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ObjectiveTo explore the application of moving epidemic method (MEM) in early warning of influenza incidence intensity in winter-spring season in Ningbo city and to provide a reference for developing effective intervention measures. MethodsInfluenza surveillance data for the period of January 2013 – May 2022 were collected from 2 national influenza sentinel hospitals in Ningbo city, Zhejiang province and a part of the data on positive rate of influenza virus detection among the registered cases in winter-spring seasons from January 2013 to December 2019 were extracted to establish a MEM model. The constructed MEM model was adopted to analyze the beginning, the end and incidence intensity of seasonal influenza epidemic during 2019 – 2020 (40th week, 2019 – 20th week, 2020) in Ningbo city; the model analysis results were compared with actual situation of the epidemic. ResultsFor the virus-positive cases in the city during the 2013 – 2022, the proportions of isolated viral strains were 24.59% for influenza A (H1N1), 37.03% for influenza A (H3N2), and 38.38% for influenza B (mainly Victoria and Yamagata strain), respectively. The established MEM model showed a better goodness-of-fit, with the parameter δ of 2.7, the sensitivity of 87.97%, the specificity of 87.68%, and the Yoden index of 0.76. Based on the fitting results of the established MEM model, the thresholds of virus-positive rate were 22.76% and 25.05% for identifying the onset and the end of the influenza epidemic and the thresholds were 43.18%, 63.22%, and 74.83% for indicating a moderate-, high-, extremely high-intensity of the influenza epidemic during 2019 – 2020 influenza season. With the established MEM threshold estimations, the trajectory of the influenza could described as following: pre-epidemic stage from 40th week to 48th week of 2019, onset/low-intensity stage from 49th week of 2019, moderate-intensity stage from 51th week of 2019 to third week of 2020, subsequent low-intensity stage from 4th week of 2020, late stage from 7th week of 2020, and the end of the epidemic by the 10th week of 2020. Compared to the seasonal epidemics between 2013 – 2019, the 2019 – 2020 winter spring influenza epidemic occurred one week earlier but ended three weeks sooner. Conclusion MEM model could be adopted effectively in early identification and intensity warning of seasonal influenza epidemic.

BackgroundInfluenza infection causes a huge burden every year, affecting approximately 8% of adults and approximately 25% of children and resulting in approximately 400,000 respiratory deaths worldwide. However, based on the number of reported influenza cases, the actual prevalence of influenza may be greatly underestimated. The purpose of this study was to estimate the incidence rate of influenza and determine the true epidemiological characteristics of this virus.MethodsThe number of influenza cases and the prevalence of ILIs among outpatients in Zhejiang Province were obtained from the China Disease Control and Prevention Information System. Specimens were sampled from some cases and sent to laboratories for influenza nucleic acid testing. Random forest was used to establish an influenza estimation model based on the influenza-positive rate and the percentage of ILIs among outpatients. Furthermore, the moving epidemic method (MEM) was applied to calculate the epidemic threshold for different intensity levels. Joinpoint regression analysis was used to identify the annual change in influenza incidence. The seasonal trends of influenza were detected by wavelet analysis.ResultsFrom 2009 to 2021, a total of 990,016 influenza cases and 8 deaths were reported in Zhejiang Province. The numbers of estimated influenza cases from 2009 to 2018 were 743,449, 47,635, 89,026, 132,647, 69,218, 190,099, 204,606, 190,763, 267,168 and 364,809, respectively. The total number of estimated influenza cases is 12.11 times the number of reported cases. The APC of the estimated annual incidence rate was 23.33 (95% CI: 13.2 to 34.4) from 2011 to 2019, indicating a constant increasing trend. The intensity levels of the estimated incidence from the epidemic threshold to the very high-intensity threshold were 18.94 cases per 100,000, 24.14 cases per 100,000, 141.55 cases per 100,000, and 309.34 cases per 100,000, respectively. From the first week of 2009 to the 39th week of 2022, there were a total of 81 weeks of epidemics: the epidemic period reached a high intensity in 2 weeks, the epidemic period was at a moderate intensity in 75 weeks, and the epidemic period was at a low intensity in 2 weeks. The average power was significant on the 1-year scale, semiannual scale, and 115-week scale, and the average power of the first two cycles was significantly higher than that of the other cycles. In the period from the 20th week to the 35th week, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were − 0.089 (p = 0.021), 0.497 (p

