RESUMEN
Objective:To analyze the epidemic characteristics of brucellosis in China from 2004 to 2018, in order to understand the development trend of brucellosis.Methods:The surveillance data of brucellosis in China from 2004 to 2018 were collected from National Public Health Science Data Center. Joinpoint regression was used to analyze the trend of brucellosis incidence in China and various provinces. Overall trends were estimated by the average annual percentage change (AAPC). Seasonal and trend decomposition using loess (STL) was used to analyze the seasonal characteristics of brucellosis in China and various provinces. The age-related thermodynamic diagram of incidence rate was used to analyze the characteristics of age-onset changes.Results:From 2004 to 2018, a total of 524 980 brucellosis cases and 16 deaths were reported nationwide, with a incidence rate of 2.61/100 000 and a case fatality rate of (3.05 × 10 -3)%. The incidence of brucellosis in China was on the rise (AAPC = 11.58%, 95% CI: 7.91% - 15.25%, P < 0.001). There was no significant trend of change in Inner Mongolia Autonomous Region, Shanxi and Shaanxi provinces ( P > 0.05). Tibet Autonomous Region showed a downward trend (AAPC = - 55.19%, P < 0.001). All other provinces were showing an upward trend (AAPC > 0, P < 0.05). The peak incidence in China occurred from April to June. In terms of provinces, the peak incidence in Hainan, Sichuan, Guizhou, Fujian and Anhui provinces occurred from April to August, the peak incidence in Chongqing and Shanghai cities occurred from June to August, and the peak incidence in other provinces was generally from April to June. There were reports of brucellosis cases in all age groups nationwide, and the age distribution showed an inverted "V" shape. The peak incidence occurred in the 50 - 54 years old (5.43/100 000), followed by the 60 - 64 years old (4.94/100 000). From 2004 to 2018, the top 3 age groups of incidence rate changed from 40 - 44, 50 - 54 and 35 - 39 years old in 2004 to 50 - 54, 60 - 64 and 55 - 59 years old in 2018. Conclusions:The incidence of brucellosis is on the rise nationwide and in most provinces from 2004 to 2018. The high incidence age is gradually changing to the elderly population.
RESUMEN
Objective:To analyze the epidemic characteristics and periodicity of hemorrhagic fever with renal syndrome (HFRS) in Jingzhou City, Hubei Province, and provide a basis for scientific prevention and control of HFRS in Jingzhou City.Methods:Retrospective analysis was used to collect HFRS case data and population data of Jingzhou City and 8 counties (cities, districts) within its jurisdiction, including Shashi District, Jingzhou District, Gongan County, Jianli City, Jiangling County, Shishou City, Honghu City, and Songzi City from 1962 to 2020, from the Archives of the Jingzhou Center for Disease Control and Prevention and the Infectious Disease Report Information Management System of the China Disease Control and Prevention Information System; and the epidemic characteristics of HFRS was analyzed in Jingzhou City and 8 counties (cities, districts) within its jurisdiction. The periodicity of HFRS onset was determined using wavelet analysis.Results:From 1962 to 2020, 18 936 HFRS cases were reported in Jingzhou City, with an average incidence rate of 5.95/100 000. There were a total of three epidemic peaks, namely from 1972 to 1973 (24.82/100 000, 24.84/100 000), 1983 (60.08/100 000), and 1995 (14.57/100 000). According to different regions, the high incidence areas of HFRS showed a phased transfer trend: from the 1960s to the 1970s, the Jiangbei area (Honghu City, Jianli City) was the highest incidence area; in the 1980s and 1990s, the high incidence areas were transferred to Jiangnan area (Songzi City, Shishou City, and Gongan County); after 2005, high incidence areas were relocated to Jiangbei area (Honghu City, Jianli City, Jiangling County). The wavelet analysis results showed that there were 12.30 and 21.77 years of HFRS epidemic cycles in Jingzhou City before 2000 ( P < 0.05); among them, the periodicity of Shashi District, Gongan County, Jiangling County, Shishou City, and Honghu City was relatively consistent with that of Jingzhou City, with epidemic cycles of about 12 or 22 years ( P < 0.05). Conclusions:Jingzhou City is currently at the peak of a 22-year epidemic cycle of HFRS, with Jiangbei area as the high incidence areas. The 12-year epidemic cycle in Jingzhou City has ended after 2000.
