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Coronavirus disease (COVID-19) has been spreading all over the world; however, its incidence and case-fatality ratio differ greatly between countries and between continents. We investigated factors associated with international variation in COVID-19 incidence and case-fatality ratio (CFR) across 107 northern hemisphere countries, using publicly available COVID-19 outcome data as of 14 September 2020. We included country-specific geographic, demographic, socio-economic features, global health security index (GHSI), healthcare capacity, and major health behavior indexes in multivariate models to explain this variation. Multiple linear regression highlighted that incidence was associated with ethnic region (p p $${R}^{2}$$ R 2 = 0.545) and CFR (partial $${R}^{2}$$ R 2 = 0.372), even after adjusting for various confounding factors.

INTRODUCTION: While the reduction in influenza cases in the Northern hemisphere in 2020 has been widely reported, the influenza transmission dynamics in the Southern hemisphere remain uncharacterized. METHODS: This study analysed the change in influenza-positive proportion (IPP) between 2010-2019 and 2020 in countries in the Southern hemisphere with ≤40% missing IPP data in FluNet to assess how coronavirus disease 2019 (COVID-19) relates to influenza activity. The analysis considered the incidence of COVID-19 reported by the World Health Organization and the implementation date of non-pharmaceutical interventions (NPIs) reported by the Oxford COVID-19 Government Response Tracker. RESULTS: In each of the seven included countries, the average IPP was lower in 2020 than in 2010-2019 (P < 0.01), with the largest difference being 31.1% (95% confidence interval 28.4-33.7%). In Argentina, Bolivia, Chile and South Africa, higher IPPs were observed during epidemiological weeks 4-16 in 2020 compared with the same weeks in 2010-2019. The IPP increased after NPIs were implemented in Argentina and South Africa, but started to decline in Bolivia, Chile, Madagascar and Paraguay before NPI implementation. CONCLUSIONS: Influenza burden and activity decreased in 2020 in the Southern hemisphere. The temporal decline in influenza activity varied between countries.

Background Despite the etiology of Kawasaki Disease (KD) remains unknown, identifying spatiotemporal clusters of KD could support its hypothesis to be infectious disease. However, spatiotemporal clusters of KD have never been reported in South Korea. Therefore, this study aims to analyse the spatiotemporal patterns of KD in South Korea using national representative data. Methods We extracted epidemiologic-week (Epi-week) based KD case (ICD-10-CM code, M303) from 2008-2017 national health insurance service data in municipal level. To determine whether spatial autocorrelation and municipal-level clusters exist, year, sex, and age group ( Results A total of 98,503 cases of KD were reported in 2008-2017 (aged Conclusions South Korea had steady increase in KD incidence since 2008 and municipal-level clusters throughout 2008-2017. Key messages Kawasaki disease in South Korea had spatiotemporal patterns.

Background The prevalence of dementia is increasing worldwide; and dementia deaths are also increasing. In South Korea, deaths from Alzheimer's disease (AD) ranked 9th in the 2018 cause of death statistics, and the ranking has risen. Methods We collected 2009 to 2018 AD deaths and related data from Statistics Korea and Community Health Survey. SaTScan software was used for detecting and evaluating the clusters with high risk in AD deaths, and R version 4.0.3 was used for mapping and analysis of affecting factors. Results A total of 39,412 people died from AD during 10 years in Korea, 7.7 per 100,000 inhabitants. While 98.7% of deaths were above-65 age, the proportion of under 65 was also rising. The global Moran index of 10 years’ AD deaths was 0.2 (P Conclusions Some clusters of AD deaths were identified, and they were influenced by institutional factors. It is necessary that the local government considers characteristics of each region when implementing the dementia policies. Key messages AD deaths in South Korea were increasing from 2009 to 2018, and their clusters were observed in southern regions. AD deaths in 2018 were influenced by dementia-related policies.

Background Personal health behaviours, which rely on community characteristics, could affect individual vulnerability on disease infection. Due to insufficient study to examine health behaviours as risk factors of COVID-19 infection, we conducted municipal level spatial analysis to investigate association between health behaviours and COVID-19 incidence. Methods We extracted cumulative COVID-19 incidence data from January 20th 2020 to February 25th 2021, health behaviours, health condition, socio-economic factors, and covariates in municipal level from publicly available dataset. We chose variables, which were standardized, considering multicollinearity (VIF Results The mean cumulative COVID-19 incidence per 10,000 population among 229 municipality was 13.73 (Standard deviation=11.43). iCAR model (DIC=2,825.3) outperformed BYM model (DIC=14,009.4). The results of iCAR model highlighted that incidence was associated with dental hygiene practice (incidence risk ratios [IRR]=0.92, 95% Credible Interval [CI]=0.85–1.00), whether tried to be thin (IRR=1.10, 95% CI = 1.00–1.20), proportion of medical personnel (IRR=1.09, 95% CI = 1.01–1.17), and volume of public transportation (IRR=1.19, 95% CI = 1.05–1.35), even after adjusting for various confounding factors. Conclusions Municipality with lower cumulative incidence was likely to have more people who practiced to keep dental hygiene and less people who tried to be thin. Key messages Municipal level spatial analysis resulted that health behaviours were associated with COVID-19 incidence in South Korea.

