scholarly journals Visualizing Spatiotemporal Epidemic Clusters on a Map-based Dashboard: A case study of early COVID-19 cases in Singapore

2021 ◽  
Vol 4 ◽  
pp. 1-5
Author(s):  
Hui Zhang ◽  
Chenyu Zuo ◽  
Linfang Ding
Keyword(s):  
Infectious Diseases ◽  
Spatiotemporal Distribution ◽  
Ministry Of Health ◽  
Test Dataset ◽  
Spatiotemporal Characteristics ◽  
Spatiotemporal Clustering ◽  
Interactive Map ◽  
Clustering Patterns

Abstract. Spatiotemporal distribution of the epidemic data plays an important role in its understanding and prediction. In order to understand the transmission patterns of infectious diseases in a more intuitive way, many works applied various visualizations to show the epidemic datasets. However, most of them focus on visualizing the epidemic information at the overall level such as the confirmed counts each country, while spending less effort on powering user to effectively understand and reason the very large and complex epidemic datasets through flexible interactions. In this paper, the authors proposed a novel map-based dashboard for visualizing and analyzing spatiotemporal clustering patterns and transmission chains of epidemic data. We used 102 confirmed cases officially reported by the Ministry of Health in Singapore as the test dataset. This experiment shown that the well-designed and interactive map-based dashboard is effective in shorten the time that users required to mine the spatiotemporal characteristics and transmission chains behind the textual and numerical epidemic data.

scholarly journals Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

2020 ◽  
Vol 9 (7) ◽  
pp. 440
Author(s):  
Junfang Gong ◽  
Jay Lee ◽  
Shunping Zhou ◽  
Shengwen Li
Keyword(s):  
Human Activity ◽  
Simulated Data ◽  
Spatiotemporal Pattern ◽  
Time Period ◽  
Spatiotemporal Clustering ◽  
Categorical Attributes ◽  
Treat All ◽  
Clustering Patterns ◽  
2D To 3D

Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency.

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.

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 How to overcome hypogalactia? Prevention and successful management of hypogalactia

2019 ◽  
pp. 21-24
Author(s):  
Article Editorial
Keyword(s):  
Practical Significance ◽  
Psychomotor Development ◽  
First Year ◽  
Successful Management ◽  
Ministry Of Health ◽  
Clinical Case Study ◽  
Problems And Solutions ◽  
The Republic ◽  
Medical University

In October 2018, a symposium for neonatologists and pediatricians «Child of the first year. Problems and Solutions» was held in the framework of the 17th Russian Congress «Innovative Technologies in Pediatrics and Pediatric Surgery». The problem of breastfeeding of newborns and its importance for the proper and harmonious development of the child was raised along with such relevant topics as the dynamics of the psychomotor development of prematurely born children, practical significance of findings of neurosonography, comprehensive assistance to children with disabilities. Report on the topic «How to overcome hypogalactia? Clinical case study, prevention and successful management of hypogalactia» was presented by Zhdanova Svetlana Igorevna, Cand. of Sci. (Med.), a neonatologist, the Republican Clinical Hospital of the Ministry of Health of the Republic of Tatarstan, Assistant of Chair of Hospital Pediatrics with a Course of Outpatient Pediatrics in Kazan State Medical University of the Ministry of Health of Russia.

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.

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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