RESUMEN
Objective:To analyze the effects of seasonal autoregressive integrated moving average model (SARIMA), generalized additive model (GAM), and long-short term memory model (LSTM) in fitting and predicting the incidence of hemorrhagic fever with renal syndrome (HFRS), so as to provide references for optimizing the HFRS prediction model.Methods:The monthly incidence data of HFRS from 2004 to 2017 of the whole country and the top 9 provinces with the highest incidence of HFRS (Heilongjiang, Shaanxi, Jilin, Liaoning, Shandong, Hebei, Jiangxi, Zhejiang and Hunan) were collected in the Public Health Science Data Center (https://www.phsciencedata.cn/), of which the data from 2004 to 2016 were used as training data, and the data from January to December 2017 were used as test data. The SARIMA, GAM, and LSTM of HFRS incidence in the whole country and 9 provinces were fitted with the training data; the fitted model was used to predict the incidence of HFRS from January to December 2017, and compared with the test data. The mean absolute percentage error ( MAPE) was used to evaluate the model fitting and prediction accuracy. When MAPE < 20%, the model fitting or prediction effect was good, 20%-50% was acceptable, and > 50% was poor. Results:From the perspective of overall fitting and prediction effect, the optimal model for the whole country and Heilongjiang, Shaanxi, Jilin, Liaoning and Jiangxi was SARIMA ( MAPE was 19.68%, 20.48%, 44.25%, 19.59%, 23.82% and 35.29%, respectively), among which the fitting and prediction effects of the whole country and Jilin were good, and the rest were acceptable. The optimal model for Shandong and Zhejiang was GAM ( MAPE was 18.29% and 21.25%, respectively), the fitting and prediction effect of Shandong was good, and Zhejiang was acceptable. The optimal model for Hebei and Hunan was LSTM ( MAPE was 26.52% and 22.69%, respectively), and the fitting and prediction effects were acceptable. From the perspective of fitting effect, GAM had the highest fitting accuracy in the whole country data, with MAPE = 10.44%. From the perspective of prediction effect, LSTM had the highest prediction accuracy in the whole country data, with MAPE = 12.23%. Conclusions:SARIMA, GAM, and LSTM can all be used as the optimal models for fitting the incidence of HFRS, but the optimal models fitted in different regions show great differences. In the future, in the establishment of HFRS prediction models, as many alternative models as possible should be included for screening to ensure higher fitting and prediction accuracy.
RESUMEN
Objective To explore the applicability of the TBATS in predicting the incidence of mumps. Methods The incidence of mumps of Jiangxi Province from 2004 to 2017 was used as the demonstration data. The incidence of mumps in Jiangxi Province from July to December 2017 was used as test data. The training data from January 2004 to June 2017 were used to train the TBATS and the SARIMA, and predict the value from July to December 2017. The fitted and predicted values were compared with the test data. The MAPE, RMSE, MAE and MER were used to evaluate model fitting and prediction effects. Results SARIMA (1,0,0)(1,1,0)12 with drift was the optimal SARIMA. The MAPE, MAE, RMSE and MER fitted by the TBATS and the SARIMA were 15.06%, 0.21, 0.29, 13.57% and 21.93%, 0.29, 0.41, 18.73%, respectively. The MAPE, MAE, RMSE and MER predicted by the TBATS and the SARIMA were 7.95%, 0.08, 0.11, 7.12% and 15.33%, 0.17, 0.18, 14.93%. Conclusion The TBATS has high accuracy in predicting the incidence of mumps and is worthy of popularization and application.
