the difficulty of diagnosing and ascertaining ZIKV infections. Herein, we explored the mechanisms behind the observed time delay of ZIKV importation by country, statistically decomposing the delay into two parts: the actual time to importation from Brazil and the reporting delay. The latter was modeled as a function of the gross domestic product (GDP) and other variables that influence underlying diagnostic capacity in a given country. We showed that a high GDP per capita is a good predictor of short reporting delay. ZIKV infection is generally mild and, without substantial laboratory capacity, cases can be underestimated. This study successfully demonstrates this phenomenon and emphasizes the importance of accounting for reporting delays as part of the data generating process for estimating time to importation.

Objectives To describe and evaluate epidemiological investigation results and containment measures implemented in Busan, where 108 cases were confirmed with coronavirus disease 2019 (COVID-19) between February 21, 2020 and March 24, 2020. Methods Any individual who tested positive for COVID-19 was classified as a confirmed case. Measures were taken to identify the source of infection and trace and quarantine contacts. Serial intervals were estimated and the effective reproduction number was computed. Results Of the total 18,303 COVID-19 tests performed between January 16, 2020 and March 24, 2020 in Busan, 108 yielded positive results (positive test rate, 0.6%). All confirmed cases were placed in isolation at hospitals. Of the 108 confirmed cases, 59 (54.6%) were female. The most common age group was 20-29 years with 37 cases (34.3%). Regarding symptoms at the time of diagnosis, cough (n=38, 35.2%) and fever (n=34, 31.5%) were most common; 12 cases (11.1%) were asymptomatic. The source of infection was identified in 99 cases (91.7%). A total of 3,223 contacts were identified and quarantined. Household contacts accounted for 196, and the household secondary attack rate was 8.2% (95% confidence interval [CI], 4.7 to 12.9). The mean serial interval was estimated to be 5.54 days (95% CI, 4.08 to 7.01). After February 26, (Rt) remained below 1 in Busan. Conclusions The early containment strategy implemented in Busan shows that control is possible if outbreaks are of limited scope. In preparation for future outbreaks, public health and healthcare systems should be re-examined and put in a ready state.

Objective: To investigate a measles outbreak that spread to Japan and Taiwan, China during March–May 2018, exploring the characteristics of the super-spreading event. Methods: A contact investigation of the index case and reconstruction of the epidemiological dynamics of measles transmission were conducted. Employing a mathematical model, the effective reproduction number was estimated for each generation of cases. Results and discussion: A single index case gave rise to a total of 38 secondary cases, 33 in Japan and five in Taiwan, China. Subsequent chains of transmission were observed in highly vaccinated populations in both Japan and Taiwan, China. The effective reproduction number of the second generation was >1 for both Japan and Taiwan, China. In Japan, the reproduction number was estimated to be

Objectives: The characteristics of disease in North Korea, including severe malnutrition and infectious disease risks, have not been openly and widely analyzed. This study was performed to estimate the risks of infectious diseases among refugees from North Korea. Methods: A literature review of clinical studies among North Korean defectors was conducted to statistically estimate the risks of infectious diseases among North Korean subjects. Results: A total of six groups of data from five publications covering the years 2004 to 2014 were identified. Tuberculosis and viral hepatitis appeared to be the two most common infectious diseases, especially among adult refugees. When comparing the risks of infectious diseases between North Korean and Syrian refugees, it is critical to remember that Plasmodium vivax malaria has been endemic in North Korea, while cutaneous leishmaniasis has frequently been seen among Syrian migrants. Conclusions: Valuable datasets from health surveys of defectors were reviewed. In addition to tuberculosis and viral hepatitis, which were found to be the two most common infectious diseases, a special characteristic of North Korean defectors was Plasmodium vivax malaria. This needs to be added to the list of differential diagnoses for pyretic patients.

Kimchi, a popular traditional Korean fermented food, is produced by fermenting vegetables with various spices and salt. Salt plays an important role in the preparation of kimchi and affects its taste and flavor. This study aimed to investigate the effects of salinity on kimchi fermentation. The salinities of five sets of kimchi samples were adjusted to 1.4%, 1.7%, 2.0%, 2.2%, and 2.5%. The characteristics of each kimchi sample, including its pH, acidity, free sugar content, free amino acid content, organic acid content, and microbial community composition, were evaluated during kimchi fermentation. The low-salinity kimchi sample showed a rapid decline in the pH at the beginning of the fermentation process, a relatively high abundance of Leuconostoc mesenteroides, and high mannitol production. In the late fermentation period, Latilactobacillus sakei had a higher abundance in the kimchi sample with high salinity than in other samples. In the initial stage of fermentation, the metabolite composition did not differ based on salinity, whereas the composition was considerably altered from the third week of fermentation. The findings showed variations in the characteristics and standardized manufacturing processes of kimchi at various salt concentrations. Therefore, salinity significantly affected the types and concentrations of fermentation metabolites in kimchi.

BackgroundIn summer 2014, an autochthonous outbreak of dengue occurred in Tokyo, Japan, in which Yoyogi Park acted as the focal area of transmission. Recognizing the outbreak, concerted efforts were made to control viral spread, which included mosquito control, public announcement of the outbreak, and a total ban on entering the park. We sought to assess the effectiveness of these control measures.Methodology/principal findingsWe used a mathematical model to describe the transmission dynamics. Using dates of exposure and illness onset, we categorized cases into three groups according to the availability of these datasets. The infection process was parametrically modeled by generation, and convolution of the infection process and the incubation period was fitted to the data. By estimating the effective reproduction number, we determined that the effect of dengue risk communication together with mosquito control from 28 August 2014 was insufficiently large to lower the reproduction number to below 1. However, once Yoyogi Park was closed on 4 September, the value of the effective reproduction number began to fall below 1, and the associated relative reduction in the effective reproduction number was estimated to be 20%-60%. The mean incubation period was an estimated 5.8 days.Conclusions/significanceRegardless of the assumed number of generations of cases, the combined effect of mosquito control, risk communication, and park closure appeared to be successful in interrupting the chain of dengue transmission in Tokyo.