Background and Objectives: Timely dection of influenza outbreaks is very important for a better reponse to outbreaks. The aim of this study was to assess the moving epidemic method for determinating influenza outbreak threshold levels in Iran. Methods: The moving epidemic method was applied to the WHO Flunet website data from January 2010 to December 2017 with the aim of determining outbreak alert thresholds. Therefore, the performance of the moving epidemic method in identifying the onset of the outbreak over eight periods was reported using sensitivity and specificity indices. Results: A total of 69169 influenza-like illnesses were recorded in the Fluent website during the study period. The smallest number of influenza-like illnesses for the epidemic was 187 cases in the third period (2011-2012) and the maximum number was 1086 cases in the seventh period (2016-2015). The earliest epidemic occurred during the third and eighth periods of the 48th week, approximately in mid-February. The minimum duration of the epidemic was 8 weeks and the maximum was 13 weeks. The overall sensitivity and specificity of the moving epidemic method were 55% and 96%, respectively. Conclusion: The moving epidemic method could be used as a complementary method to detection of influenza outbreks. It is recommended to apply the moving epidemic method on influenza and similar communicable diseses.

Objectives: The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China. Methods: Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure. Results: For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%. Conclusions: Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

• Use of the moving epidemic method (MEM) for detecting influenza epidemic thresholds in the subtropics was explored. • Using influenza virus stratified surveillance data with the MEM was well implemented. • The MEM could detect an influenza epidemic 1 week in advance in Guangdong, China. • Local diverse seasonal patterns of influenza subtypes in Guangdong were revealed. The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China. Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure. For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%. Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds. [ABSTRACT FROM AUTHOR]

Background: Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. Methods: We applied the moving epidemic method (MEM) using the R Language implementation (package "mem"). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. Results: The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. Conclusions: This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. [ABSTRACT FROM AUTHOR]

Background: Influenza infection causes a huge burden every year, affecting approximately 8% of adults and approximately 25% of children and resulting in approximately 400,000 respiratory deaths worldwide. However, based on the number of reported influenza cases, the actual prevalence of influenza may be greatly underestimated. The purpose of this study was to estimate the incidence rate of influenza and determine the true epidemiological characteristics of this virus., Methods: The number of influenza cases and the prevalence of ILIs among outpatients in Zhejiang Province were obtained from the China Disease Control and Prevention Information System. Specimens were sampled from some cases and sent to laboratories for influenza nucleic acid testing. Random forest was used to establish an influenza estimation model based on the influenza-positive rate and the percentage of ILIs among outpatients. Furthermore, the moving epidemic method (MEM) was applied to calculate the epidemic threshold for different intensity levels. Joinpoint regression analysis was used to identify the annual change in influenza incidence. The seasonal trends of influenza were detected by wavelet analysis., Results: From 2009 to 2021, a total of 990,016 influenza cases and 8 deaths were reported in Zhejiang Province. The numbers of estimated influenza cases from 2009 to 2018 were 743,449, 47,635, 89,026, 132,647, 69,218, 190,099, 204,606, 190,763, 267,168 and 364,809, respectively. The total number of estimated influenza cases is 12.11 times the number of reported cases. The APC of the estimated annual incidence rate was 23.33 (95% CI: 13.2 to 34.4) from 2011 to 2019, indicating a constant increasing trend. The intensity levels of the estimated incidence from the epidemic threshold to the very high-intensity threshold were 18.94 cases per 100,000, 24.14 cases per 100,000, 141.55 cases per 100,000, and 309.34 cases per 100,000, respectively. From the first week of 2009 to the 39th week of 2022, there were a total of 81 weeks of epidemics: the epidemic period reached a high intensity in 2 weeks, the epidemic period was at a moderate intensity in 75 weeks, and the epidemic period was at a low intensity in 2 weeks. The average power was significant on the 1-year scale, semiannual scale, and 115-week scale, and the average power of the first two cycles was significantly higher than that of the other cycles. In the period from the 20th week to the 35th week, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were - 0.089 ( p  = 0.021), 0.497 ( p  < 0.001), -0.062 ( p  = 0.109) and - 0.084 ( p  = 0.029), respectively. In the period from the 36th week of the first year to the 19th week of the next year, the Pearson correlation coefficients between the time series of influenza onset and the positive rate of pathogens, including A(H3N2), A (H1N1)pdm2009, B(Victoria) and B(Yamagata), were 0.516 ( p  < 0.001), 0.148 ( p  < 0.001), 0.292 ( p  < 0.001) and 0.271 ( p  < 0.001), respectively., Conclusion: The disease burden of influenza has been seriously underestimated in the past. An appropriate method for estimating the incidence rate of influenza may be to comprehensively consider the influenza-positive rate as well as the percentage of ILIs among outpatients. The intensity level of the estimated incidence from the epidemic threshold to the very high-intensity threshold was calculated, thus yielding a quantitative standard for judging the influenza prevalence level in the future. The incidence of influenza showed semi-annual peaks in Zhejiang Province, including a main peak from December to January of the next year followed by a peak in summer. Furthermore, the driving factors of the influenza peaks were preliminarily explored. While the peak in summer was mainly driven by pathogens of A(H3N2), the peak in winter was alternately driven by various pathogens. Our research suggests that the government urgently needs to address barriers to vaccination and actively promote vaccines through primary care providers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Xue, Wu, Ding, Wang, Fu, Yang, Lin and Lu.)