RESUMEN
Objective To explore the optimal combination of parameters for the maximum spatial cluster size and maximum temporal cluster size of scan statistics. Methods The daily incidence data of hand-foot-and-mouth disease (HFMD) in Jingzhou in 2016 was collected as data source. The maximum spatial cluster sizes were set to 50%, 40%, 30%, 20%, and 10% of the population at risk. The maximum temporal cluster sizes were set to 7d, 14d, 30d, and 60d. A total of 20 parameter setting schemes were formed and spatial-temporal scanning was conducted one by one. The areas where the number of towns covered by the scanning area was less than 25 were selected, and the clustered epidemic of hand-foot-mouth disease can be detected at the same time in Xiejiaping Town of Songzi City and Sanzhou Town of Jianli County. The combination of large LLR and RR values was the optimal parameter setting. Results When the spatial windows were set to 20% of the population at risk, and the temporal windows were set to 30d, a total of 6 aggregation areas were detected. The number of covered townships was less than 25, and the clustered epidemic of Xiejiaping Township and Sanzhou Town were successfully detected. The LLR and RR values of the detected aggregation area were relatively large. This combination was the optimal parameter setting. Conclusion The combination of different parameters has a significant impact on the results of spatial-temporal scan statistics. It is recommended that parameters be optimized before applying this method.
RESUMEN
Objective To investigate the spatial correlation and spatial cluster pattern of hemorrhagic fever with renal syndrome (HFRS) in Jingzhou City,Hubei Province from 2013 to 2017.Methods The HFRS surveillance data during 2013-2017 were collected from China Disease Prevention and Control Information System.Software ArcGIS 10.3 was used to analyze the spatial distribution,and global autocorrelation analysis (Moran'sI) and hot spot analysis (Getis-Ord Gi) were used to analyze the spatial autocorrelation.Spatial cluster pattern was explored by trend surface analysis and directional distribution.Results In 2013-2017,the global Moran's I was 0.117 6 (P ＞ 0.05),0.349 8 (P ＜ 0.05),0.102 1 (P ＞ 0.05),0.276 3 (P ＜ 0.05),and 0.394 8 (P ＜ 0.05),respectively.The Getis-Ord Gi analysis showed that there were 7,8,8,8,15 hot areas with high incidence of HFRS during this period,respectively,which were part of townships in Jiangling County,Shashi District,Jianli County,and Honghu City.The cold spot area with low incidence of HFRS was only detected in 2015,and it was part of the township in Shashi District and Jingzhou District.The trend surface analysis showed that the inverted-U type curve could reflect the HFRS distribution from northern to southern,and it was also from eastern to western.The directional distribution showed that the HFRS cases were distributed in the north-central part of Jingzhou in 2013-2017,and they were inconsistent with the distribution of the Yangtze River system.Conclusions The incidence of HFRS has an obvious spatial clustering characteristic,and the areas at high risk are mainly in the north-central part of Jingzhou City.The spatial cluster pattern of HFRS has nothing to do with the Yangtze River system.
