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.

scholarly journals Geographical surveillance of COVID-19: Diagnosed cases and death in the United States

2020 ◽  
Author(s):  
Raid Amin ◽  
Terri Hall ◽  
Jacob Church ◽  
Daniela Schlierf ◽  
Martin Kulldorff
Keyword(s):  
Public Health ◽  
New York ◽  
New England ◽  
Disease Surveillance ◽  
Multiple Testing ◽  
The United States ◽  
Space Time ◽  
Scan Statistics ◽  
Public Health Agencies ◽  
Incidence And Mortality

AbstractBackgroundCOVID-19 is a new coronavirus that has spread from person to person throughout the world. Geographical disease surveillance is a powerful tool to monitor the spread of epidemics and pandemic, providing important information on the location of new hot-spots, assisting public health agencies to implement targeted approaches to minimize mortality.MethodsCounty level data from January 22-April 28 was downloaded from USAfacts.org to create heat maps with ArcMap™ for diagnosed COVID-19 cases and mortality. The data was analyzed using spatial and space-time scan statistics and the SaTScan™ software, to detect geographical cluster with high incidence and mortality, adjusting for multiple testing. Analyses were adjusted for age. While the spatial clusters represent counties with unusually high counts of COVID-19 when averaged over the time period January 22-April 20, the space-time clusters allow us to identify groups of counties in which there exists a significant change over time.ResultsThere were several statistically significant COVID-19 clusters for both incidence and mortality. Top clusters with high rates included the areas in and around New York City, New Orleans and Chicago, but there were also several small rural clusters. Top clusters for a recent surge in incidence and mortality included large parts of the Midwest, the Mid-Atlantic Region, and several smaller areas in and around New York and New England.ConclusionsSpatial and space-time surveillance of COVID-19 can be useful for public health departments in their efforts to minimize mortality from the disease. It can also be applied to smaller regions with more granular data.

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 Leveraging media informatics for the surveillance and understanding of disease outbreaks

2019 ◽  
Vol 115 (3/4) ◽  
Author(s):  
Bankole Falade
Keyword(s):  
New York ◽  
Mass Media ◽  
Disease Surveillance ◽  
Risk Perceptions ◽  
Religious Beliefs ◽  
New York Times ◽  
Disease Outbreaks ◽  
Google Trends ◽  
Aberration Detection ◽  
Empirical Equivalence

Syndromic disease surveillance mechanisms can be enhanced by incorporating mass media informatics for disease discourse and aberration detection and social psychology for understanding risk perceptions and the drivers of uptake and resistance. Using computerised text analysis, the coverage of the outbreak of Zika virus in Brazil in 2017/2018 in four newspapers – O Estado, O Globo, the Times of London and the New York Times – was examined and patterns were compared with Google Trends. Quantitative indicators showed waves of attention to Zika peaked in the same period but local newspapers, O Estado and O Globo, indicated lower levels of anxiety in the run up to the Olympics when compared with foreign media. The unusual surge in attention to dengue in early 2015 was an early indication to sound the alarm for extensive clinical investigations. This, together with the flagging of Zika by O Globo almost a year before the global alarm, indicates the suitability of this method for surveillance and detection of aberrations. Media attention waves are also significantly associated with Google Trends, indicating empirical equivalence. Qualitative indicators show the extra motivation over Google, World Wide Web or Twitter searches by highlighting public perceptions. Findings show the absence of a stable body of scientific knowledge at the outbreak and an ensuing crisis of understanding. Local concerns were about the economic crisis, religious beliefs, poverty and crime – all inhibitors to containment – while the global alarm was amplified by risk to tourists and athletes, and political disputes mixed with religious beliefs. Significance: This study contributes to research on the use of longitudinal media data as surrogate sources for syndromic disease surveillance. Mass media informatics provide empirical equivalence to Google Trends. Clinical and non-clinical factors contributed to public anxiety over disease epidemics. Lack of clinical knowledge at the onset of the crisis contributed to anxiety among scientists and the public.

Inhaltsanalyse elektronisch gespeicherter Massendaten der internationalen Presse

2003 ◽  
Vol 15 (3) ◽  
pp. 98-105 ◽  
Author(s):  
Mark Galliker ◽  
Jan Herman
Keyword(s):  
New York ◽  
New York Times

Zusammenfassung. Am Beispiel der Repräsentation von Mann und Frau in der Times und in der New York Times wird ein inhaltsanalytisches Verfahren vorgestellt, das sich besonders für die Untersuchung elektronisch gespeicherter Printmedien eignet. Unter Co-Occurrence-Analyse wird die systematische Untersuchung verbaler Kombinationen pro Zähleinheit verstanden. Diskutiert wird das Problem der Auswahl der bei der Auswertung und Darstellung der Ergebnisse berücksichtigten semantischen Einheiten.

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