ABSTRACT
As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.
ABSTRACT
The study of climate and respiratory viral infections using big data may enable the recognition and interpretation of relationships between disease occurrence and climatic variables. In this study, realtime reverse transcription quantitative PCR (qPCR) methods were used to identify Human respiratory coronaviruses (HCoV). infections in patients below 10 years of age with respiratory infections who visited Dankook University Hospital in Cheonan, South Korea, from January 1, 2012, to December 31, 2018. Out of the 9010 patients who underwent respiratory virus real-time reverse transcription qPCR test, 364 tested positive for HCoV infections. Among these 364 patients, 72.8% (n = 265) were below 10 years of age. Data regarding the frequency of infections was used to uncover the seasonal pattern of the two viral strains, which was then compared with local meteorological data for the same time period. HCoV-229E and HCoV-OC43 showed high infection rates in patients below 10 years of age. There was a negative relationship between HCoV-229E and HCoV-OC43 infections with air temperature and wind-chill temperatures. Both HCoV-229E and HCoV-OC43 rates of infection were positively related to atmospheric pressure, while HCoV-229E was also positively associated with particulate matter concentrations. Our results suggest that climatic variables affect the rate in which children below 10 years of age are infected with HCoV. These findings may help to predict when prevention strategies may be most effective.