RESUMEN
Objective@#To analyze the changes of the characteristics of Hemorrhagic fever with renal syndrome (HFRS) in Jingzhou City in different periods.@*Methods@#According to the HFRS epidemic data of Jingzhou City in 2009-2018, based on the incidence rate, the HFRS epidemic situation in Jingzhou City was divided into three periods: 2009-2012 (low), 2013-2016 (middle), and 2017-2018 (high). Descriptive epidemiological methods, standard deviation ellipse and spatio-temporal scanning analysis were used to analyze the time, region, population distribution and temporal and spatial trends of HFRS epidemic in the three periods.@*Results@#The incidence of HFRS in Jingzhou City in the three periods was seasonal and bimodal. The peak incidence included spring and summer peaks (May-July) and autumn-winter peaks (January, November-December). The HFRS cases in Jingzhou City were concentrated in Jianli County, Jiangling County and Honghu City in the three periods. The incidence rates were 0.48/100 000, 1.98/100 000, 0.84/100 000, 0.89/100 000, 1.88/100 000, 1.20/100 000; 4.82/100 000, 13.37/100 000, and 4.58/100 000. The incidence of HFRS in males was higher than that in females in the three periods (χ2=43.38, P < 0.05); the occupations of HFRS in the three periods were mainly farmers, which were 56.26%(69/122), 69.61% (126/181), 74.94% (293/391), respectively. In 116 farmers, growing rice [48.28% (56/116)] and shrimp rice [27.59% (32/116)] were mostly. From the age point of view, the incidence rate in 2009-2017 was 55 to 64 years old; the incidence rate of 2018 was 60 to 69 years old. The results of standard deviation ellipse analysis showed that the expansion trend of HFRS epidemic areas in Jingzhou City was not obvious, and the center of gravity was located in Jianli County or Jiangling County. Spatio-temporal scans revealed that the first-class spatial-temporal clustering areas in the three periods were 2 towns and villages in Jiangling County, and the gathering time was from December 7, 2010 to January 2, 2011; in some townships in Jiangling County and Shacheng District, the gathering time was from December 7, 2016 to February 28, 2017; some townships in Jiangling County and surrounding counties, gathered from April 27, 2018 to July 16, 2018.@*Conclusions@#The HFRS epidemic season in Jingzhou City in different periods is basically the same; the high-incidence areas are basically the same, but there are local fluctuations; the population is mainly male farmers, and the age of high-incidence has shifted back. We should adapt to local conditions and formulate scientific and reasonable comprehensive prevention and control measures.
RESUMEN
Objective To analyze the changes of the characteristics of Hemorrhagic fever with renal syndrome (HFRS) in Jingzhou City in different periods. Methods According to the HFRS epidemic data of Jingzhou City in 2009 - 2018, based on the incidence rate, the HFRS epidemic situation in Jingzhou City was divided into three periods: 2009 - 2012 (low), 2013 - 2016 (middle), and 2017 - 2018 (high). Descriptive epidemiological methods, standard deviation ellipse and spatio-temporal scanning analysis were used to analyze the time, region, population distribution and temporal and spatial trends of HFRS epidemic in the three periods. Results The incidence of HFRS in Jingzhou City in the three periods was seasonal and bimodal. The peak incidence included spring and summer peaks (May - July) and autumn-winter peaks (January, November - December). The HFRS cases in Jingzhou City were concentrated in Jianli County, Jiangling County and Honghu City in the three periods. The incidence rates were 0.48/100000, 1.98/100000, 0.84/100000, 0.89/100000, 1.88/100000, 1.20/100000; 4.82/100000, 13.37/100000, and 4.58/100000. The incidence of HFRS in males was higher than that in females in the three periods (χ2 = 43.38, P < 0.05); the occupations of HFRS in the three periods were mainly farmers, which were 56.26％(69/122), 69.61％ (126/181), 74.94％ (293/391), respectively. In 116 farmers, growing rice [48.28％ (56/116)] and shrimp rice [27.59％ (32/116)] were mostly. From the age point of view, the incidence rate in 2009 - 2017 was 55 to 64 years old; the incidence rate of 2018 was 60 to 69 years old. The results of standard deviation ellipse analysis showed that the expansion trend of HFRS epidemic areas in Jingzhou City was not obvious, and the center of gravity was located in Jianli County or Jiangling County. Spatio-temporal scans revealed that the first-class spatial-temporal clustering areas in the three periods were 2 towns and villages in Jiangling County, and the gathering time was from December 7, 2010 to January 2, 2011; in some townships in Jiangling County and Shacheng District, the gathering time was from December 7, 2016 to February 28, 2017; some townships in Jiangling County and surrounding counties, gathered from April 27, 2018 to July 16, 2018. Conclusions The HFRS epidemic season in Jingzhou City in different periods is basically the same; the high -incidence areas are basically the same, but there are local fluctuations; the population is mainly male farmers, and the age of high-incidence has shifted back. We should adapt to local conditions and formulate scientific and reasonable comprehensive prevention and control measures.