scholarly journals Scalable Analysis of COVID-19 Spatiotemporal Patterns Based on Data Mining Tools: Using 3D Bins to Predict Short-time Focus Locations

2021 ◽  
Author(s):  
Olga de Cos ◽  
Valentín Castillo ◽  
David Cantarero
Keyword(s):  
Data Mining ◽  
Spatial Pattern ◽  
Statistical Significance ◽  
Temporal Trends ◽  
Spatial Association ◽  
Space Time ◽  
Spatiotemporal Patterns ◽  
Prevention Measures ◽  
3D Space ◽  
Mining Tools

Abstract Background: An interesting research line is related to COVID-19 behavior from a territorial and temporal perspective. Hence, the use of 3D space-time bins is a useful tool to contrast limitations of visual assessment and reveal the detailed areas most at risk for the pandemic or even more the emergency hotspots can be useful to not only study but also predict spatial pattern of the COVID-19 at an intra-urban scale.Methods: We developed the SITAR Fast Action Territorial Information System using ESRI Technologies Ecosystem. More specifically, we used ArcGIS Pro (desktop) and ArcGIS Online (cloud). Therefore, our general research methodology is based on Geographic Information Technologies from a multiscalar perspective and based on detailed entities (geocoded COVID-19 cases for the region of Cantabria, Spain). The main research method is related to data mining tools using 3D bins and analysing emerging hotspots.Results: The spatial autocorrelation analysis of the COVID-19 reveals that the distribution of the cases is not random. Otherwise, the Moran´s Index confirms that the spatial pattern of COVID-19 cases is statistically significative, and it presents a clustered distribution. And in the cases of elderly homes, COVID-19 outbreaks and spatial focus are linked while in the rest of the cases there is not this spatial association. The analysis of 3D bins and emerging hotspots is revealing from the point of view of geoprevention in that it significantly limits the territory on which it would be important to focus the analysis. In fact, of the 1,414 starting cubes, focusing on the 602 remaining cubes (with statistical significance), all correspond to a hotspot pattern.Conclusions: Our results evidence the existence of significant space-temporal trends that it can serve as support of emerging hotspots of COVID-19 that it can be used as a prelude to what will happen in the next future. To our knowledge, this is the first study for Spain that demonstrates the interest of the 3D space-time cubes method to engage the prevention measures proposed by policy makers with a scalar perspective. 3D bins can therefore be used as a proxy to assess the spatiotemporal patterns in public health studies.

scholarly journals Space-time Classification Index for Assessing COVID-19 Hotspots

2021 ◽  
Author(s):  
David Haynes ◽  
Chetan Tiwari
Keyword(s):  
New York ◽  
Disease Surveillance ◽  
Spatial Information ◽  
Assessment Tool ◽  
Temporal Trends ◽  
Spatial Association ◽  
New York Times ◽  
Space Time ◽  
Temporal Duration ◽  
Classification Index

Objectives: To develop new methods to address problems associated with use of traditional measures of disease surveillance, including prevalence and positivity rates. Methods: We use data from the public New York Times Github repository to develop a space-time classification index of COVID-19 hotspots. The Local Indicator of Spatial Association (LISA) statistic is applied to identify daily clusters of COVID-19 cases, from July 4th to July 19th. Results: The classification index is a spatial and temporal assessment tool that seeks to incorporate temporal trends of the clusters that are "high-high" and "high-low". Two classifications support the index: severity and temporal duration. We define severity as the number of times a county is statistically significant and temporal duration captures the number of consecutive days a county is a hotspot. Conclusions: The space-time classification index provides a statistically robust measure of the spatial patterns of COVID-19 hotspots. Spatial information is not captured through measures like the positivity rate, which merely divides the number of cases by tests conducted. The index proposed in this paper can guide intervention efforts by classifying counties with six-levels of importance.

