Difference between revisions of "Group17 proposal"

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== Data Description ==
 
== Data Description ==
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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.
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[https://acleddata.com/data-export-tool/ ACLED data] are derived from a wide range of local, regional and national sources and the information is collected by trained data experts worldwide. An updated overview of ACLED’s current coverage is available on the ACLED website. ACLED data are available to the public and are released in real-time. Data can be downloaded through the data export tool on the ACLED website or can be accessed through the API(a manual is available online). Curated data files– such as regional data files, or aggregate country-year files– can also be accessed online on the ACLED website.
  
 
{| class="wikitable" style="width: 100%; height: 14em;"
 
{| class="wikitable" style="width: 100%; height: 14em;"
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! Data Fields !! Description !! Example !! Datatype
 
! Data Fields !! Description !! Example !! Datatype
 
|-
 
|-
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| nid || A unique identifier assigned to each publication that was extracted || 364253 || Numeric
+
| iso || A numeric code for each individual country. || 50 || Numeric
 
|-
 
|-
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| 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
+
| event_id_cnty || An individual identifier by number and country acronym(updated annually). || BGD17280 || Text
 
|-
 
|-
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| organism_type || What type of organism tested positive for MERS-CoV (human, mammal, or environmental). || human || Categorical
+
| event_id_no_cnty || An individual numeric identifier(updated annually). || 17280 || Numeric
 
|-
 
|-
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| 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
+
| event_date || The day, month and year on which an event took place. || 1-FEB-20 || Date
 
|-  
 
|-  
 
| lat || This field records the latitude in decimal degrees. || 30.209423 || Numeric
 
| lat || This field records the latitude in decimal degrees. || 30.209423 || Numeric
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| long || This field records the longitude in decimal degrees. || 67.018009 || Numeric
 
| long || This field records the longitude in decimal degrees. || 67.018009 || Numeric
 
|-  
 
|-  
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| pathogen || Name the pathogen identified (e.g. MERS-CoV, Bat Coronaviruses, and other MERS-CoV-like pathogens). || MERS-CoV || Categorical
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| year || The year in which the event took place. || 2020 || Numeric
 
|-
 
|-
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| patient_type || index, unspecified, NA, secondary, import, or absent. || index || Categorical
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| time_precision || A numeric code indicating the level of certainty of the date coded for the event. || 1 || Numeric
 
|-
 
|-
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| transmission_route || zoonotic, direct, unspecified, or animal-to-animal. || direct || Categorical
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| event_type || The type of event. || Protests || Categorical
 
|-
 
|-
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| country || ISO3 code for country in which the case occurred. || KOR || Categorical
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| country || The country in which the event occurred. || Bangladesh || Categorical
 
|-
 
|-
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| origin || Open-ended field to provide more details on the specific in-country location of MERS-CoV case. || Jordan || Categorical
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| sub_event_type|| The type of sub_event. || Peaceful protest || Categorical
 
|-
 
|-
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| 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
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| actor1 || The named actor involved in the event. || Protesters (Bangladesh) || Categorical
 
|-
 
|-
−
| month_start || Month that the occurrence(s) began. || 1 || Date Time
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| assoc_actor_1 || The named actor associated with or identifying ACTOR1. || JSD: Jatiya Samajtantrik Dal || Categorical
 
|-
 
|-
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| month_end || Month that the occurrence(s) ended. || 1 || Date Time
+
| inter1 || A numeric code indicating the type of ACTOR1. || 6 || Numeric
 
|-
 
|-
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| year_start || year that the occurrence(s) began. || 2013 || Year
+
| actor2 || The named actor involved in the event. || Civilians (Pakistan) || Categorical
 
|-
 
|-
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| year_end || year that the occurrence(s) ended. || 2012 || Year
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| assoc_actor_2 || The named actor associated with or identifying ACTOR2. || BNP: Bangladesh Nationalist Party || Categorical
 
