Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015

Changes in Influenza Activities Impacted by NPI Based on 4-Year Surveillance in China: Epidemic Patterns and Trends

BACKGROUND

Since the Non-pharmaceutical Intervention (NPI) by COVID-19 emerged, influenza activity has been somewhat altered.

OBJECTIVES

The aim of this study was to explore changes in influenza activities in the context of COVID-19 based on the sentinel hospitals/units in Guangdong, southern China.

METHODS

The surveillance data in influenza-like illness (ILI) were collected from 21 cities in Guangdong between September 2017 and August 2021, while 43 hospitals/units were selected to analyze the predominant types of influenza, population characteristics, and seasonal features by three methods (the concentration ratio, the seasonal index, and the circulation distribution), based on a descriptive epidemiological approach.

RESULTS

During the four consecutive influenza seasons, a total of 157345 ILIs were tested, of which 9.05% were positive for influenza virus (n = 14238), with the highest positive rates for both IAV (13.20%) and IBV (5.41%) in the 2018-2019 season. After the emergence of COVID-19, influenza cases decreased near to zero from March 2020 till March 2021, and the dominant type of influenza virus changed from IAV to IBV. The highest positive rate of influenza existed in the age-group of 5 ~  < 15 years in each season for IAV (P < 0.001), which was consistent with that for IBV (P < 0.001). The highest annual positive rates for IBV emerged in eastern Guangdong, while the highest annual positive rates of IAV in different seasons existed in different regions. Furthermore, compared with the epidemic period (ranged from December to June) during 2017-2019, the period ended three months early (March 2020) in 2019-2020, and started by five months behind (April 2021) during 2020-2021.

CONCLUSION

The highest positive rates in 5 ~  < 15 age-group suggested the susceptible in this age-group mostly had infected with infected B/Victoria. Influenced by the emergence of COVID-19 and NPI responses, the epidemic patterns and trends of influenza activities have changed in Guangdong, 2017-2021.

Performance of discrete wavelet transform-based method in the detection of influenza outbreaks in Iran: An ecological study

Background and Aim

Timely detection of outbreaks is one of the main purposes of the health surveillance system. The presence of appropriate methods in the detection of outbreaks can have an important role in the timely detection of outbreaks. Because of the importance of this issue, this study aimed to assess the performance of discrete wavelet transform (DWT) based methods in detecting influenza outbreaks in Iran from January 2010 to January 2020.

Methods

All registered influenza-positive virus cases in Iran from January 2010 to January 2010 were obtained from the FluNet web base tool, the World Health Organization website. The combination method that includes DWT and Shewhart control chart was used in this study. All analyses were performed using MATLAB software version 2018a Stata software version 15.

Results

The Mean ± SD and median of reported influenza cases from January 2010 to January 2020 was 36 ± 108 and four cases per week. The combination of the DWT and Shewhart control chart with K = 0.25 had the most sensitivity. The most specificity in the detection of nonoutbreak days was seen in the combination of DWT and Shewhart control chart with K = 1.5, K = 1.75, and K = 2, respectively. The combination of DWT and Shewhart control chart with K = 0.5 had the best performance in the detection of outbreaks (sensitivity = 0.64, specificity: 0.90, Youden index: 0.54, and area under the curve [AUC]: 0.77).

Conclusion

The DWT-based method in detecting influenza outbreaks has acceptable performance, but it is recommended that this method's performance be assessed in detecting outbreaks of other infectious diseases.

Incidence and seroprevalence of seasonal influenza a viruses in Egypt: Results of a community-based cohort study

Background

H1N1 and H3N2 influenza A viruses circulate in people as seasonal influenza viruses. Data on influenza infection rates and circulation in demographic subpopulations in Egypt are limited. In this study, we aimed to determine the incidence and seroprevalence of seasonal influenza A virus infections in a cohort of rural Egyptians between 2017 and 2020.

Methods

A total of 2383 subjects were enrolled from 390 households in five study sites in Northern Egypt. Informed consents were obtained. Sera were collected from participants on an annual basis (Baseline: 2016–2017, Follow up 1: 2017–2018, Follow up 2: 2018–2019, and Follow up 3: 2019–2020) to determine seroprevalence of antibodies against H1N1 and H3N2 viruses by hemagglutination inhibition assay and to estimate incidence based on seroconversion.

Results

Seropositivity against H1N1 was over 40% and over 60% against H3N2. The high seroprevalence was due to natural infection because participants were mostly unvaccinated. Seropositive participants were younger than seronegative participants indicating that the infection rate is higher in children. Incidence of both viruses ranged from 4% to 28% depending on study year. The incidence and seroprevalence of H3N2 and H1N1 infections at Follow up 1, 2, and 3 showed an increase at Follow up 2 observed for all age categories corresponding to season 2018–2019, at which the vaccine efficacy was the lowest worldwide compared with preceding and following seasons.

Conclusions

This cohort study provided estimates of influenza A infection rates among rural Egyptians. We recommend updating influenza vaccination programs to focus on such populations.

