Factors associated with hospitalization for seasonal influenza in Morocco.

Background: Morocco is actively working towards expanding its influenza vaccine policy to cover high-risk groups, as recommended by the World Health Organization (WHO). Aims: We assessed the risk factors for influenza-associated hospitalization for severe acute respiratory infections (SARI) that occurred during the last 5 seasons. Methods: We conducted a retrospective, analytical study among patients recruited in the ambulatory and hospital sites of the influenza sentinel surveillance system in Morocco between 2014 and 2019. Using multiple logistic regression, we compared the characteristics of influenza-positive patients with SARI to those with influenza-like illness (ILI) to identify factors associated with severe disease. Results: We included 1323 positive influenza patients with either SARI (41.7%) or ILI diagnosis (58.3%). A(H1N1)pdm09, A(H3N2) and influenza B, respectively, contributed 49.2%, 29.5% and 20.6% of the cases. The main risk factors considered in the bivariate analysis were found in the multivariate analysis to be significantly associated with influenza-related hospitalization (SARI): age < 2 years (aOR = 7.08, P < 0.001); age ≥ 65 years (aOR = 3.59, P < 0.001); diabetes (aOR = 1.98, P = 0.017); obesity (aOR = 2.94, P = 0.034); asthma or chronic respiratory disease (aOR = 4.99, P < 0.001); chronic renal failure (aOR = 4.74, P = 0.005); pregnancy (aOR = 7.49, P < 0.001); and the A(H1N1)pdm09 subtype (aOR = 1.82, P < 0.001). Conclusion: This study provides epidemiological evidence for the expected benefit of an influenza vaccination strategy for high-risk groups as recommended by the WHO.

Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME.

The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-CoV-2 infection had elevated plasma levels of VEGF-A, MIP-1b, and IL-17. RANTES and TNF were associated with healthy individuals, whereas IL-27, IL-9, IL-12p40, and MCP-3 were associated with non-Severity. These findings suggest that these cytokines may promote the development of novel preventive and therapeutic pathways for disease management. In this study, the use of artificial intelligence is intended to support clinical diagnoses of patients to determine how each cytokine may be responsible for the severity of COVID-19, which could lead to the identification of several cytokines that could aid in treatment decision-making and vaccine development.

Viral aetiology of influenza-like illnesses and severe acute respiratory illnesses in Morocco, September 2014 to December 2016.

Background: There is a scarcity of information on the viral aetiology of influenza-like illness (ILI) and severe acute respiratory infection (SARI) among patients in Morocco. Methods: From September 2014 to December 2016, we prospectively enrolled inpatients and outpatients from all age groups meeting the World Health Organization (WHO) case definition for ILI and SARI from 59 sentinel sites. The specimens were tested using real-time monoplex reverse-transcription polymerase chain reaction method for detecting 16 relevant respiratory viruses. Results: At least one respiratory virus was detected in 1423 (70.8%) of 2009 specimens. Influenza viruses were the most common, detected in 612 (30.4%) of processed samples, followed by respiratory syncytial virus (RSV) in 359 (17.9%), human rhinovirus (HRV) in 263 (13.1%), adenovirus (HAdV) in 124 (6.2%), parainfluenza viruses (HPIV) in 107 (5.3%), coronaviruses (HCoV) in 94 (4.7%), human bocavirus (HBoV) in 92 (4.6%), and human metapneumovirus (HMPV) in 74 (3.7%). From 770 samples from children under 5 years old, RSV (288, 36.6%), influenza viruses (106, 13.8%), HRV (96, 12.5%) and HAdV (91, 11.8%) were most prevalent. Among 955 samples from adults, Influenza viruses (506, 53.0%), and HRV (167, 17.5%) were most often detected. Co-infections were found in 268 (18.8%) of 1423 positive specimens, and most (60.4%) were in children under 5 years of age. While influenza viruses, RSV, and HMPV had a defined period of circulation, the other viruses did not display clear seasonal patterns. Conclusions: We found that RSV was predominant among SARI cases in Morocco, particularly in children under 5 years of age. Our results are in line with reported data from other parts of the world, stating that RSV is the leading cause of lower respiratory tract infections in infants and young children.