Background Diabetes is emerging as one of the complications of coronavirus disease 2019 (COVID-19), but this is hard to be revealed with cross-sectional studies since it is also known as the major predisposing factor for high-risk COVID-19. Therefore, this study aimed to estimate the risk of new-onset diabetes after COVID-19 through a population follow-up study. Methods All COVID-19 confirmed cases in Korea from January 20 to June 4, 2020, were matched with national health insurance data and their health screening data, both provided by the National Health Insurance Service of Korea. Controls were selected as the people who received the PCR test for COVID-19 and showed negative results in the same period and followed up until July 19, 2020. We selected the outcome as the diagnosis of diabetes according to the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10, E10 – E14). People who were diagnosed with diabetes in the past five years were excluded from both groups. After performing a log-rank test between groups, adjusted incidence rate and hazard ratio were estimated using Cox proportional hazard modeling. Demographic characteristics (age, sex, region, family histories of hypertension/diabetes, and income) and underlying health conditions such as hypertension, dyslipidemia, heart disease, alcohol consumption, cigarette smoking, and BMI were adjusted. Proportional assumptions were tested by the zph test and the sensitivity analysis by excluding each factor in turn and comparing results. Results A total of 6,247 COVID-19 patients and 143,594 controls without diabetes in the past were included for the analysis. The number of new-onset diabetes were 759 (12.15%) in COVID-19 patients and 3,465 (2.41%) in controls (P < 0.01). The adjusted incidence of diabetes was 15.34 (95% confidence interval, CI: 14.10 – 16.66) and 11.18 (95% CI: 10.67 – 11.72) per 100 person-year, respectively, with the mean follow-up time as 46.31 (standard deviation: 16.37) days. The adjusted hazard ratio of diabetes in COVID-19 cases was 2.97 (95% CI: 2.44 – 3.63). Conclusion Since COVID-19 patients showed a higher incidence of new-onset diabetes in a short-time follow-up, we should consider diabetes as one of the possible complications of COVID-19. Disclosures All Authors: No reported disclosures

The preventive measures to control the coronavirus disease (COVID-19) pandemic could affect other virus outbreaks. However, any changes in their incidence have not been evaluated. This study aimed to determine how the COVID-19 management policy has influenced the positive rates of other respiratory viruses. We collected data from the weekly reports of Korea Influenza and Respiratory Viruses Surveillance System on eight targeted viruses—adenovirus, human bocavirus, human coronavirus, human metapneumovirus, human rhinovirus, influenza virus, parainfluenza virus, and respiratory syncytial virus—from weeks 1 to 18 per year for the past 10 years. We compared the mean of each week in 2020 with the past 10 years using paired t-test. The study period was divided based on the date of the first COVID-19 case in South Korea (before: weeks 1–4; after: weeks 5–18). The overall positive rate of the respiratory viral infection was 38.7% in 2020 and 62.0% in 2010–2019. The positive rates of respiratory viruses with seasonality—hRV, IFV, and PIV—decreased during the COVID-19 pandemic. Although the positive rate of RSV increased significantly in the early weeks, the changes after week 5 were not significant. This phenomenon may be attributed to the strict COVID-19 control measures.

Although coronavirus disease 2019 (COVID-19) is an ongoing pandemic, the mean serial interval was measured differently across nations. Through the Korean national COVID-19 contact tracing system, we were able to investigate personal contacts in all symptomatic cases in Korea from January 20 to August 3, 2020. The mean serial interval was calculated by the duration between the symptom onset of the infector and infectee, and became shorter after the case definition changed to include not-imported cases in Korea on February 20, 2020. The mean serial interval before and after this fifth case definition was 6.12 and 3.93 days based on the infectors' symptom onset date, respectively, and 4.02 days in total with the median of 3 days. Older age and women lead to longer serial intervals., Graphical Abstract

Background Since varicella epidemics repeatedly occurred in Korea, it is essential to control varicella outbreaks preemptively in the targeted region. Therefore, we aimed to reveal spatiotemporal clusters of varicella and the regional risk factor of varicella incidence at the national level. Methods All varicella cases (defined as ICD-10 codes, B01-B09) from 2013 to 2017 in Korea were extracted from National Health Insurance Service. Of the total, 566,978 cases were realigned spatially by 250 administrative districts of Korea and temporally by a week. Spatial autocorrelation was tested using the global Moran’s I statistics using Monte Carlo simulation. Kulldorff’s prospective space-time scan statistics were used to reveal space-time clusters of varicella. Possible risk factors were extracted from the Korean Statistical Information Service and Community Health Survey of Korea, including hand hygiene perceptions, alcohol and smoking status, the proportion of children under 15 years old, the number of households, and household income by regions. After selecting significant risk factors through non-spatial generalized linear models, a conditional autoregressive spatiotempoal model with Bayesian extension was applied to estimate the regional factors affecting varicella incidence. Results There was spatial autocorrelation using Global Moran’s I statistics (P< 0.01). When the maximum cluster size was limited to 10% of the population, 17 spatiotemporal clusters were detected in specific regions in Korea (figure 1). Low perception of hand hygiene, the high proportion of alcohol drinking and cigarette smoking, high children proportion, low number of familial member, and low household income were associated with varicella spatiotemporal incidence (odds ratio: 0.97, 1.01, 2.31, 1.10, 0.99, 0.99, respectively; 95% credible intervals of all risk factors did not include 1). Figure 1. Space-time prospective clusters of varicella in Korea using varicella incidence from 2013 to 2017. Relative risks ratio of each cluster is described at the point. Conclusion Varicella incidence shows spatiotemporal clustering patterns in specific regions. Since regional factors such as the perception rate of hand hygiene, child proportion, alcohol drinking, cigarette smoking, and low household income affect varicella’s spatiotemporal incidence, strategies for targeted control of high-risk regions are strongly recommended. Disclosures All Authors: No reported disclosures