Group17 proposal

Proposal

Poster

Application

Research Paper

Overview

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) emerged as a global health concern in 2012 when the first human case was documented in Saudi Arabia. Then listed as one of the WHO Research and Development Blueprint priority pathogens, cases were reported in 27 countries across four continents. Imported cases into non-endemic countries such as France, Great Britain, the United States, and South Korea had caused secondary cases, thus highlighting the spread of MERS-CoV far beyond the countries where index cases originated. Reports in animals showed that viral circulation was far more widespread than suggested by human cases alone. In this project, we aim to analyse the spread of MERS-CoV and the factors affecting it's intensity.

Project Motivation

With the recent emergence of Covid virus, containing the epidemic requires an understanding of how corona virus spreads, and factors impacting the intensity of cases within and across regions. This project aims at delivering an R shiny app that first provides a basic understanding of the nature of the virus, e.g. the types of pathogens identified in MERS, the kinds of organisms which are susceptible to MERS contraction, and time series analysis to visualize the evolution of MERS outbreak across a 6-years time period (2012-2018). The detailed description of these variables are shown in the Section:Data Description. Geospatio-temporal analysis will be performed to identify the intensity of outbreak in different regions across time. Finally, we will further deep-dive into how certain factors intensifies the spread of the disease using spatial-join analysis.

Proposed Analytical Methods & Visualisation

1. Exploratory Data Analysis

Radar Chart : A radar chart will be used to do multi-variate analysis, for instance, to show the different event types of the armed conflict over different countries in South Asia.

Line Chart : A line chart will be used to visualize the total number of incidents and the number of fatalities in those incidents over different periods of time.

Slope Chart : Compares the ranking of countries over time and intensity of armed conflicts in South Asia over time to get a glimpse of the time series data.

2. Spatio-Temporal Analysis

Spatial Temporal is used to analyse the data across both space and time at the same time. The intent of this analysis is to describe the armed conflicts at a certain location and time. With the help of interactivity in the visualization, the user will be provided with the ability to customize the location and time in the spatial temporal analysis.

To establish this analysis, point pattern analysis will be used to study the spatial arrangement of points in a 2 dimensional space. The spatial temporal analysis will be linked to a study region linked to the point pattern analysis.

Finally a kernel density plot will be used to highlight the density of the events in the selected filters through a heat-map. The kernel approach computes the localized density of the subsets of the study area.

3. Spatial Join Analysis Visualisations TBC

Data Description

This database is publicly available online. Each of the 861 rows represents a unique occurrence of MERS-CoV. Rows containing an index, unspecified, or imported case represent a single case of MERS-CoV. Rows containing mammal and secondary cases may represent more than one case but are still unique geospatial occurrences.

Data Fields	Description	Example	Datatype
nid	A unique identifier assigned to each publication that was extracted	364253	Numeric
occ_id	Unique identifier assigned to each occurrence of MERS-CoV. A single pdf may represent more than one occurrence. Each row will have its own occ_id, starting at 1 and numbered consecutively to 883.	1	Numeric
organism_type	What type of organism tested positive for MERS-CoV (human, mammal, or environmental).	human	Categorical
organism_specific	Specifies the exact organism that tested positive for MERS-CoV. Names are made consistent with Wilson and Reeder (2005) Mammal Species of the World.	Homo sapiens	Categorical
lat	This field records the latitude in decimal degrees.	30.209423	Numeric
long	This field records the longitude in decimal degrees.	67.018009	Numeric
pathogen	Name the pathogen identified (e.g. MERS-CoV, Bat Coronaviruses, and other MERS-CoV-like pathogens).	MERS-CoV	Categorical
patient_type	index, unspecified, NA, secondary, import, or absent.	index	Categorical
transmission_route	zoonotic, direct, unspecified, or animal-to-animal.	direct	Categorical
country	ISO3 code for country in which the case occurred.	KOR	Categorical
origin	Open-ended field to provide more details on the specific in-country location of MERS-CoV case.	Jordan	Categorical
loc_confidence	States the level of confidence that researchers had when assigning a geographic location to the MERS-CoV case (good or bad).	good	Categorical
month_start	Month that the occurrence(s) began.	1	Date Time
month_end	Month that the occurrence(s) ended.	1	Date Time
year_start	year that the occurrence(s) began.	2013	Year
year_end	year that the occurrence(s) ended.	2012	Year
year_accuracy	If years were reported, this field was assigned a value of ‘0’. If assumptions were required, this field was assigned a value of ‘1’.	0	Numeric

Software Tools

• RStudio: https://rstudio.com/

Proposed R Packages

Packages	Purpose
plotly()	To help with creating visuals for exploratory analysis
ggplot2()	To create elegant data visualizations using grammar of graphics
trelliscope()	To create interactive trelliscope displays
tidyverse()	To do data manipulation and exploration with dplyr() etc
gganimate()	To create plots with animation
leaflet()	To create maps within the application
spatstat()	To analyse spatial data
ads()	To analyse geographical data for spatial point pattern analysis
GeoXB()	To create interactive spatial exploratory data analysis
Shiny()	To create interactive web application for the final product

References

1. https://acleddata.com/#/dashboard 2. https://mgimond.github.io/Spatial/point-pattern-analysis.html 3. https://en.wikipedia.org/wiki/Point_pattern_analysis 4. https://www.omnisci.com/technical-glossary/spatial-temporal

Team Members

• Oishee Bhattacharyya
• Jaideep Ballani
• Denise Adele Chua Hui Shan