Ebola hemorrhagic fever, caused by the highly virulent RNA virus of the filoviridae family, has become one of the world's most feared pathogens. The virus induces acute fever and death, often associated with hemorrhagic symptoms in up to 90% of infected patients. The known sub-types of the virus are Zaire, Sudan, Taï Forest, Bundibugyo and Reston Ebola viruses. In the past, outbreaks were limited to the East and Central African tropical belt with the exception of Ebola Reston outbreaks that occurred in animal facilities in the Philippines, USA and Italy. The on-going outbreak in West Africa that is causing numerous deaths and severe socio-economic challenges has resulted in widespread anxiety globally. This panic may be attributed to the intense media interest, the rapid spread of the virus to other countries like United States and Spain, and moreover, to the absence of an approved treatment or vaccine. Informed by this widespread fear and anxiety, we analyzed the commonly used strategies to manage and control Ebola outbreaks and proposed new approaches that could improve epidemic management and control during future outbreaks. We based our recommendations on epidemic management practices employed during recent outbreaks in East, Central and West Africa, and synthesis of peer-reviewed publications as well as published "field" information from individuals and organizations recently involved in the management of Ebola epidemics. The current epidemic management approaches are largely "reactive", with containment efforts aimed at halting spread of existing outbreaks. We recommend that for better outcomes, in addition to "reactive" interventions, "pre-emptive" strategies also need to be instituted. We conclude that emphasizing both "reactive" and "pre-emptive" strategies is more likely to lead to better epidemic preparedness and response at individual, community, institutional, and government levels, resulting in timely containment of future Ebola outbreaks.
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http://dx.doi.org/10.1016/j.bjid.2015.02.004 | DOI Listing |
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
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December 2024
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S.
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December 2024
School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, China.
Fine particulate matter has been linked with acute coronary syndrome. Nevertheless, the key constituents remain unclear. Here, we conduct a nationwide case-crossover study in China during 2015-2021 to quantify the associations between fine particulate matter constituents (organic matter, black carbon, nitrate, sulfate, and ammonium) and acute coronary syndrome, and to identify the critical contributors.
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December 2024
Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health & Veterinary Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK.
Rabies is a viral zoonosis that kills thousands of people annually in low- and middle-income countries across Africa and Asia where domestic dogs are the reservoir. 'Zero by 30', the global strategy to end dog-mediated human rabies, promotes a One Health approach underpinned by mass dog vaccination, post-exposure vaccination of bite victims, robust surveillance and community engagement. Using Integrated Bite Case Management (IBCM) and whole genome sequencing (WGS), we enhanced rabies surveillance to detect an outbreak in a formerly rabies-free island province in the Philippines.
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