Adapting an integrated acute respiratory infections sentinel surveillance to the COVID-19 pandemic requirements, Egypt, 2020-2022.

Objectives: In Egypt, an integrated surveillance for acute respiratory infections (ARIs) was established in 2016 to identify the causes of ARIs. The surveillance system includes 19 governmental hospitals. In response to the coronavirus disease 2019 (COVID-19) pandemic, the World Health Organisation (WHO) requested surveillance adaptation to address the emerging challenges. This study aims to describe the experience in Egypt of adapting ARI surveillance to the COVID-19 pandemic. Methods: WHO case definitions were used to identify patients with ARIs. Nasopharyngeal/oropharyngeal swabs were collected for SARS-CoV-2 and influenza testing. Demographic and clinical information were obtained by interviewing patients at the hospitals. During the COVID-19 pandemic, the first two outpatients daily and every fifth admitted patient were enrolled in the study. To determine the status of ARIs in Egypt during the pandemic, patient demographic, clinical and laboratory data from 2020 to 2022 were obtained and descriptive analyses were performed. Results: Overall, 18,160 patients were enrolled in the study, including 7923 (43.6%) seen at outpatient clinics and 10,237 (56.4%) inpatients. Of the study participants, 6453 (35.5%) tested positive for ARIs, including 5620 (87.1%) for SARS-CoV-2, 781 (12.1%) for influenza and 52 (0.8%) for SARS-CoV-2/influenza coinfection. SARS-CoV-2 was the cause for 95.3% of admitted patients and 65.4% of outpatients. Influenza subtypes included A/H3 (55.7%), Influenza-B (29.1%) and H1/pdm09 (14.2%). Compared with influenza, SARS-CoV-2 tended to infect the elderly, in warm weather and in urban governorates, and resulted in more hospitalisations, longer hospital stays and higher case fatalities (16.3% vs 6.6%, p < 0.001). Conclusions: ARI surveillance in Egypt was successfully adapted to the COVID-19 pandemic and effectively described the clinical characteristics and severity of circulating viruses. Surveillance reported the re-emergence of influenza with a severe course and high fatality. Surveillance is essential for monitoring the activity of respiratory viruses with the aim of guiding clinical management, including preventative and control measures.

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts.

BACKGROUND: Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched. METHODS: The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of 'High' or above were added to an operational watchlist and assistance was deployed as needed. RESULTS: Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%-67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available. CONCLUSIONS: The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

Epidemiology and outcome of influenza-associated infections among hospitalized patients with acute respiratory infections, Egypt national surveillance system, 2016-2019.

INTRODUCTION: Egypt has established different types of surveillance systems to monitor influenza activities, early detect outbreaks, and tailor efficient prevention and control strategies. This is the first study to describe epidemiology and outcome of influenza-associated infections among hospitalized patients using the National Electronic Disease Surveillance System (NEDSS) data, 2016-2019. METHODS: Data reported from 284 hospitals all over Egypt were extracted from the NEDSS. Data of hospitalized patients with Acute Respiratory Infections (ARI), 2016-2019, were included in the analysis. Laboratory testing for influenza by RT-PCR according to US CDC testing protocol was used to confirm influenza type and subtype. RESULTS: Overall 46 417 patients hospitalized with ARI were identified, their mean age was 30.9 ± 26 and 52.9% were males. Among 41 512 (89.4%) laboratory-tested patients, 7167 (17.3%) were positive for one or more types of influenza viruses. Influenza viruses circulated in all ages and throughout the year, with higher rates in winter, late childhood, and middle ages. Mortality from influenza was significantly higher than other causes of ARIs (5.0% vs 3.8%, P < .001), and it was associated with older ages, December-May, delay in hospital admission, residence in urban and frontier governorates and infection with A/H1N1 virus. The distribution of influenza subtype by time shows alternate pattern between A/H1N1 and H3N2, each subtype peaks every other year with a high peak of A/H1N1 in 2016. CONCLUSIONS: The national Egyptian surveillance succeeded to describe the epidemiology of hospitalized patients with ARIs and influenza in Egypt over time. Surveillance with strain-specific laboratory testing and annual assessment of associated severity might be useful to guide influenza prevention and control strategies including vaccination and case management.

Evaluating tools to define influenza baseline and threshold values using surveillance data, Egypt, season 2016/17.

BACKGROUND: Establishing influenza thresholds and transmission intensity can help evaluate seasonal changes in influenza severity and potential pandemics. We aimed to evaluate the moving epidemic method (MEM) for calculating influenza thresholds for season 2016/17 in Egypt using four parameters, to identify the most useful parameter. Also to measure the agreement between both the country-specific statistical empirical method and World Health Organization method to MEM for determining the length and intensity level of activity of the influenza season. METHODS: Routinely epidemiological and laboratory data from sentinel surveillance sites for Severe Acute Respiratory Infection (SARI) and influenza-like illness (ILI) were used for calculating thresholds for seasons between 2010/11 and 2015/16 to test 2016/17 season. The parameters calculated were: screened ILI consultation rate × 1000, screened ILI composite parameter, influenza positivity percentage among sampled SARI cases, and influenza positivity percentage among sampled ILI and SARI cases. These parameters assess seasonality and intensity of influenza activity using the three proposed methods (mentioned above). Agreement between the three methods was done using several approaches. RESULTS: The intensity of influenza activity by MEM was lower than the other two methods. Agreement between MEM and each of the other two techniques varied appreciably from good to very good for seasonal duration, and poor to fair for intensity level. In addition, parameters including laboratory data showed a pattern of bi-wave activity; the first wave occurred in winter mostly between epidemiological weeks 39 and 52 and the second occurred in spring mostly between weeks 12 and 17. CONCLUSION: Parameters including laboratory data were more useful in defining seasonality of influenza. Further exploration of the MEM model in future seasons may help to provide a more comprehensive understanding of its use and application.