A Systematic Review of Influenza Epidemiology and Surveillance in the Eastern Mediterranean and North African Region

Seasonal influenza represents a huge health burden, resulting in significant mortality and morbidity. Following the 2009 H1N1 pandemic, focus has been directed on the burden of influenza globally. Country and regional disease burden estimates play important roles in helping inform decisions on national influenza intervention programmes. Despite improvements in influenza surveillance following the 2009 pandemic, many opportunities remain unexplored in the Eastern Mediterranean and North African (EMNA) region, which has a high prevalence of patients with chronic disease and thus a population at high risk of influenza complications. We conducted a systematic literature review of Embase, Medline, Scopus and the Cochrane Database of Systematic Reviews from 1 January 1998 to 31 January 2020 covering the EMNA region with the aim to describe the epidemiology of influenza in the region and assess the influenza epidemiological surveillance research landscape. Relevant data on study characteristics, population, clinical/virology characteristics and epidemiology were extracted and summarised descriptively. Of the 112 studies identified for inclusion, 90 were conducted in the Eastern Mediterranean region, 19 in North Africa and three across the EMNA region. Data were reported on 314,058 laboratory-confirmed influenza cases, 96 of which were derived from surveillance systems. Amongst the surveillance studies, the percentage of positive cases reported ranged from 1% to 100%. The predominantly identified influenza strain was strain A; H1N1 was the most prominent circulating subtype. Typing was performed in approximately 75% and subtyping in 50% of studies, respectively. Data on those considered most at risk for influenza complications were collected in 21% of studies, highlighting a regional gap for these data. Our review reveals existing gaps in regional estimates of influenza health and economic burden, hospitalisation rates and duration, and highlights the need for robust and high-quality epidemiology data to help inform public health interventions.

Aberration detection in influenza trends in Iran by using cumulative sum chart and period regression

OBJECTIVES

This study aims to determine the alarm thresholds in influenza outbreaks and aberration detection in the influenza trend in Iran by using cumulative sum control chart (CUSUM) and period regression.

METHODS

We used the weekly reported influenza-positive (types A and B) cases from Iran between January 2015 and November 2019. The period regression model and CUSUM chart were used as detection algorithms to figure out the alarm thresholds.

RESULTS

The mean ± SD and the median (95% CI) of the determined threshold per week were 34.85 ± 15.29 and 28.30 (17.67-64.62). According to the period regression, there were nine epidemic periods of influenza from 2015 to 2019. By using the CUSUM and considering a different h (h is an appropriate value that leads to the desired estimation for upper control limit) for the calculation of the upper control limit, 88, 84, 73 and 67 weeks were determined as the epidemic period.

CONCLUSION

According to the current study, the incidence of influenza showed a cyclic pattern and the epidemic recurred each year. Understanding this cyclical pattern can help health policymakers launch prevention programs such as vaccination during certain months of the year.

Performance of Bayesian outbreak detection algorithm in the syndromic surveillance of influenza-like illness in small region

Early warning for Infectious disease outbreak is an important public health policy concern, and finding a reliable method for early warning remains one of the active fields for researchers. The purpose of this study was to evaluate the performance of the Bayesian outbreak detection algorithm in the surveillance of influenza-like illness in small regions. The Bayesian outbreak detection algorithm (BODA) and modified cumulative sum control chart algorithm (CUSUM) were applied to daily counts of influenza-like illness in Tehran, Iran. We used data from September 2016 through August 2017 to provide background counts for the algorithms, and data from September 2017 through August 2018 used for testing the algorithms. The performances of the BODA and modified CUSUM algorithms were compared with the results coming from experts' signal inspections. The data of syndromic surveillance of influenza-like illness in Tehran had a median daily counts of 7 (IQR = 3-14). The data showed significant seasonal trends and holiday and day-of-the-week effects. The utility of the BODA algorithm in real-time detection of the influenza outbreak was better than the modified CUSUM algorithm. Moreover, the best performance was when a trend included in the analysis. The BODA algorithm was able to detect the influenza outbreaks with 4-5 days delay, with the least false-positive alarm. Applying the BODA algorithm as an outbreak detection method in influenza-like syndromic surveillance might be useful in early detection of the outbreaks in small regions.

High prevalence of SARS-CoV-2 and influenza A virus (H1N1) coinfection in dead patients in Northeastern Iran

In the last months of 2019, an outbreak of fatal respiratory disease started in Wuhan, China, and quickly spread to other parts of the world. It was named COVID-19, and to date, thousands of cases of infection and death are reported worldwide. This disease is associated with a wide range of symptoms, which makes accurate diagnosis of it difficult. During previous severe acute respiratory syndrome (SARS) pandemic in 2003, researchers found that the patients with fever, cough, or sore throat had a 5% influenza virus-positive rate. This finding made us think that the wide range of symptoms and also relatively high prevalence of death in our patients may be due to the coinfection with other viruses. Thus, we evaluated the coinfection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with other respiratory viruses in dead patients in North Khorasan. We evaluated the presence of influenza A/B virus, human metapneumovirus, bocavirus, adenovirus, respiratory syncytial virus (RSV), and parainfluenza viruses in 105 SARS-CoV-2 positive dead patients, using polymerase chain reaction (PCR) and reverse transcription PCR tests. We found coinfection with influenza virus in 22.3%, RSV, and bocavirus in 9.7%, parainfluenza viruses in 3.9%, human metapneumovirus in 2.9%, and finally adenovirus in 1.9% of SARS-CoV-2 positive dead cases. Our findings highlight a high prevalence of coinfection with influenza A virus and the monopoly of coinfection with Human metapneumovirus in children.

Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran

Objective

Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria.

Results

It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4393-y) contains supplementary material, which is available to authorized users.