Unable to write in log file ../../bases/logs/gimorg/logerror.txt Search | WHO COVID-19 Research Database
Show: 20 | 50 | 100
Results 1 - 3 de 3
Add filters

Document Type
Year range
PLoS One ; 18(3): e0283804, 2023.
Article in English | MEDLINE | ID: covidwho-2266217


Acute respiratory tract infections (ARTIs) during the winter months are associated with higher morbidity and mortality compared to other seasons of the year, with children below five, elderly, and immunocompromised patients being the most susceptible. Influenza A and B viruses, rhinovirus, coronaviruses, respiratory syncytial virus, adenovirus, and parainfluenza viruses, are the most frequently identified causes of viral ARTIs. In addition, the emergence of SARS-CoV-2 in 2019 provided an additional viral cause of ARTIs. The aim of this study was to provide an overview of the epidemiological status of upper respiratory infections, their main causative agents, and reported clinical presentation in the winter months of 2021, during two important surges of COVID-19 in Jordan. Nasopharyngeal samples were collected from 339 symptomatic patients during the period from December 2021 to March 2022, followed by nucleic acid isolation using a Viral RNA/DNA extraction Kit. The causative virus species associated with the patient's respiratory symptoms was determined utilizing a multiplex real-time PCR targeting 21 viruses, 11 bacteria, and a single fungus. SARS-CoV-2 was identified in 39.2% of the patients (n = 133/339). A total of 15 different pathogens were also identified as co-infections among these 133 patients (n = 67/133). SARS-CoV-2-Bacterial coinfections (37.6%, n = 50/133) were the most frequent, with Bordetella species being the most common, followed by Staphylococcus aureus, and H.influenzae type B. Viral coinfection rate was 27.8% (n = 37/133), with Influenza B virus and Human bocavirus being the most common. In Conclusion, Both SARS-CoV-2, influenza B virus, and Bordetella accounted for the majority of infections in patients with URTI during the winter months of 2021-2022. Interestingly, more than 50% of the patients with symptoms of URTIs were confirmed to have a coinfection with two or more respiratory pathogens, with SARS-CoV-2 and Bordetella coinfection being most predominant.

COVID-19 , Coinfection , Respiratory Tract Infections , Child , Humans , Aged , SARS-CoV-2 , Jordan/epidemiology , COVID-19/epidemiology , Prevalence , Seasons , Coinfection/epidemiology , Respiratory Tract Infections/epidemiology , Influenza B virus/genetics , Bacteria/genetics
PLoS One ; 18(2): e0281689, 2023.
Article in English | MEDLINE | ID: covidwho-2238477


BACKGROUND: The development of specific immunoglobulins to COVID-19 after natural infection or vaccination has been proposed. The efficacy and dynamics of this response are not clear yet. AIM: This study aims to analyze the immunoglobulins response among COVID-19 patients, COVID-19 vaccine recipients and random individuals. METHODS: A total of 665 participants including 233 COVID-19 patients, 288 COVID-19 vaccine recipients, and 144 random individuals were investigated for anti-COVID-19 immunoglobulins (IgA, IgG, IgM). RESULTS: Among COVID-19 patients, 22.7% had detectable IgA antibodies with a mean of 27.3±57.1 ng/ml, 29.6% had IgM antibodies with a mean of 188.4±666.0 BAU/ml, while 59.2% had IgG antibodies with a mean of 101.7±139.7 BAU/ml. Pfizer-BioNTech vaccine recipients had positive IgG in 99.3% with a mean of 515.5±1143.5 BAU/ml while 85.7% of Sinopharm vaccine recipients had positive IgG with a mean of 170.0±230.0 BAU/ml. Regarding random individuals, 54.9% had positive IgG with a mean of 164.3±214 BAU/ml. The peak IgM response in COVID-19 patients was detected early at 15-22 days, followed by IgG peak at 16-30 days, and IgA peak at 0-60 days. IgM antibodies disappeared at 61-90 days, while IgG and IgA antibodies decreased slowly after the peak and remained detectable up to 300 days. The frequency of IgG positivity among patients was significantly affected by increased age, admission department (inpatient or outpatient), symptoms, need for oxygen therapy, and increased duration between positive COVID-19 RT PCR test and serum sampling (p˂0.05). Positive correlations were noted between different types of immunoglobulins (IgG, IgM, and IgA) among patients. CONCLUSIONS: Natural infection and COIVD-19 vaccines provide IgG-mediated immunity. The class, positivity, mean, efficacy, and duration of immunoglobulins response are affected by the mechanism of immunity and host related variables. Random community individuals had detectable COVID-19 IgG at ~55%, far from reaching herd immunity levels.

COVID-19 Vaccines , COVID-19 , Humans , COVID-19/prevention & control , Immunoglobulin G , Immunoglobulin A , Immunoglobulin M , Antibodies, Viral
J Infect Public Health ; 14(6): 689-695, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1118563


BACKGROUND: The number of COVID-19 infected patients has been soaring in the Middle East countries. The disease poses a significant threat, decisions about prioritizing care should be made in accordance with the proven risk factors for complications. OBJECTIVE: The present study provides the first bespoke prediction model in the Middle East to identify COVID-19 patients, who are at higher risk for complications. METHOD: A case-control study design was adopted to compare the characteristics of successfully recovered patients with those who had complications. Complications were defined as admission to the intensive care unit, mechanical ventilation, sepsis or septic shock, pneumonia or respiratory failure, and death. The prediction model was created through multivariable logistic regression. Overall statistical significance tests for the model were carried out. RESULTS: All COVID-19 infected hospitalized patients (n = 133) in Amman - Jordan were included in the study. Successfully recovered were 125 patients. The median age (IRQ) was 26 (10-40). Almost 30% were >40 years. Patients with complications were eight patients, age 63 (51.5-71.5). The prediction model identified the following variables as risk factors: diabetes (OR = 59.7; 95% CI: 3.5-1011.5, p = 0.005), fever (OR = 24.8; 95% CI: 1.4-447.3, p = 0.029), SHORTNESS OF BREATH (OR = 15.9; 95% CI: 1.3-189.7, p = 0.029), body mass index (OR = 0.74; 95% CI: 0.61-0.88, p = 0.001), abnormal Neutrophils (OR = 16.8; 95% CI: 1.0-292.0, p = 0.053). Prediction model was statistically significant, χ2(5) = 86.1, p < 0.0005. CONCLUSIONS: Unlike reports from China, the most influential variables that led to disease progression in Jordanian patients were diabetes, fever, shortness of breath, body mass index, and abnormal neutrophils. Similar to reports from the USA, smoking was not a leading factor for complications. Comorbidities and patient health status, rather than age, were the primary risk factors for complications. Treatment with Hydroxychloroquine showed no protective effect.

COVID-19 , RNA, Viral , Case-Control Studies , China , Hospitalization , Humans , Jordan/epidemiology , Middle Aged , Middle East , Risk Factors , SARS-CoV-2