Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis

Public Health ◽  
2021 ◽  
Vol 201 ◽  
pp. 108-114
Author(s):  
Xiaojun Guo ◽  
Houxue Shen ◽  
Sifeng Liu ◽  
Naiming Xie ◽  
Yingjie Yang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Memory System ◽  
System Model

scholarly journals Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19

2021 ◽  
Vol 18 (9) ◽  
pp. 4432
Author(s):  
Ronald Manríquez ◽  
Camilo Guerrero-Nancuante ◽  
Felipe Martínez ◽  
Carla Taramasco
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Preventive Measures ◽  
Weighted Graph ◽  
Real Data ◽  
Sir Model ◽  
Weighted Graphs ◽  
Epidemic Disease ◽  
The Real

The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19’s pandemic context.

Verification of Plastic Interactive Systems

i-com ◽  
2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Raquel Oliveira ◽  
Sophie Dupuy-Chessa ◽  
Gaëlle Calvary
Keyword(s):  
Power Plant ◽  
User Interfaces ◽  
Nuclear Power ◽  
Interactive Systems ◽  
System Model ◽  
Equivalence Checking ◽  
The Past ◽  
Different Levels ◽  
Formal Techniques

AbstractInteractive systems have largely evolved over the past years. Nowadays, different users can interact with systems on different devices and in different environments. The user interfaces (UIs) are expected to cope with such variety. Plastic UIs have the capacity to adapt to changes in their context of use while preserving usability. Such capability enhances UIs, however, it adds complexity on them. We propose an approach to verifying interactive systems considering this adaptation capability of the UIs. The approach applies two formal techniques: model checking, to the verification of properties over the system model, and equivalence checking, to compare different versions of a UI, thereby identifying different levels of UI equivalence. We apply the approach to a case study in the nuclear power plant domain in which several UI are analyzed, properties are verified, and the level of equivalence between them is demonstrated.

scholarly journals Hierarchical Information Entropy System Model for TWfMS

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 732
Author(s):  
Qiang Han ◽  
Deren Yang
Keyword(s):  
Information Entropy ◽  
Dissipative Structure ◽  
Workflow Management ◽  
Fundamental Principle ◽  
System Model ◽  
Transformation Rule ◽  
Entropy Model ◽  
Research Areas ◽  
System Service

Under the infrastructure of three gradually deepening layers consisting of System, Service and Software, the information entropy of the Trustworthy Workflow Management System (TWfMS) will evolve from being more precise to more undetermined, due to a series of exception event X occurring on certain components (ExCs), along with the life cycle of TWfMS, experienced in its phased original, as-is, to-be, and agile-consistent stages, and recover, more precisely again, by turning back to the original state from the agile-consistent stage, due to its self-autonomous improvement. With a special emphasis on the system layer, to assure the trustworthiness of WfMS, this paper firstly introduces the preliminary knowledge of the hierarchical information entropy model with correlation theories. After illustrating the fundamental principle, the transformation rule is deduced, step by step, followed by a case study, which is conducive to generating discussions and conclusions in the different research areas of TWfMS. Overall, in this paper, we argue that the trustworthiness maintenance of WfMS could be analyzed and computational, through the viewpoint that all the various states of TWfMS can be considered as the transformation between WfMS and its trustworthiness compensate components, whose information entropy fluctuate repeatedly and comply with the law of the dissipative structure system.

scholarly journals Reducing bias in risk indices for COVID-19

2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Michał Paweł Michalak ◽  
Jack Cordes ◽  
Agnieszka Kulawik ◽  
Sławomir Sitek ◽  
Sławomir Pytel ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Spatial Heterogeneity ◽  
Disease Burden ◽  
Risk Indices ◽  
Study Evaluation ◽  
Dynamical Properties ◽  
Time Periods ◽  
Regional Disease ◽  
Over Time

Spatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.

scholarly journals GIS-BASED INFECTIOUS DISEASE DATA MANAGEMENT ON A CITY SCALE, CASE STUDY OF ST. PETERSBURG, RUSSIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1463-1467
Author(s):  
I. Kuznetsov ◽  
E. Panidi ◽  
A. Kolesnikov ◽  
P. Kikin ◽  
V. Korovka ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Geographical Information Systems ◽  
Medical Data ◽  
Geographical Information ◽  
Infrastructure Development ◽  
Collection System ◽  
Medical Infrastructure ◽  
Availability Assessment ◽  
The City

Abstract. Medical geography and medical cartography can be denoted as classical application domains for Geographical Information Systems (GISs). GISs can be applied to retrospective analysis (e.g., human population health analysis, medical infrastructure development and availability assessment, etc.), and to operative disaster detection and management (e.g., monitoring of epidemics development and infectious diseases spread). Nevertheless, GISs still not a daily-used instrument of medical administrations, especially on the city and municipality scales. In different regions of the world situation varies, however in general case GIS-based medical data accounting and management is the object of interest for researchers and national administrations operated on global and national scales. Our study is focused onto the investigation and design of the methodology and software prototype for GIS-based support of medical administration and planning on a city scale when accounting and controlling infectious diseases. The study area is the administrative territory of the St. Petersburg (Russia). The study is based upon the medical statistics data and data collection system of the St. Petersburg city. All the medical data used in the study are impersonalized accordingly to the Russian laws.

A Computer-Aided Conceptual Design System: Model and Applications

2011 ◽  
Vol 308-310 ◽  
pp. 538-541
Author(s):  
Yuan Chen
Keyword(s):  
Mechanism Design ◽  
Conceptual Design ◽  
System Model ◽  
Design System ◽  
Modeling Framework ◽  
System A ◽  
Principle Solution ◽  
Computer Aided

An effort is made to give a description of a computer-aided conceptual design system. A novel Function-Action-Behavior-Mechanism (FABM) modeling framework is proposed to realize mapping from the overall function to principle solution according to customer’s requirements. Expansion and modification rules in the demand behavior are developed to extend the innovation of principle solution. A case study on pan mechanism design for cooking robot is presented to show the procedure of how to implement the intelligent reasoning based on the FABM model.

Enhancing Student Learning on Emerging Infectious Diseases: An Ebola Exemplar

2018 ◽  
Vol 80 (7) ◽  
pp. 493-500
Author(s):  
Derek Dube ◽  
Tracie M. Addy ◽  
Maria R. Teixeira ◽  
Linda M. Iadarola
Keyword(s):  
Infectious Diseases ◽  
Undergraduate Students ◽  
Ebola Virus ◽  
Influenza Pandemic ◽  
Emerging Infectious Diseases ◽  
Health Science ◽  
Science Courses ◽  
Study Approach ◽  
Mode Of Instruction

Throughout global history, various infectious diseases have emerged as particularly relevant within an era. Some examples include the Bubonic plague of the fourteenth century, the Spanish Influenza pandemic of 1918, the HIV epidemic of the 1980s, and the Zika virus outbreak in 2015–16. These instances of emerging infectious disease represent ideal opportunities for timely, relevant instruction in natural and health science courses through case studies. Such instructional approaches can promote student engagement in the material and encourage application and higher-order thinking. We describe here how the case study approach was utilized to teach students about emerging infectious diseases using the 2014–16 Ebola virus outbreak as the subject of instruction. Results suggest that students completing the case study not only had positive perceptions of the mode of instruction, but also realized learning gains and misconception resolution. These outcomes support the efficacy of case pedagogy as a useful teaching tool in emerging infectious diseases, and augment the paucity of literature examining Ebola virus knowledge and misconceptions among undergraduate students within United States institutions.

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