Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Background: The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM. Methods: Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slowacetylation phenotype. The efficacy of the adjusted 150-mg dose was evaluated in a timeto-kill assay performed using the bacterial isolate Mycobacterium tuberculosis (Mtb) H37Ra in a HFIM that mimicked the individual pharmacokinetic profile of the patient. Results: The isoniazid concentration observed in the HFIM adequately reflected the target drug exposures simulated by the model. After 7 days of repeated dose administration, isoniazid killed 4 log 10 Mtb CFU/mL in the treatment arm, while the control arm without isoniazid increased 1.6 log 10 CFU/mL. Conclusion Our results provide an example of the utility of the HFIM for predicting the efficacy of specific recommended doses of anti-tuberculosis drugs in real clinical setting.