Data collection
In 2004, the Epidemiology Department of the Ministry of Health of Morocco launched a year-round public sector syndromic surveillance system for ILI comprised of 412 primary health centers, with a catchment population of almost 12 million people. Sites report weekly ILI activity to the regional and central levels, where health officials aggregate the surveillance data. A case definition similar to the1999 WHO ILI case definition recommended for public health surveillance, defined as “a sudden onset of fever, a temperature>38°C and cough or sore throat in the absence of another diagnosis” was used from 2004 to 2015 (15, 16). In 2015, Morocco adopted the updated WHO standard ILI case definition (5)developed in2011,as “an acute respiratory illness with a measured temperature of ≥ 38 °C and cough, with onset within the past 10 days”(17). Reporting includes the total number of ILI consultations aggregated by gender and age group, as well as total outpatient consultations. The proportion of ILI visits among all outpatient consultations is used as a proxy for influenza activity.
In 2007, the Moroccan National Influenza Center (NIC) began a virologic surveillance system in both ambulatory and hospital sites to complement the syndromic system and provide data on laboratory-confirmed influenza activity (18). After an interruption in data collection beginning in 2010, virologic surveillance was resumed in 8 sentinel sites in 2014. Specimens were collected and characterized between September and June. Epidemiologic data on both mild (ILI) and severe illness in hospitalized patients (WHO-defined severe acute respiratory infection [SARI]) were integrated into this system (17).
We used eleven seasons of syndromic surveillance data (2005/2006 to 2016/2017, excluding the 2009/2010 pandemic year from analysis as influenza activity was not reflective of a typical season); this was described elsewhere (17). We compared two methodologies for establishing seasonal baseline activity and epidemic thresholds. We also compared the calculated thresholds with the observed weeks for the start and end of the 2017/2018 season. season. Using three seasons of virologic ILI surveillance data (2014/2015 to 2016/2017), we used the MEM method to make calculations using the composite parameter recommended by WHO (35); this method estimates the proportion of laboratory-confirmed influenza ILI consultations among all outpatient consultations, or the product of weekly ILI consultations of total outpatient visits and weekly percentage of influenza-positive specimens among respiratory tests.
Methodology &statistical procedures
Overview of WHO and MEM methods
The methods discussed in order to standardize country information on influenza activity, have raised basic concepts summarized in table 1.
Table 1: Summary of WHO method and MEM concepts
Concepts
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WHO method (5, 35)
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MEM (25, 35)
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Average epidemic curve
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Find 3-week moving average of ILI%. Find median peak week for each season. Align the multiple seasons on median peak week. Calculate the average ILI% for each week. Indicates the usual level of influenza activity that occurs during a typical year.
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MEM software produces an average curve, lower interval, and higher interval.
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Alert threshold
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Calculate the mean and standard deviation (SD) of the average epidemic curve. For each week, the alert threshold is 1.645 SD above the weekly ILI% mean. ILI% >1.645 SD indicates high ILI activity or outbreaks and may be used to characterize a severe season.
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Alert curve
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A graph consisting of the alert thresholds for each epidemic week.
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Seasonal threshold (WHO) or pre-epidemic threshold (MEM)
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Median weekly ILI% over all weeks (i.e., the average epidemic curve is not used). Indicates the level of influenza activity that signals the start and end of the annual influenza season(s).
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For prospective surveillance: upper limit of the 95% one-sided confidence interval of the arithmetic mean of the 30 highest pre-epidemic weekly ILI% values. Parameter value which marks the start of the epidemic period.
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Post-epidemic threshold (MEM)
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For prospective surveillance: upper limit of the 95% one-sided confidence interval of the arithmetic mean of the 30 highest post-epidemic weekly ILI% values.
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Epidemic period start
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The third of three consecutive weeks with ILI% above seasonal threshold. Indicates that influenza activity occurs consistently.
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For retrospective analysis of individual season data: see “length of epidemic period”.
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Epidemic period end
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The third of three consecutive weeks with ILI% below seasonal threshold
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For retrospective analysis of individual season data: see “length of epidemic period”.
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Length of epidemic period
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Weeks from epidemic start to end.
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For retrospective analysis of individual season data: MEM software uses a “maximum accumulated proportions percentage (MAP)” algorithm to split the season into three periods: a pre-epidemic, an epidemic, and a post-epidemic period.
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Epidemic percentage
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Proportion of total cases that occurred during the epidemic period
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Moderate (WHO) or medium (MEM) intensity
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Upper 40% limit of 1-sided CI of mean of all peak values.