Machine learning approaches in Covid-19 severity risk prediction in Morocco.

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Influenza epidemiology and risk factors for severe acute respiratory infection in Morocco during the 2016/2017 and 2017/2018 seasons.

INTRODUCTION: in order to implement an influenza vaccination program for high-risk-groups in Morocco, as recommended by the World Health Organization, an epidemiological study indicating the influenza virus effect in the development of complicated influenza for subjects with co-morbidity was required. The present study aims to evaluate the risk factors for severe acute respiratory infections caused by influenza in risk groups. METHODS: this research is based on the epidemiological and virological surveillance data of severe acute respiratory infections and influenza-like illness during the 2016/2017 and 2017/2018 seasons. It was realized using a retrospective series study with a descriptive and analytical purpose. RESULTS: the over-recruitment of pediatric cases with a severe acute respiratory infection has been significantly rectified because cases of severe acute respiratory infections under 15 years old in the 2017/2018 season represent only 57.9%, whereas they represented 75.9% of the total cases of severe acute respiratory infections during the 2016/2017 season. The influenza positivity rate has increased globally and specifically by age group, clinical service and co-morbidity. The risk factors considered were significantly associated with hospitalization for influenza-associated severe acute respiratory infections. The multivariate logistic regression analysis considers male sex (OR=2.1), age ≥65 years (OR=5.4), presence of influenza cases in the surroundings (OR=0.1), diabetes (OR=7.5) and chronic respiratory disease (OR=10.9) as risk factors influenza-associated severe acute respiratory infections. CONCLUSION: the risk assessment of influenza-associated severe acute respiratory infections in high-risk groups revealed national epidemiological findings, particularly for diabetics and the elderly. An influenza vaccination program for these high-risk-groups becomes much recommended in Morocco.

Outbreak Investigation of a Multipathogen Foodborne Disease in a Training Institute in Rabat, Morocco: Case-Control Study.

BACKGROUND: On June 18, 2017, the public health service was alerted about 43 students in the training institute in Rabat who were admitted to the emergency room for acute gastroenteritis following the uptake of a meal a day before. OBJECTIVE: This study aimed to investigate the foodborne disease outbreak by confirming the outbreak, identifying the source of contamination, and recommending control measures. METHODS: We conducted a case-control study. Cases and controls were selected in a ratio of 1:1. We defined a case as any member of the training institute who attended the Ramadan buffet in the institute's restaurant and who had presented, in the weekend of June 16 to 20, 2017, symptoms of diarrhea or vomiting with at least one of the following signs: abdominal pain, fever, headache, nausea, and dizziness. A control was defined as anyone who attended the Ramadan buffet in the institute's restaurant but had not presented any symptoms from June 16 to 20, 2017. We conducted a bivariate and multivariable analysis. Stools of ill students were collected, and a food specimen was collected for bacterial testing. RESULTS: A total of 50 cases and 50 controls were selected. Among the cases, males were predominant (43/50, 86%); the median age was 21 years. A total of 47 cases sought medical care. There were no hospitalizations and no deaths. The episode was short with an estimated average incubation period of 9 hours. The epidemic curve oriented toward a common source of contamination. Among food items, briwates were strongly associated with the illness with an odd ratio of 14.23 (95% CI 5.04-40.04; P<.001). Laboratory testing of briwates showed presence of Escherichia coli O157 and Staphylococcus aureus. CONCLUSIONS: This foodborne disease outbreak was likely caused by briwates that was contaminated with S aureus and E coli. We recommended strengthening hygiene measures. Food handling techniques should be taught as part of continuous professional development for food handlers.