This booklet contains the basic data and graphs forCOVID-19 in Iraq during the first three months of thepandemic ( 24 February to 19 May - 2020 ) , It isperformed to help researchers regarding this health problem (PDF) Information Booklet COVID-19 Graphs For Iraq First 3 Months. Available from: https://www.researchgate.net/publication/341655944_Information_Booklet_COVID-19_Graphs_For_Iraq_First_3_Months#fullTextFileContent [accessed Oct 26 2024].

Objective: To review and identify the major drivers for COVID-19 vaccine acceptance. Methods: A scoping review of studies of COVID-19 vaccine perceptions and barriers to using the COVID-19 vaccines. Two search engines, including PubMed and Google Scholar, were purposefully searched. Results: Eight studies from different countries were reviewed to categorize factors influencing people's acceptance of COVID-19 according to the Health Belief Model (HBM). Perceived susceptibility, and severity of the disease (COVID-19), in addition to perceived benefits of COVID-19 vaccination and "cues to action", can enhance vaccination acceptance. In contrast, perceived barriers to the COVID-19 vaccine can increase people's hesitancy to be vaccinated

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

 The objectives of this research are to determine and find out the reality of crops structure of greenhouses in association of Al-Watan  in order to stand on the optimal use of economic resources available for the purpose of reaching a crop structure optimization of the farm that achieves maximize profit and gross and net farm incomes , using the method of linear programming to choose the farm optimal plan with the highest net income , as well as identifying production plans farm efficient with (income - deviation) optimal (E-A) of the Association and derived, which takes into account the margin risk wich derived from each plan using the model( MOTAD), as a model of models of linear programming alternative programming m

Background: Coronavirus disease 2019 (COVID-19) is
one of the updated challenges facing the whole world.
Objective: To identify the characteristics risk factors that
present in humans to be more liable to get an infection
than others.
Methods: A cross-sectional study was conducted for
positively confirmed 35 patients with polymerase chain
reaction in Wasit province at AL-Zahraa Teaching
Hospital from the period of March 13th till April 20th. All
of them full a questionnaire regarded by risk factors and
other comorbidities. Data were analyzed by SPSS version
23 using frequency tables and percentage. For numerical
data, the median, and interquartile range (IQR) were used.
Differences between categoric

Background: since December 2019, China and in particularly Wuhan, faced an unprecedented an outbreak challenge of coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2. Clinical characteristics of Iraqi patients with COVID-19 and risk factors for mortality needed to be shared with the health care providers to improve the overall disease experience. Methods: prospective, single-center study recruited patients with confirmed SARS-CoV-2 infection who were admitted to Al-Shifaa Isolation Center / Baghdad Medical City between the mid of March and the end of April 2020 until had been discharged or had died. Demographic data, information on clinical signs, symptoms, at presentation, treatment, have been collected