Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010-2018.

In recent years, there have been a significant influenza activity and emerging influenza strains in China, resulting in an increasing number of influenza virus infections and leading to public health concerns. The aims of this study were to identify the epidemiological and aetiological characteristics of influenza and establish seasonal autoregressive integrated moving average (SARIMA) models for forecasting the percentage of visits for influenza-like illness (ILI%) in urban and rural areas of Shenyang. Influenza surveillance data were obtained for ILI cases and influenza virus positivity from 18 sentinel hospitals. The SARIMA models were constructed to predict ILI% for January-December 2019. During 2010-2018, the influenza activity was higher in urban than in rural areas. The age distribution of ILI cases showed the highest rate in young children aged 0-4 years. Seasonal A/H3N2, influenza B virus and pandemic A/H1N1 continuously co-circulated in winter and spring seasons. In addition, the SARIMA (0, 1, 0) (0, 1, 2)12 model for the urban area and the SARIMA (1, 1, 1) (1, 1, 0)12 model for the rural area were appropriate for predicting influenza incidence. Our findings suggested that there were regional and seasonal distinctions of ILI activity in Shenyang. A co-epidemic pattern of influenza strains was evident in terms of seasonal influenza activity. Young children were more susceptible to influenza virus infection than adults. These results provide a reference for future influenza prevention and control strategies in the study area.

Correlation between the migration scale index and the number of new confirmed coronavirus disease 2019 cases in China.

In late December 2019, patients of atypical pneumonia due to an unidentified microbial agent were reported in Wuhan, Hubei Province, China. Subsequently, a novel coronavirus was identified as the causative pathogen which was named SARS-CoV-2. As of 12 February 2020, more than 44 000 cases of SARS-CoV-2 infection have been confirmed in China and continue to expand. Provinces, municipalities and autonomous regions of China have launched first-level response to major public health emergencies one after another from 23 January 2020, which means restricting movement of people among provinces, municipalities and autonomous regions. The aim of this study was to explore the correlation between the migration scale index and the number of confirmed coronavirus disease 2019 (COVID-19) cases and to depict the effect of restricting population movement. In this study, Excel 2010 was used to demonstrate the temporal distribution at the day level and SPSS 23.0 was used to analyse the correlation between the migration scale index and the number of confirmed COVID-19 cases. We found that since 23 January 2020, Wuhan migration scale index has dropped significantly and since 26 January 2020, Hubei province migration scale index has dropped significantly. New confirmed COVID-19 cases per day in China except for Wuhan gradually increased since 24 January 2020, and showed a downward trend from 6 February 2020. New confirmed COVID-19 cases per day in China except for Hubei province gradually increased since 24 January 2020, and maintained at a high level from 24 January 2020 to 4 February 2020, then showed a downward trend. Wuhan migration scale index from 9 January to 22 January, 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Wuhan from 22 January to 4 February. Hubei province migration scale index from 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Hubei province from 22 January to 4 February. Our findings suggested that people who left Wuhan from 9 January to 22 January, and those who left Hubei province from 10 January to 24 January, led to the outbreak in the rest of China. The 'Wuhan lockdown' and the launching of the first-level response to this major public health emergency may have had a good effect on controlling the COVID-19 epidemic. Although new COVID-19 cases continued to be confirmed in China outside Wuhan and Hubei provinces, in our opinion, these are second-generation cases.

Epidemiological study of scarlet fever in Shenyang, China.

BACKGROUND: Since 2011, there has been an increase in the incidence of scarlet fever across China. The main objective of this study was to depict the spatiotemporal epidemiological characteristics of the incidence of scarlet fever in Shenyang, China, in 2018 so as to provide the scientific basis for effective strategies of scarlet control and prevention. METHODS: Excel 2010 was used to demonstrate the temporal distribution at the month level and ArcGIS10.3 was used to demonstrate the spatial distribution at the district/county level. Moran's autocorrelation coefficient was used to examine the spatial autocorrelation and the Getis-Ord statistic was used to determine the hot-spot areas of scarlet fever. RESULTS: A total of 2314 scarlet fever cases were reported in Shenyang in 2018 with an annual incidence of 31.24 per 100,000. The incidence among males was higher than that among females(p<0.001). A vast majority of the cases (96.89%) were among children aged 3 to 11 years. The highest incidence was 625.34/100,000 in children aged 5-9 years. In 2018 there were two seasonal peaks of scarlet fever in June (summer-peak) and December (winter-peak). The incidence of scarlet fever in urban areas was significantly higher than that in rural areas(p<0.001). The incidence of scarlet fever was randomly distributed in Shenyang. There are hotspot areas located in seven districts. CONCLUSIONS: Urban areas are the hot spots of scarlet fever and joint prevention and control measures between districts should be applied. Children aged 3-11 are the main source of scarlet fever and therefore the introduction of prevention and control into kindergarten and primary schools may be key to the control of scarlet fever epidemics.

The spatiotemporal epidemiological study on human brucellosis in shenyang, China from 2013 to 2022.

Background: Epidemiological characteristics of human brucellosis (HB) have changed over the last decade. In this study, we depicted the spatiotemporal features of HB in Shenyang, China, from 2013 to 2022 and the objective was to visualise spatiotemporal patterns and identify high-risk regions with the purpose to provide evidence for HB prevention and control. Methods: We performed an observational epidemiological study using HB data obtained from the National Notifiable Disease Reporting System (NNDRS). Joinpoint regression analysis was employed to determine the changing trends in the annual incidence. A vector boundary map of Shenyang was used to visualise spatial distribution. Spatial autocorrelation was identified using both global and local Moran's autocorrelation coefficients, while hotspot areas were determined using the Getis-Ord statistic. Results: A combined sum of 4103 HB cases were analysed, and the average level of annual incidence of HB was 5.52 per 100,000. The incidence of HB showed obvious seasonality, with a notable peak observed from April to July (summer peak). The annual incidence in Shenyang has been on the rise since 2013, with an annual percentage change (APC) of 6.39% (95%CI 1.29%, 12.39%). Xinmin County exhibited the most elevated average annual incidence rate, with Faku County ranking second. The average annual incidence in rural areas exhibited a significantly greater disparity compared to suburban areas (P < 0.001), whereas the incidence rate in suburban areas demonstrated a significantly higher contrast when compared to urban areas (P < 0.001). A clustered distribution of the annual incidence of HB was observed for all years from 2013 to 2022. Abnormally high values were found in suburban areas, and no abnormally high values were found after 2017. The low-low clustering areas were found in urban as well as suburban areas from 2013 to 2022. Hotspots (P < 0.05) were located in rural areas, while cold spots (P < 0.05) were found in both urban and suburban areas. Since 2020, there have been no hotspots in Shenyang. Conclusions: Rural areas are high-risk areas for HB and may be key to controlling HB epidemics. Although the annual incidence of HB in rural areas has increased, owing to the stability of spatial relationships and the disappearance of hotspots, there is little possibility of outbreaks; however, stricter monitoring should be applied in rural areas to prevent the emergence of new transmission routes.