Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97-0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97-0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26-2.74) and 1.078 (95%CI 1.04-1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93-1.31) and 1.007 (95%CI 0.99-1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.
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http://dx.doi.org/10.1038/s41598-022-24024-9 | DOI Listing |
J Coll Physicians Surg Pak
January 2025
Department of Pathology, National Institute of Cardiovascular Diseases, Karachi, Pakistan.
Objective: To determine the frequency of multidrug-resistant (MDR) bacterial isolates in respiratory specimens obtained from ventilated patients admitted to critical care units at the National Institute of Cardiovascular Diseases (NICVD), along with COVID-19-positive cases.
Study Design: An observational study. Place and Duration of the Study: National Institute of Cardiovascular Diseases, between November 2021 and March 2022.
Crit Care
January 2025
Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
Background: Carbapenem-Resistant Gram-Negative Bacteria, including Carbapenem-Resistant Enterobacterales (CRE) and Carbapenem-Resistant Pseudomonas aeruginosa (CRPA), are common causes of infections in intensive care units (ICUs) in Italy.
Objective: This prospective observational study evaluated the epidemiology, management, microbiological characterization, and outcomes of hospital-acquired CRE or CRPA infections treated in selected ICUs in Italy.
Methods: The study included patients with hospital-acquired infections due to CRE and CRPA treated in 20 ICUs from June 2021 to February 2023.
BMC Med Inform Decis Mak
January 2025
Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.
View Article and Find Full Text PDFObjectives: To analyze the clinical and biological characteristics and to evaluate the risk factors associated with the mortality of patients with COVID-19 in Commune IV of the District of Bamako.
Methods: The cohort consisted of COVID-19 patients managed from March 2020 to June 2022 at the Bamako Dermatology Hospital and the Pasteur Polyclinic in Commune IV in Bamako. The studied variables were sociodemographic, clinical, and biological.
Nat Commun
January 2025
Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.
Animal models that accurately reflect COVID-19 are vital for understanding mechanisms of disease and advancing development of improved vaccines and therapeutics. Pigs are increasingly recognized as valuable models for human disease due to their genetic, anatomical, physiological, and immunological similarities to humans, and they present a more ethically viable alternative to non-human primates. However, pigs are not susceptible to SARS-CoV-2 infection which limits their utility as a model.
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