Fine-Scale Spatial Prediction on the Risk of Plasmodium vivax Infection in the Republic of Korea.

BACKGROUND: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria incidence but face challenges due to delayed case detection and response. To improve this, machine learning models for predicting malaria, focusing on high-risk areas, have been developed. METHODS: The study targeted the northern region of ROK, near the demilitarized zone, using a 1-km grid to identify areas for prediction. Grid cells without residential buildings were excluded, leaving 8,425 cells. The prediction was based on whether at least one malaria case was reported in each grid cell per month, using spatial data of patient locations. Four algorithms were used: gradient boosted (GBM), generalized linear (GLM), extreme gradient boosted (XGB), and ensemble models, incorporating environmental, sociodemographic, and meteorological data as predictors. The models were trained with data from May to October (2019-2021) and tested with data from May to October 2022. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: The AUROC of the prediction models performed excellently (GBM = 0.9243, GLM = 0.9060, XGB = 0.9180, and ensemble model = 0.9301). Previous malaria risk, population size, and meteorological factors influenced the model most in GBM and XGB. CONCLUSION: Machine-learning models with properly preprocessed malaria case data can provide reliable predictions. Additional predictors, such as mosquito density, should be included in future studies to improve the performance of models.

Factors associated with the timely diagnosis of malaria and the utilization of types of healthcare facilities: a retrospective study in the Republic of Korea.

BACKGROUND: This study aimed to analyze trends in the timely diagnosis of malaria cases over the past 10 years in relation to the utilization of different types of healthcare facilities. METHODS: The study included 3,697 confirmed and suspected cases of malaria reported between January 1, 2013, and December 31, 2022, in the national integrative disease and healthcare management system. Some cases lacking a case report or with information missing from the case report were excluded from the analysis. A generalized linear model with a Poisson distribution was constructed to estimate risk ratios and 95% confidence intervals adjusted for other variables, such as distance. RESULTS: When cases involving diagnosis >5 days after symptom onset in confirmed patients (5DD) were examined according to the type of healthcare facility, the risk ratio of 5DD cases was found to be higher for tertiary hospitals than for public health facilities. Specifically, the risk ratio was higher when the diagnosis was established at a tertiary hospital, even after a participant had visited primary or secondary hospitals. In an analysis adjusted for the distance to each participant's healthcare facility, the results did not differ substantially from the results of the crude analysis. CONCLUSION: It is imperative to improve the diagnostic capabilities of public facilities and raise awareness of malaria at primary healthcare facilities for effective prevention and control.

Risk factors associated with death due to severe fever with thrombocytopenia syndrome in hospitalized Korean patients (2018-2022).

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) has no vaccine or treatment and an extremely high fatality rate. We aimed to analyze and evaluate the risk factors for death associated with SFTS. METHODS: Among reports from 2018 to 2022, we compared and analyzed 1,034 inpatients aged 18 years or older with laboratory-confirmed SFTS who underwent complete epidemiological investigations. RESULTS: Most of the inpatients with SFTS were aged 50 years or older (average age, 67.6 years). The median time from symptom onset to death was 9 days, and the average case fatality rate was 18.5%. Risk factors for death included age of 70 years or older (odds ratio [OR], 4.82); agriculture-related occupation (OR, 2.01); underlying disease (OR, 7.20); delayed diagnosis (OR, 1.28 per day); decreased level of consciousness (OR, 5.53); fever/chills (OR, 20.52); prolonged activated partial thromboplastin time (OR, 4.19); and elevated levels of aspartate aminotransferase (OR, 2.91), blood urea nitrogen (OR, 2.62), and creatine (OR, 3.21). CONCLUSION: The risk factors for death in patients with SFTS were old age; agriculture-related occupation; underlying disease; delayed clinical suspicion; fever/chills; decreased level of consciousness; and elevated activated partial thromboplastin time, aspartate aminotransferase, blood urea nitrogen, and creatine levels.

Current Status of Q Fever and the Challenge of Outbreak Preparedness in Korea: One Health Approach to Zoonoses.

Human Q fever, a zoonosis caused by Coxiella burnetii, presents with diverse clinical manifestations ranging from mild self-limited febrile illnesses to life-threatening complications such as endocarditis or vascular infection. Although acute Q fever is a benign illness with a low mortality rate, a large-scale outbreak of Q fever in the Netherlands led to concerns about the possibility of blood transfusion-related transmission or obstetric complications in pregnant women. Furthermore, a small minority (< 5%) of patients with asymptomatic or symptomatic infection progress to chronic Q fever. Chronic Q fever is fatal in 5-50% of patients if left untreated. In South Korea, Q fever in humans was designated as a notifiable infectious disease in 2006, and the number of Q fever cases has increased sharply since 2015. Nonetheless, it is still considered a neglected and under-recognized infectious disease. In this review, recent trends of human and animal Q fever in South Korea, and public health concerns regarding Q fever outbreaks are reviewed, and we consider how a One Health approach could be applied as a preventive measure to prepare for zoonotic Q fever outbreaks.