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Background: School-aged children were reported to act as the main transmitter during influenza epidemic seasons. It is vital to set up an early detection method to help with the vaccination program in such a high-risk population. However, most relative studies only focused on the general population. Our study aims to describe the influenza epidemiology characteristics in Hubei Province and to introduce the moving epidemic method to establish the epidemic thresholds for age-specific groups., Methods: We divided the whole population into pre-school, school-aged and adult groups. The virology data from 2010/2011 to 2017/2018 were applied to the moving epidemic method to establish the epidemic thresholds for the general population and age-specific groups for the detection of influenza in 2018/2019. The performances of the model were compared by the cross-validation process., Results: The epidemic threshold for school-aged children in the 2018/2019 season was 15.42%. The epidemic thresholds for influenza A virus subtypes H1N1 and H3N2 and influenza B were determined as 5.68%, 6.12% and 10.48%, respectively. The median start weeks of the school-aged children were similar to the general population. The cross-validation process showed that the sensitivity of the model established with school-aged children was higher than those established with the other age groups in total influenza, H1N1 and influenza B, while it was only lower than the general population group in H3N2., Conclusions: This study proved the feasibility of applying the moving epidemic method in Hubei Province. Additional influenza surveillance and vaccination strategies should be well-organized for school-aged children to reduce the disease burden of influenza in China.

Objective: To evaluate the incidence intensity of hand, foot, and mouth disease (HFMD) in 2018/2019 season in southern China by Moving Epidemic Method (MEM), and compare the intensity among provinces, so as to provide basis for optimizing the allocation of public health resources. Methods: The weekly incidence data of HFMD of children under 5 years old in 15 provinces of southern China from March 1, 2012 to February 28, 2019 were collected from Disease Surveillance Reporting System of Chinese Center for Disease Control and Prevention, and the epidemic intensity threshold of each province in southern China during this period was calculated and evaluated by MEM. Results: In the first incidence peak of 2018/2019 HFMD season, in 15 provinces in the south China, 6 provinces (Jiangsu, Zhejiang, Jiangxi, Chongqing, Sichuan and Yunnan) reported very high incidence rates in children under 5 years old while Guangdong, Guangxi and Hainan provinces had low incidence level. In the second incidence peak, the incidences in 6 provinces (Shanghai, Jiangsu, Zhejiang, Chongqing, Sichuan and Yunnan) reached very high levels. The incidences in remaining provinces also reached medium or high levels. In most provinces, the thresholds in the first incidence peak were higher than those in the second incidence peak, but Chongqing and Sichuan were different. The results of model validation showed that the sensitivity and specificity of MEM were higher than 70% except for Hainan, Chongqing and Yunnan. Conclusions: For southern provinces with two incidence peaks in HFMD season, MEM can be used to determine the epidemic intensity thresholds of different incidence peaks by dividing the disease season to analyze the incidence intensity of HFMD in different stages. The epidemic intensity threshold established by MEM integrates the historical data, and the province (city) with extremely high epidemic level identified represents that the province (city) has an abnormal increase compared with the historical incidence level, which requires more attention from all areas and timely implementation of prevention and control measures.

Objective: To calculate both the epidemic and intensity thresholds for different levels in Beijing and to establish a tiered alert system in the 2018-2019 influenza season as well as to evaluate the performance of calculated thresholds. Method: Weekly count of influenza-like illness and percentage of influenza-like illness (ILI % ) of the last five influenza seasons were modeled by 'moving epidemic method' (MEM) to calculate the influenza epidemic and intensity thresholds at different levels. A cross-validation procedure was used to evaluate the performance. Indicators of Matthew correlation coefficient, Youden's index, sensitivity and specificity were calculated. Results: For weekly count of influenza-like illness, data showed that the epidemic threshold for 2018-2019 influenza season was 12 984 and the medium, high and very high intensity thresholds were 22 503, 37 589, 47 157, respectively. Matthew correlation coefficient of the epidemic threshold was 62 % and youden's index as 60 % , sensitivity as 69 % , specificity as 91 % . Data on weekly ILI % , the epidemic threshold for 2018-2019 influenza season was 1.66 % , with medium, high and very high intensity thresholds as 2.46 % , 3.84 % and 4.66 % , respectively. The overall Matthew correlation coefficient of the epidemic threshold was 59 % , with 54 % for the Youden's index, sensitivity as 60 % and specificity as 94 % . Conclusions: MEM produced a good specific signal for detecting the influenza epidemics and the accuracy of the method was acceptable. The early warning performance regarding the application of weekly count on influenza-like illness was slightly better than ILI % . This method could be applied in the practical influenza epidemic alert "work in Beijing" .

