scholarly journals The bumblebee Bombus terrestris carries a primary inoculum of Tomato brown rugose fruit virus contributing to disease spread in tomatoes

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210871 ◽  
Author(s):  
Naama Levitzky ◽  
Elisheva Smith ◽  
Oded Lachman ◽  
Neta Luria ◽  
Yaniv Mizrahi ◽  
...  
Keyword(s):  
Bombus Terrestris ◽  
Disease Spread ◽  
Primary Inoculum

NUMERICAL AND STABILITY ANALYSIS OF THE TRANSMISSION DYNAMICS OF SVIR EPIDEMIC MODEL WITH STANDARD INCIDENCE RATE

2019 ◽  
Vol 4 (2) ◽  
pp. 349 ◽  
Author(s):  
Oluwatayo Michael Ogunmiloro ◽  
Fatima Ohunene Abedo ◽  
Hammed Kareem
Keyword(s):  
Reproduction Number ◽  
Disease Spread ◽  
Asymptotically Stable ◽  
Equilibrium Solutions ◽  
Transform Method ◽  
Globally Asymptotically Stable ◽  
Differential Transform ◽  
Mathematical Techniques ◽  
Fourth Order Method ◽  
Theoretical Results

In this article, a Susceptible – Vaccinated – Infected – Recovered (SVIR) model is formulated and analysed using comprehensive mathematical techniques. The vaccination class is primarily considered as means of controlling the disease spread. The basic reproduction number (Ro) of the model is obtained, where it was shown that if Ro<1, at the model equilibrium solutions when infection is present and absent, the infection- free equilibrium is both locally and globally asymptotically stable. Also, if Ro>1, the endemic equilibrium solution is locally asymptotically stable. Furthermore, the analytical solution of the model was carried out using the Differential Transform Method (DTM) and Runge - Kutta fourth-order method. Numerical simulations were carried out to validate the theoretical results. 

Development of the Biomaterials Technology for the Infection Resistance

2018 ◽  
Vol 24 (8) ◽  
pp. 886-895 ◽  
Author(s):  
Tengzhou Xu ◽  
Zhou Chen ◽  
Zhaofeng Chen ◽  
Yuxin Fan ◽  
Haifeng Mao
Keyword(s):  
Disease Spread ◽  
Metallic Ions ◽  
Comparative Performance ◽  
Tissue Interactions ◽  
Infection Resistance ◽  
Glass Base ◽  
Microbial Proliferation ◽  
The Common ◽  
Multicenter Clinical Trials ◽  
Technical Solutions

Infections caused by microbial proliferation are one of the common issues and serious threats to the medical care, and they usually result in disease spread. Therefore, it is a significant issue for developing the antiinfective biomaterials to control this problem, according to the specific clinical application. Meanwhile, all their properties, the best anti-infective performance, the safe biocompatibility and the appropriate tissue interactions must be conformed to each other. At present, technologies are developing novel biomaterials and surfaces endowed with anti-infective properties, relying either on bactericidal or anti-biofilm activities. This review focuses on thoroughly summarizing numerous kinds of antibacterial biomaterials, including the antibacterial matrix biomaterials, antibacterial coatings and films, nanostructured materials and antibacterial fibers. Among these strategies, the utilization of bio-glass base and graphene base antibacterial matrix, and their effects on the antibiosis mechanism were emphatically discussed. Simultaneously, the effects and mechanisms of nano-coated metallic ions are also mentioned. Overall, there is a wealth of technical solutions to contrast the establishment of an implant infection. The lack of well-structured prospective multicenter clinical trials hinders the achievement of conclusive data on the efficacy and comparative performance of antibacterial biomaterials.

COVID-19 Pandemic: Experiences in China and Implications for its Prevention and Treatment Worldwide

2020 ◽  
Vol 20 (6) ◽  
pp. 410-416 ◽  
Author(s):  
Ning-Ning Liu ◽  
Jing-Cong Tan ◽  
Jingquan Li ◽  
Shenghui Li ◽  
Yong Cai ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
International Community ◽  
Disease Spread ◽  
Global Dynamics ◽  
The World ◽  
Prevention And Treatment ◽  
History Of

The outbreak of COVID-19 due to SARS-CoV-2 originally emerged in Wuhan in December 2019. As of March 22, 2020, the disease spread to 186 countries, with at least 305,275 confirmed cases. Although there has been a decline in the spread of the disease in China, the prevalence of COVID-19 around the world remains serious despite containment efforts undertaken by national authorities and the international community. In this article, we systematically review the brief history of COVID-19 and its epidemic and clinical characteristics, highlighting the strategies used to control and prevent the disease in China, which may help other countries respond to the outbreak. This pandemic emphasizes the need to be constantly alert to shifts in both the global dynamics and the contexts of individual countries, making sure that all are aware of which approaches are successful for the prevention, containment and treatment of new diseases, and being flexible enough to adapt the responses accordingly.

Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease (Preprint)

2020 ◽  
Author(s):  
Aleksandr Farseev ◽  
Yu-Yi Chu-Farseeva ◽  
Yang Qi ◽  
Daron Benjamin Loo
Keyword(s):  
Public Health ◽  
Quantitative Research ◽  
Health Indicators ◽  
Initial Study ◽  
Disease Spread ◽  
Factors Affecting ◽  
Country Level ◽  
Development Scenarios ◽  
Health Factors ◽  
Country Clusters

UNSTRUCTURED The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

scholarly journals The case for tracking misinformation the way we track disease

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser
Keyword(s):  
Public Health ◽  
Real Time ◽  
Supervised Machine Learning ◽  
Disease Spread ◽  
The Public ◽  
Monitoring Tools ◽  
Core Function ◽  
Public Health Organizations ◽  
Front Line Workers ◽  
Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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