Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza-like illness surveillance data 2009-2018 in Tunisia

Identify future epidemic threshold and intensity for influenza-like illness in Iraq by using the moving epidemic method

Objectives

Influenza-like illness (ILI) entered the Iraq surveillance system in 2021. The alert threshold was determined using the cumulative sum 2 method, which did not provide other characteristics. This study uses the moving epidemic method (MEM) to describe duration and estimate alert thresholds for ILI in Iraq for 2023-2024.

Methods

MEM default package was used to estimate influenza 2023-2024 epidemic thresholds. Analysis was repeated using optimum parameter of epidemic timing for fixed criteria method, which is 3.3. Arithmetic means and 95% confidence interval upper limit were used to estimate threshold. Geometric mean and 40%, 90%, and 97.3% confidence interval upper limits were used to estimate intensity levels. Aggregated Centers for Disease Control and Prevention surveillance data were used to detect epidemic thresholds, length, sensitivity, and predictive values.

Results

ILI activity starts at week 30 and lasts 7 weeks. Optimized epidemic threshold is 4513 cases, lower than default (4540 cases). Optimized medium-intensity level was higher than default, and high and very high-intensity levels were lower.

Conclusions

MEM is essential to determine an influenza epidemic's threshold and intensity levels. Despite requiring 3-5 years of data, using it on data for 2.5 years has resulted in an epidemic threshold slightly higher than the threshold calculated using the cumulative sum 2 method.

Stress and Infant Media Exposure During COVID-19: A Study Among Latino Families

OBJECTIVE

The COVID-19 pandemic disproportionately harmed Latino families; however, its effects on their stress and media routines remain understudied. We examined economic and parenting stress patterns during the COVID-19 pandemic and estimated associations between these forms of stress and nonadherence to American Academy of Pediatrics (AAP) infant media exposure recommendations among Latino families. We also explored how nonadherence with AAP recommendations varied with COVID-19 cases.

METHODS

We analyzed baseline data from an ongoing clinical trial recruiting low-income Latino parent-infant dyads. Nonadherence with AAP media exposure recommendations (ScreenQ) and economic and parent stress were measured using parent reports. Additional variables included epidemiological data on COVID-19 cases. Linear models examined associations between the pandemic and both stress variables as well as between stress and ScreenQ. Using locally weighted scatterplot smoothing curve fitting, the rise and fall of NJ COVID-19 cases were overlayed with ScreenQ scores over time to visualize and explore trends.

RESULTS

All parents identified as Latino (62.6% unemployed, 91.5% limited English proficiency). Mean infant age was 8.2 months. Parent stress increased over time during the COVID-19 pandemic (r = 0.13, p = 0.0369). After covariate adjustment, economic and parent stress were associated with increased nonadherence with AAP recommendations (standardized beta = 0.16, 95% confidence interval [CI], 0.03-0.29; standardized beta = 0.18, 95% CI, 0.04-0.31, respectively). Nonadherence to media exposure recommendations seemed to track with rises in the number of COVID-19 cases with a lag of 7 days.

CONCLUSION

Parent and economic stress were associated with nonadherence to infant media exposure recommendations among Latino families. These findings highlight the need for practitioners to support families from under-resourced communities and to promote healthy media routines.

Strengthening influenza surveillance capacity in the Eastern Mediterranean Region: Nearly two decades of direct support from the United States Centers for Disease Control and Prevention

Since 2004, the US Centers for Disease Control and Prevention (CDC) Influenza Division (ID) has supported seven countries in the Eastern Mediterranean region and the World Health Organization Regional Office for the Eastern Mediterranean to establish and strengthen influenza surveillance. The substantial growth of influenza surveillance capacities in the region demonstrates a commitment by governments to strengthen national programs and contribute to global surveillance. The full value of surveillance data is in its use to guide local public health decisions. CDC ID remains committed to supporting the region and supporting partners to translate surveillance data into policies and programs effectively.

Applying the Moving Epidemic Method to Establish the Influenza Epidemic Thresholds and Intensity Levels for Age-Specific Groups in Hubei Province, China

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.

Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza-like illness surveillance data 2009-2018 in Tunisia

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.