Geospatial Horizon Scanning Tool
This is a tool for assessing the change in the global risk of disease X emergence over time.
What is Disease X?
Disease X
is defined as a novel pathogen which causes a pandemic.
As the name suggests, the exact nature of disease X is not known.
Pandemics are most commonly caused by emerging infectious diseases (EDI), or diseases which are changing their incidence, impact or range. Disease X represents such a disease.
Pandemics are most commonly caused by emerging infectious diseases (EDI), or diseases which are changing their incidence, impact or range. Disease X represents such a disease.
The Drivers of Disease X Emergence
Previous work has highlighted ten key drivers of disease X emergence,
which are outlined in the "Driver information"tab on the sidebar.
Global datasets for each driver are included in the model. The location from which these datasets were taken can also be found in the sidebar.
Global datasets for each driver are included in the model. The location from which these datasets were taken can also be found in the sidebar.
How to use the model
The model (found in the "Model" tab) can combine any, or all, of the 10 drivers of
disease X emergence using a weighted average. The weights can be set using the sliders,
or preset weights from any of the four disease scanarios can be used.
Four Disease Scenarios
An expert knowledge elicitation workshop was carried out to determine weightings for four
disease scenarios, which can be inputted into the model. Information on these diseases
can be found in the "Disease Scenarios" tab.
This work is supported
by the European Unions Horizon 2020 research and innovation programme under Grant No.874735 (VEO).
Global risk model
To run the model, select weightings for each driver. These can be set to 0 if the
driver is not relevant to your scenario. You can use the preset driver weightings
for any of the 4 disease scenarios that are outlined in the "Disease Scenarios" tab.
Then select two timepoints to compare. Otherwise, the 'none' setting can be toggled to equally weight the drivers.
Model Inputs
Model Output
Drivers of Disease X Emergence
On this page, you will find information about the source
of the data for each of the 10 drivers of disease X
emergence used in the model. Data is also given a score out of 18 for quality. Drivers sourced according to
Horigan et. al
Driver 1: Wildlife Density
Data derived from:
the ICUN red list
Data contains:
The number of animals of any species that may be present in each grid cell of the global map.
Data quality score: 9
Data contains:
The number of animals of any species that may be present in each grid cell of the global map.
Data quality score: 9
Driver 2: Wildlife Diversity
Data derived from:
the ICUN red list
Data contains:
The number of different species that may be present in each grid cell of the global map.
Data quality score: 9
Data contains:
The number of different species that may be present in each grid cell of the global map.
Data quality score: 9
Driver 3: Climate
Data derived from:
CEDA
Data contains:
average rainfall and average annual temperature, which is averaged over every two years from 1990-2022.
Data quality score: 13
Data contains:
average rainfall and average annual temperature, which is averaged over every two years from 1990-2022.
Data quality score: 13
Driver 4: Livestock Population
Data derived from:
FAO species data
Data contains:
the number of livestock animals in a given area. The data is separated into chickens, sheep, goats, horses, buffalo, ducks and pigs.
Data contains:
the number of livestock animals in a given area. The data is separated into chickens, sheep, goats, horses, buffalo, ducks and pigs.
Data quality score: 13
Driver 5: Wild Caught Meat Consumption
Data derived from:
University of Gothenburg
Data contains:
the output of a predictive model of the global distribution of bushmeat-related activities.
Data quality score: 11
Data contains:
the output of a predictive model of the global distribution of bushmeat-related activities.
Data quality score: 11
Driver 6: Land Use Change
Data derived from:
Copernicus Land Monitoring Service
Data contains:
Data on global land usage types in 7 categories: Forest, Other Vegetation, Wetland, Bare, Cropland, Urban and Water. Land Use Change records any movement between categories relative to the ‘baseline’ in 2015.
Data quality score: 9
Data contains:
Data on global land usage types in 7 categories: Forest, Other Vegetation, Wetland, Bare, Cropland, Urban and Water. Land Use Change records any movement between categories relative to the ‘baseline’ in 2015.
Data quality score: 9
Driver 7: Deforestation
Data derived from:
Copernicus Land Monitoring Service
Data contains:
Data from 2016-2020 on reduction in tree cover relative to the ‘baseline’ of 2015.
Data quality score: 17
Data contains:
Data from 2016-2020 on reduction in tree cover relative to the ‘baseline’ of 2015.
Data quality score: 17
Driver 8: Human Density
Data derived from:
GHSL
Data contains:
The number of humans present within 10km square grids.
Data quality score: 15
Data contains:
The number of humans present within 10km square grids.
Data quality score: 15
Driver 9: Food Production
Data derived from:
Our World in Data
Data contains:
per capita kilocalorie supply from all foods per day. The map shows the quantity of food that is available at the end of the supply chain.
Data quality score: 16
Data contains:
per capita kilocalorie supply from all foods per day. The map shows the quantity of food that is available at the end of the supply chain.
Data quality score: 16
Driver 10: Conflict and Economic Disaster
Data derived from:
Fragile States Index
Data contains:
an index for assessing the vulnerability of states to collapse. There are twelve conflict risk indicators are used to measure the condition of a state at any given moment.
Data quality score: 17
Data contains:
an index for assessing the vulnerability of states to collapse. There are twelve conflict risk indicators are used to measure the condition of a state at any given moment.
Data quality score: 17
Preset Disease Scenarios
In september 2024, an expert knowledge elicitation workshop was held to determine appropriate weightings for each of the drivers for the four disease scenarios below.
Disease 1: Very Foul Disease (VFD)
- VFD is a viral infection of birds with mild zoonotic properties.
- It has been found in wild bird populations of various species, as well as domestic birds.
- It has also been found in rare cases in domestic pigs and cows.
- Transmission occurs through contact, consumption of infected carcasses and aerial droplets.
- VFD is highly infectious with a relatively low mortality rate.
Disease 2: Bluerot
- Bluerot is a bacterial infection that causes severe illness in animals.
- It is commonly found in pigs, cows and buffalo.
- Bluerot has been found in wild boars and ungulates.
- It is transmitted through direct contact with infected animals or body fluids and consumption of infected animals.
Disease 3: Innsmouth Fever
- Innsmouth Fever is transmitted by mosquitos.
- It is zoonotic, humans infected experience gastrointestinal issues.
- It is suspected to have a reservoir in wild birds as well as small mammals, though neither show symptoms when infected.
- Proliferation of the mosquito host species is dependent on the amount of rainfall, as this impacts the number of breeding sites.
Disease 4: X-pox
- X-pox is a viral respiratory disease.
- It is zoonotic, humans infected experience flu-like symptoms.
- It is suspected to have a reservoir in small wild mammals in remote rural areas.
- It is transmitted through airborne droplets and is very infectious between humans.