Influenza and respiratory syncytial virus (RSV) are common causes of respiratory tract infections and place a burden on health services each winter. Systems to describe the timing and intensity of such activity will improve the public health response and deployment of interventions to these pressures. Here we develop early warning and activity intensity thresholds for monitoring influenza and RSV using two novel data sources: general practitioner out-of-hours consultations (GP OOH) and telehealth calls (NHS 111). Moving Epidemic Method (MEM) thresholds were developed for winter 2017-2018. The NHS 111 cold/flu threshold was breached several weeks in advance of other systems. The NHS 111 RSV epidemic threshold was breached in week 41, in advance of RSV laboratory reporting. Combining the use of MEM thresholds with daily monitoring of NHS 111 and GP OOH syndromic surveillance systems provides the potential to alert to threshold breaches in real-time. An advantage of using thresholds across different health systems is the ability to capture a range of healthcare-seeking behaviour, which may reflect differences in disease severity. This study also provides a quantifiable measure of seasonal RSV activity, which contributes to our understanding of RSV activity in advance of the potential introduction of new RSV vaccines. [ABSTRACT FROM AUTHOR]

Objective: We planned to evaluate the effectiveness of moving epidemic method (MEM) in calculating influenza epidemic threshold of 7 climatic zones in China mainland. Methods: The positive rate of influenza virus was obtained from the National Influenza Surveillance Network System from 2010/2011 to 2017/2018. We divided the 31 provinces into 7 climatic zones according to previous literatures and applied MEM to calculate the influenza epidemic threshold of 2018/2019 influenza season for these climatic zones. Sensitivity, specificity, positive predictive value and negative predictive value were calculated to evaluate the effectiveness of MEM. Results: Pre-epidemic threshold (the positive rate of influenza virus) varied from 9.66% (temperate zone) to 16.36% (subtropical zone) for 2018/2019 influenza season. The gap between pre-epidemic and post-epidemic thresholds was less than 5% except for plateau zone. The sensitivity was 86.16% (95 CI: 66.81%-98.23%), the specificity was 94.92% (95 CI: 91.13%-98.41%), the positive predictive value was 89.87% (95% CI : 84.39%-94.38%), the negative predictive value was 92.96% (95% CI : 84.46%-99.17%). Conclusion: Overall, moving epidemic Method performs well in calculating influenza epidemic threshold in China, much better than the previous study.

BackgroundTo control respiratory syncytial virus (RSV), which causes acute respiratory infections, data and methods to assess its epidemiology are important.AimWe sought to describe RSV seasonality, affected age groups and RSV-type distribution over 12 consecutive seasons in the Netherlands, as well as to validate the moving epidemic method (MEM) for monitoring RSV epidemics.MethodsWe used 2005-17 laboratory surveillance data and sentinel data. For RSV seasonality evaluation, epidemic thresholds (i) at 1.2% of the cumulative number of RSV-positive patients per season and (ii) at 20 detections per week (for laboratory data) were employed. We also assessed MEM thresholds.ResultsIn laboratory data RSV was reported 25,491 times (no denominator). In sentinel data 5.6% (767/13,577) of specimens tested RSV positive. Over 12 seasons, sentinel data showed percentage increases of RSV positive samples. The average epidemic length was 18.0 weeks (95% confidence intervals (CI):  16.3-19.7) and 16.5 weeks (95% CI: 14.0-18.0) for laboratory and sentinel data, respectively. Epidemics started on average in week 46 (95% CI: 45-48) and 47 (95% CI:  46-49), respectively. The peak was on average in the first week of January in both datasets. MEM showed similar results to the other methods. RSV incidence was highest in youngest (0-1 and >1-2 years) and oldest (>65-75 and > 75 years) age groups, with age distribution remaining stable over time. RSV-type dominance alternated every one or two seasons.ConclusionsOur findings provide baseline information for immunisation advisory groups. The possibility of employing MEM to monitor RSV epidemics allows prospective, nearly real-time use of surveillance data.