IBF-dengue-mosquito-model

Forecast dengue outbreaks. Part of 510's impact-based forecasting portal. Built to support Philippines Red Cross.

This model:

1. calculates expected mosquito abundance given meteorological data
2. forecast dengue risk given mosquito abundance
3. determines if dengue risk is anomalously high and, if so, gives an alert
4. calculates the expected number of dengue cases given dengue risk

See details in the IBF-dengue technical note.

If you want to run this model on Azure, follow these instructions.

Setup

Generic requirements:

• Google Cloud account
• Google Earth Engine service account

For 510: project service account accessible here, login credentials in Bitwarden

with Docker

1. Install Docker
2. Download input data from here and move it to

mosquito_model/input/

5. Copy your Google Earth Engine service account credentials (stored as .json) and IBF-system credentials (stored as .env) in

mosquito_model/credentials/

3. Build the docker image from the root directory

docker build -t rodekruis/mosquito-model .

3. Create a docker container

docker run --name mosquito-model rodekruis/mosquito-model

4. Run and access the container

docker run -it --entrypoint /bin/bash rodekruis/mosquito-model

5. Check that everything is working by running the model (see Usage below)

Manual Setup

Specific requirements:

• Python >= 3.6.5

1. Move to project root

cd mosquito_model

2. Install project

pip install .

3. Download input data from here and move it to

mosquito_model/input/

5. Copy your Google Earth Engine service account credentials (stored as .json) and IBF-system credentials (stored as .env) in

mosquito_model/credentials/

Usage

Usage: run-mosquito-model [OPTIONS]

Options:
  --countrycode TEXT             country iso code
  --vector TEXT                  vector file with admin boundaries
  --admincode TEXT               name of admin code in vector file
  --temperaturesuitability TEXT  table with suitability vs temperature
  --thresholds TEXT              table with thresholds and coefficients (risk vs dengue cases)
  --demographics TEXT            table with demographic data
  --credentials TEXT             credentials directory
  --data TEXT                    input data directory
  --dest TEXT                    output data directory
  --predictstart TEXT            start predictions from date (%Y-%m-%d)
  --predictend TEXT              end predictions on date (%Y-%m-%d)
  --storeraster                  store raster data locally
  --verbose                      print output at each step
  --help                         show this message and exit