Background It is plausible that the routine immunization among infants using pneumococcal conjugate vaccine 13 (PCV13) from 2013 and among the elderly using pneumococcal polysaccharide vaccine 23 (PPV23) from 2014 contributed to reducing the pneumonia mortality among the elderly in Japan. The present study aimed to estimate the causal effect of this vaccination on pneumonia mortality, using the available cause-of-death data and employing a difference-in-difference (DID) design. Methods Two types of mortality data, that is, prefecture-dependent and age- and gender-specific mortality data, from 2003 to 2017 were retrieved. We used mortality due to malignant neoplasm and heart disease as control groups and employed a DID design with an assumed parallel mortality trend between pneumonia and control group mortality since 2013 to estimate the causal effect of pneumococcal vaccination from 2014. Results Our estimation based on malignant neoplasm and heart disease as controls indicated that the reduced pneumonia mortality in 2017 owing to pneumococcal vaccination was as large as 41.9 (33.2, 50.6) and 31.2 (23.8, 38.6) per 100,000 individuals, respectively. The largest mortality reduction was observed for the oldest group (aged ≥90 years), especially among men. Discussion The pneumococcal vaccination program, perhaps mainly represented by high vaccination coverage of PCV13 among children and partly by PPV23 administration with low coverage among the elderly in Japan, was shown to have reduced the pneumonia mortality in the elderly at the population level.

Objectives: There have been errors in determining the end of the Ebola virus disease (EVD) epidemic when adhering to the criteria of the World Health Organization. The present study aimed to review and learn from all known recrudescence events in West Africa occurring in 2014â2016. Methods: Background mechanisms of five erroneous declarations in Guinea, Liberia, and Sierra Leone during 2014â2016 were reviewed. Results: Three cases of recrudescence were suspected to have been caused by sexual contact with survivors, one to be due to international migration, and one was linked to a potentially immunocompromised mother. The three sexual transmission events involving survivorsâthe first two in Liberia and one in Sierra Leoneârequired 164 days, >150 days, and approximately 180 days, respectively, from discharge of the survivors to confirmation of the recrudescent case. Conclusions: The events of recrudescence were associated with relatively uncommon routes of transmission other than close contact during burial or care-giving, including sexual transmission, possible immunocompromise, and migration. Recognition of the sexual transmission risk among survivors could potentially involve discrimination, which may lead to under-ascertainment. Keywords: Ebola virus disease, Survivors, Sexually transmitted infections, Epidemic, Decision-making

Background A Rohingya refugee camp in Cox’s Bazar, Bangladesh experienced a large-scale diphtheria epidemic in 2017. The background information of previously immune fraction among refugees cannot be explicitly estimated, and thus we conducted an uncertainty analysis of the basic reproduction number, R0. Methods A renewal process model was devised to estimate the R0 and ascertainment rate of cases, and loss of susceptible individuals was modeled as one minus the sum of initially immune fraction and the fraction naturally infected during the epidemic. To account for the uncertainty of initially immune fraction, we employed a Latin Hypercube sampling (LHS) method. Results R0 ranged from 4.7 to 14.8 with the median estimate at 7.2. R0 was positively correlated with ascertainment rates. Sensitivity analysis indicated that R0 would become smaller with greater variance of the generation time. Discussion Estimated R0 was broadly consistent with published estimate from endemic data, indicating that the vaccination coverage of 86% has to be satisfied to prevent the epidemic by means of mass vaccination. LHS was particularly useful in the setting of a refugee camp in which the background health status is poorly quantified.

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.

Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility.

Stress has been shown to suppress immune function and increase susceptibility to inflammatory disease and psychiatric disease. CD4(+)CD25(+) regulatory T (Treg) cells are prominent in immune regulation. This study was conducted to determine if anti-CD25 antibody (Ab) mediated depletion of Treg cells in mice susceptibility to stress-induced development of depression-like behaviors, as well as immunological and neurochemical activity. To accomplish this, an elevated plus-maze test (EPM), tail suspension test (TST), and forced swim test (FST) were used to examine depression-like behaviors upon chronic immobilization stress. Immune imbalance status was observed based on analysis of serum cytokines using a mouse cytometric bead array in conjunction with flow cytometry and changes in the levels of serotonin (5-HT) and dopamine (DA) in the brain were measured by high performance liquid chromatography (HPLC). The time spent in the open arms of the EPM decreased significantly and the immobility time in the FST increased significantly in the anti-CD25 Ab-treated group when compared with the non stressed wild-type group. In addition, interlukin-6 (IL-6), tumor necrosis factor-á (TNF-á), interlukin-2 (IL-2), interferon-gamma (IFN-γ), interlukin-4 (IL-4) and interlukin-17A (IL-17A) concentrations were significantly upregulated in the stressed anti-CD25 Ab-treated group when compared with the non stressed wild-type group. Furthermore, the non stressed anti-CD25 Ab-treated group displayed decreased 5-HT levels within the hippocampus when compared with the non stressed wild-type group. These results suggest that CD4(+)CD25(+) Treg cell depletion modulated alterations in depressive behavior, cytokine and monoaminergic activity. Therefore, controlling CD4(+)CD25(+) Treg cell function during stress may be a potent therapeutic strategy for the treatment of depression-like symptoms.