Correlations Between the Incidence of National Notifiable Infectious Diseases and Public Open Data, Including Meteorological Factors and Medical Facility Resources.
OBJECTIVE
This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea.


METHODS
We collected and stored 660,000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul.


RESULTS
Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient.


CONCLUSIONS
In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.
Biomedical Prediction Technology Laboratory, Convergence Technology Research Division, Korea Institute of Science and Technology Information, Daejeon, Korea ; Laboratory of Computational Biology and Bioinformatics, Institute of Public Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea.
Jin-Hwa
JH
Jang
Laboratory of Computational Biology and Bioinformatics, Institute of Public Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea ; Graduate Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea.
Ji-Hae
JH
Lee
Laboratory of Computational Biology and Bioinformatics, Institute of Public Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea ; Graduate Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea.
Mi-Kyung
MK
Je
Laboratory of Computational Biology and Bioinformatics, Institute of Public Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea.
Myeong-Ji
MJ
Cho
Department of Parasitology and Tropical Medicine, Seoul National University College of Medicine, Seoul, Korea ; Institute of Endemic Diseases, Seoul National University Medical Research Center, Seoul, Korea.
Young Mee
YM
Bae
Laboratory of Computational Biology and Bioinformatics, Institute of Public Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea ; Graduate Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea.
Hyeon Seok
HS
Son
Biomedical Prediction Technology Laboratory, Convergence Technology Research Division, Korea Institute of Science and Technology Information, Daejeon, Korea.
Insung
I
Ahn
D000397:Air Pollution; D003141:Communicable Diseases / Q000453:epidemiology*; D016208:Databases, Factual; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology; D008685:Meteorological Concepts; D056910:Republic of Korea / Q000453:epidemiology; D013696:Temperature
Correlation coefficient; Incidence; Infectious disease; Meteorology