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Upper 40% limit of the one-sided confidence interval of the geometric mean of the 30 highest epidemic weekly ILI% values.
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High intensity
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Upper 90% limit of 1-sided CI of mean of all peak values.
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Upper 90% limit of the one-sided confidence interval of the geometric mean of the 30 highest epidemic weekly ILI% values.
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Extraordinary (WHO) or very high (MEM) intensity
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Upper 97.5% limit of 1-sided CI of mean of all peak values.
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Upper 95% limit of the one-sided confidence interval of the geometric mean of the 30 highest epidemic weekly ILI% values.
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The WHO method
The 2012 WHO Global Epidemiological Surveillance Standards for Influenza (WHO manual) (5) included a simple method to establish an average epidemic curve to identify the beginning of the influenza season using national influenza surveillance data. This method characterizes the intensity of influenza activity each year and may be used to describe the seasonality of influenza virus circulation. Using ILI as a proxy for influenza virologic activity (20, 21), we used weekly proportion of ILI among all outpatient consultations as our indicator of influenza activity.
With this method, we were able to produce an average epidemic curve. Using data from the average epidemic curve, we used statistical measures of variance to establish an alert threshold.
We determined the flat baseline for expected influenza activity throughout the year in order to develop an indicator for the onset of influenza season (seasonal threshold). Sustained influenza activity (i.e., three consecutive weeks) above this baseline indicated the start of the influenza season or the epidemic period (5). In the final step, moderate, high, and extraordinary intensity thresholds were estimated as described in the WHO Pandemic Influenza Severity Assessment manual (35), (Figure 1).
The Moving Epidemic Method
The Moving Epidemic Method (MEM) (24-29) is an alternative tool developed to help model influenza epidemics also using retrospective national surveillance data. It may be described as a combination rate-difference model that uses cumulative differences in rates to determine epidemic periods and intensity of activity (28,29).
We uploaded our surveillance data via the MEM application user interface (24), and fit the model using three steps. We first visually compared activity over the eleven seasons in order to compare the timing of peak activity and activity trends across seasons. The MEM procedure has three main steps: First, the length, start and the end of the annual epidemics are determined, splitting the season in three periods: a pre-epidemic, an epidemic and a post-epidemic period (28, 29). In the second step, we built the model by using retrospective data from all eleven seasons. The MEM app calculated the pre-epidemic threshold that marks the start of the epidemic period (analogous to the seasonal threshold in the WHO method). In the third step, medium, high, and very high intensity thresholds were estimated (Table 2). Using the app, we produced graphs of each season showing the pre-epidemic, epidemic and post-epidemic periods (Figure 2).In addition, as the assumption that ILI activity is reflecting influenza virus circulation has limitations, we created a second seasonal threshold with this methodology using the composite parameter recommended by WHO for three seasons of virologic ILI surveillance (Figure 3).
Lastly, we calculatedindicators of performance of the app to detect epidemics, using values from the model for sensitivity, specificity, positive predictive value, negative predictive value, percent agreement and the Matthew correlation coefficient (Table 3). The application allowed us to optimize the model by searching the optimum slope of the MAP curve to optimize the goodness-of-fit of the model for detecting epidemics.
The MEM app calculates goodness-of-fit indicators in an iterative process using a cross- validation procedure (28).True positives (TP) were then defined as values of epidemic period above the threshold, true negatives (TN) as values of the non-epidemic period below the threshold, false positives (FP) as values of the non-epidemic period above the threshold and false negatives (FN) as values of epidemic period below the threshold. The process was repeated for each season in the dataset and all TP, TN, FP and FN were pooled. To measure the performance of the threshold, the following statistics and definitions were used (28):
- Sensitivity: The number of epidemic weeks above the pre-epidemic threshold and above the post-epidemic threshold divided by the number of epidemic weeks (epidemic length).
- Specificity: The number of non-epidemic weeks below the pre-epidemic threshold and below the post-epidemic threshold divided by the number of non-epidemic weeks.
- Positive predictive value (PPV): The number of epidemic weeks above the threshold divided by the number of weeks above the threshold.
- Negative predictive value (NPV): The number of non-epidemic weeks below the threshold divided by the number of weeks below the threshold.
Ethics statement
The ILI sentinel surveillance system is a public health activity organized by the Ministry of Health of Morocco. Personally identifiable data is excluded from this surveillance system; as a result, no request for authorization from the National Ethics Committees was required. Indeed, the Royal Dahir N°1-15-110 dated August 4, 2015, promulgating the law N°28-13 relating to the protection of persons participating in biomedical research, provides for special provisions for non-interventional or observational researches as stipulated in its articles 2 and 26.