Revealing the Origin and Nature of Drug Resistance of Dynamic Tumour Systems

2013 ◽  
pp. 354-382
Author(s):  
Ricardo Santiago-Mozos ◽  
Imtiaz A. Khan ◽  
Michael G. Madden
Keyword(s):  
Data Mining ◽  
Cytotoxic Effect ◽  
Statistical Significance ◽  
Cell Lineage ◽  
Clonal Expansion ◽  
Interpretable Models ◽  
Microscopy Data ◽  
Mining Tools ◽  
Over Time

In this paper, the authors identify the strategies that resistant subpopulations of cancer cells undertake to overcome the effect of the anticancer drug Topotecan. For the analyses of cell lineage data encoded from timelapse microscopy, data mining tools are chosen that generate interpretable models of the data, addressing their statistical significance. By interpreting the short-term and long-term cytotoxic effect of Topotecan through these data models, the authors reveal the strategies that resistant subpopulations of cells undertake to maximize their clonal expansion potential. In this context, this paper identifies a pattern of cell death independent of cytotoxic effect. Finally, it is observed that cells exposed to Topotecan have higher movement over time, indicating a putative relationship between cytotoxic effect and cell motility.

scholarly journals Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases

2021 ◽  
Vol 10 (4) ◽  
pp. 261
Author(s):  
Olga De Cos ◽  
Valentín Castillo ◽  
David Cantarero
Keyword(s):  
At Risk ◽  
Information System ◽  
Temporal Trends ◽  
Space Time ◽  
Time Behaviour ◽  
Testing Method ◽  
3D Space ◽  
The Difference

The space–time behaviour of COVID-19 needs to be analysed from microdata to understand the spread of the virus. Hence, 3D space–time bins and analysis of associated emerging hotspots are useful methods for revealing the areas most at risk from the pandemic. To implement these methods, we have developed the SITAR Fast Action Territorial Information System using ESRI technologies. We first modelled emerging hotspots of COVID-19 geocoded cases for the region of Cantabria (Spain), then tested the predictive potential of the method with the accumulated cases for two months ahead. The results reveal the difference in risk associated with areas with COVID-19 cases. The study not only distinguishes whether a bin is statistically significant, but also identifies temporal trends: a reiterative pattern is detected in 58.31% of statistically significant bins (most with oscillating behaviour over the period). In the testing method phase, with positive cases for two months ahead, we found that only 7.37% of cases were located outside the initial 3D bins. Furthermore, 83.02% of new cases were in statistically significant previous emerging hotspots. To our knowledge, this is the first study to show the usefulness of the 3D bins and GIS emerging hotspots model of COVID-19 microdata in revealing strategic patterns of the pandemic for geoprevention plans.

scholarly journals Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 562
Author(s):  
Jorge Moreda-Piñeiro ◽  
Joel Sánchez-Piñero ◽  
María Fernández-Amado ◽  
Paula Costa-Tomé ◽  
Nuria Gallego-Fernández ◽  
...  
Keyword(s):  
Air Quality ◽  
Road Traffic ◽  
Statistical Significance ◽  
Temporal Trends ◽  
Air Pollutant ◽  
Concentration Data ◽  
Southwest Atlantic ◽  
Pollutant Concentrations ◽  
Air Quality Monitoring Stations ◽  
Almost All

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

scholarly journals Space-time cluster analysis of American visceral leishmaniasis in Bauru, São Paulo State, Brazil

2012 ◽  
Vol 28 (10) ◽  
pp. 1949-1964 ◽  
Author(s):  
Vanessa Aparecida Feijó de Souza ◽  
Luiz Ricardo Paes de Barros Cortez ◽  
Ricardo Augusto Dias ◽  
Marcos Amaku ◽  
José Soares Ferreira Neto ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Urban Areas ◽  
Sao Paulo ◽  
Space Time ◽  
São Paulo ◽  
Prevention Measures ◽  
Paulo State ◽  
Positive Tendency ◽  
São Paulo State ◽  
The City

A space-time analysis of American visceral leishmaniasis (AVL) in humans in the city of Bauru, São Paulo State, Brazil was carried out based on 239 cases diagnosed between June 2003 and October 2008. Spatial analysis of the disease showed that cases occurred especially in the city's urban areas. AVL annual incidence rates were calculated, demonstrating that the highest rate occurred in 2006 (19.55/100,000 inhabitants). This finding was confirmed by the time series analysis, which also showed a positive tendency over the period analyzed. The present study allows us to conclude that the disease was clustered in the Southwest side of the city in 2006, suggesting that this area may require special attention with regard to control and prevention measures.

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