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 Radar Chart on Pathogen Types

Line Chart (Trellis): To visualise the number of MERS cases over time The line chart will be partitioned by organism_type variable (records the type of organism on which MERS was tested positive)

Slope Chart Compares the ranking/ intensity of MERS incidents in each region over time. Reference: https://www.r-bloggers.com/creating-slopegraphs-with-r/

2. Spatio-Temporal Analysis (Describe point pattern analysis here) Kernel Density Plot 3D Chart (latitude, longitude and time)

3. Spatial Join Analysis Visualisations TBC

Data Description

The database used for this project is derived from Global Terrorism Database (GTD) handled by the University of Maryland. The database is very comprehensive and includes the repository of terrorist activities starting from 1970 to 2015. For the purposes of this project, the dataset has been filtered to the years 2012 to 2015. Some of the important variables that have been taken into account for this analysis is as mentioned below:

Data Fields	Description	Example	Datatype
GTD ID	Incidents from the GTD follow a 12‐digit Event ID system, wherein first 8 numbers are for the date recorded and last 4 numbers for sequential case number for the given day (0001, 0002 etc).	199307250001	Numeric
iyear, imonth, iday	These fields contain the dates and hence will be merged to get the date field.	2011-02-03	Numeric
country	This field identifies the country or location where the incident occurred.	Afghanistan	Categorical
region	This field identifies the region in which the incident occurred.	North America	Categorical
latitude	This field records the latitude.	30.209423	Numeric
longitude	This field records the longitude.	67.018009	Numeric
attacktype1	This field captures the general method of attack and often reflects the broad class of tactics used.	Assassination	Categorical
weaptype1	This field records the general type of weapon used in the incident.	Biological	Categorical
targtype1	The target/victim type field captures the general type of target/victim.	Business	Categorical
gname	This field contains the name of the group that carried out the attack.	Al-Shabaab	Text variable
nkill	This field stores the number of total confirmed fatalities for the incident.	4	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://www.nature.com/articles/s41597-019-0330-0#Tab1

Team Members

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