Objectives
This study assessed the economic value of 2 major initiatives within the national infectious disease response system of the Korea Disease Control and Prevention Agency (KDCA): the Training Programs for Public Health Workforce (TPPHW) and the Infectious Disease Information System (IDIS). Methods: We developed a structured estimation model to quantify the economic benefits of pandemic preparedness investments and applied it to the KDCA’s TPPHW and IDIS. The model incorporated parameters elicited from 29 senior officials into a cost-benefit analysis (CBA) framework, from which we calculated the net present value (NPV) and benefit-cost ratio (BCR) over a 10-year period, with sensitivity analyses. Results: Both initiatives yielded positive net benefits under baseline assumptions, although the margin was substantially greater for workforce training than for the information system. At the assumed discount rate of 4.5%, the CBA yielded a BCR of 2.120 and an NPV of 27.2 billion Korean won (KRW) for TPPHW. For IDIS, the corresponding values were a BCR of 1.071 and an NPV of 2.7 billion KRW. Sensitivity analysis showed that workforce training remained economically justified across all scenarios, whereas the information system remained viable only under more favorable assumptions. Conclusion: This study contributes to the literature by systematically applying CBA to infectious disease preparedness in the Republic of Korea. The findings support continued investment in skilled workforce training and integrated information infrastructure for future pandemic preparedness. The study is limited by its reliance on expert-elicited parameters and its focus on monetary benefits, excluding health outcomes such as disability-adjusted life years and quality-of-life improvements.

Objectives
The aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India. Methods: Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India. Results: In total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting. Conclusion: The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.