|-
 
|-
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| 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
+
| inter2 || A numeric code indicating the type of ACTOR2. || 7 || Numeric
 +
|-
 +
| interaction || A numeric code indicating the interaction between types of ACTOR1 and ACTOR2. || 67 || Numeric
 +
|-
 +
| region || The region of the world where the event took place. || Southern Asia || Categorical
 +
|-
 +
| admin1 || The largest sub-national administrative region in which the event took place. || Barisal || Categorical
 +
|-
 +
| admin2 || The second-largest sub-national administrative region in which the event took place. || Barisal || Categorical
 +
|-
 +
| admin3 || The third-largest sub-national administrative region in which the event took place. || Barisal || Categorical
 +
|-
 +
| location || The location in which the event took place. || Barisal || Categorical
 +
|-
 +
| geo_precision || A numeric code indicating the level of certainty of the location coded for the event. || 1 || Numeric
 +
|-
 +
| source || The source of the event report. || Daily Star(Bangladesh). || Categorical
 +
|-
 +
| source_scale || The scale(local, regional, national, international) of the source. || National || Categorical
 +
|-
 +
| fatalities || The number of reported fatalities which occurred during the event. || 0 || Numeric
 
|-
 
|-
 
|}
 
|}

Revision as of 10:06, 2 March 2020

Group17

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.

Example of radar chart showing multivariate analysis.

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.

Example of line chart showing multiple lines coloured by impact.

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.

Example of slope chart showing different countries over time.

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.

Example of kernel density plot

3. Spatial Join Analysis Visualisations TBC

Project Timeline

Data Description

ACLED data are derived from a wide range of local, regional and national sources and the information is collected by trained data experts worldwide. An updated overview of ACLED’s current coverage is available on the ACLED website. ACLED data are available to the public and are released in real-time. Data can be downloaded through the data export tool on the ACLED website or can be accessed through the API(a manual is available online). Curated data files– such as regional data files, or aggregate country-year files– can also be accessed online on the ACLED website.

Data Fields Description Example Datatype
iso A numeric code for each individual country. 50 Numeric
event_id_cnty An individual identifier by number and country acronym(updated annually). BGD17280 Text
event_id_no_cnty An individual numeric identifier(updated annually). 17280 Numeric
event_date The day, month and year on which an event took place. 1-FEB-20 Date
lat This field records the latitude in decimal degrees. 30.209423 Numeric
long This field records the longitude in decimal degrees. 67.018009 Numeric
year The year in which the event took place. 2020 Numeric
time_precision A numeric code indicating the level of certainty of the date coded for the event. 1 Numeric
event_type The type of event. Protests Categorical
country The country in which the event occurred. Bangladesh Categorical
sub_event_type The type of sub_event. Peaceful protest Categorical
actor1 The named actor involved in the event. Protesters (Bangladesh) Categorical
assoc_actor_1 The named actor associated with or identifying ACTOR1. JSD: Jatiya Samajtantrik Dal Categorical
inter1 A numeric code indicating the type of ACTOR1. 6 Numeric
actor2 The named actor involved in the event. Civilians (Pakistan) Categorical
assoc_actor_2 The named actor associated with or identifying ACTOR2. BNP: Bangladesh Nationalist Party Categorical
inter2 A numeric code indicating the type of ACTOR2. 7 Numeric
interaction A numeric code indicating the interaction between types of ACTOR1 and ACTOR2. 67 Numeric
region The region of the world where the event took place. Southern Asia Categorical
admin1 The largest sub-national administrative region in which the event took place. Barisal Categorical
admin2 The second-largest sub-national administrative region in which the event took place. Barisal Categorical
admin3 The third-largest sub-national administrative region in which the event took place. Barisal Categorical
location The location in which the event took place. Barisal Categorical
geo_precision A numeric code indicating the level of certainty of the location coded for the event. 1 Numeric
source The source of the event report. Daily Star(Bangladesh). Categorical
source_scale The scale(local, regional, national, international) of the source. National Categorical
fatalities The number of reported fatalities which occurred during the event. 0 Numeric

Software Tools

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
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