Association among sentinel surveillance, meteorological factors, and infectious disease in Gwangju, Korea.

The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (temperature: r = 0.710**; relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), rotavirus (r = - 0.408**; r = - 0.618**), norovirus (r = - 0.463**; r = - 0.316**), influenza virus (r = - 0.726**; r = - 0.672**), coronavirus (r = - 0.684**; r = - 0.408**), and coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predict infectivity through various types of data related to infection are important.

Effects of climatic factors on human parainfluenza 1, 2, and 3 infections in Cheonan, Republic of Korea.

Studying relationships between meteorological conditions and respiratory virus infections may help interpret the causality of disease outbreaks and provide a better understanding of the seasonal distribution of viruses. Therefore, in this study, we analyzed the correlations between meteorological data and the trends of infection by human parainfluenza virus-1 (HPIV-1; also known as human respirovirus 1), human parainfluenza virus-2 (human orthorubulavirus 2), and human parainfluenza virus-3 (human respirovirus 3) using 9010 viral samples collected at Dankook University Hospital from January 1, 2012, to December 31, 2018. Infection frequency data were used to detect the seasonal patterns of HPIV-1, HPIV-2, and HPIV-3 infections, and these patterns were compared with local weather data over the same period. We performed descriptive statistical analysis, frequency analysis, t test, and binomial logistic regression analysis to examine the relationships of weather and particulate matter conditions with the incidence of HPIV-1, HPIV-2, and HPIV-3 infections. The highest average infection rate with one of these three viruses (88.17%) was found in children aged 1-9 years. Specifically, the infection rate of HPIV-1 was 91.9% in children aged 1-9 years, whereas that of HPIV-2 and HPIV-3 was 86.3%. HPIV infection exhibited a meaningful relationship with climatic factors, such as temperature, wind-chill temperature, and atmospheric pressure. Our results suggest that climate changes might affect the rate of infection by HPIV. These findings may help in predicting the effectiveness of preventive strategies of HPIV infection.

Investigation of occurrence patterns of respiratory syncytial virus A and B in infected-patients from Cheonan, Korea.

BACKGROUND: Respiratory infections caused by viruses affect the lower respiratory tract; these infections are severe in patients with underlying diseases and can even lead to death. Respiratory syncytial virus (RSV), one of the causative agents of respiratory viral infections, is the most common cause of pneumonia and bronchiolitis in children and adults. METHODS: Respiratory specimens (nasopharyngeal aspirate, nasal swab, throat swab, etc.), which were sent to the Department of laboratory medicine from January 2012 to December 2018 for detection of respiratory viruses via real time reverse transcription PCR (Real time RT-PCR) were used in this study. RSV detected by real-time RT-PCR were analyzed on the basis of co-infection, sex and age of the patients, and year and month of sample collection. RESULTS: During the study period, we observed that the RSV detection rate was 12.8% (n = 1150/9010); the detection rate of RSV-A (7.1%) was higher than that of RSV-B (5.8%). The detection rate of RSV was the highest at 36.5% in December, and RSV-A and RSV-B were in vogue every year. Co-infection rate of RSVs was the highest in the patients over 80 years of age; RSVs showed the highest Co-infection with Rhinoviruses. CONCLUSIONS: During the study period, prevalence was different among the two subtypes of RSV, and the average age of RSV-B-positive patients was higher than that of RSV-A. Co-infection rate tended to increase every year. RSVs cause mild as well as severe infections. There are reports of serious clinical progress as RSVs cause overlapping infections with other viruses and increase the risk of secondary bacterial infections. Thus, further research on RSV should be done.