An open-source framework for Rift Valley Fever forecasting

OpenRVFcast

EcoHealth Alliance’s ongoing OpenRVFcast project is developing a generalizable, open-source modeling framework for predicting Rift Valley Fever (RVF) outbreaks in Africa, funded by the Wellcome Trust’s climate-sensitive infectious disease modeling initiative. We aim to integrate open data sets of climatic and vegetation data with internationally-reported outbreak data to build an modeling pipeline that can be adapted to varying local conditions in RVF-prone regions across the continent.

Repository Structure and Reproducibility

data/ contains downloaded and transformed data sources. These data are .gitignored and non-EHA users will need to download the data.
R/ contains functions used in this analysis.
reports/ contains literate code for R Markdown reports generated in the analysis
outputs/ contains compiled reports and figures.
This project uses the {renv} framework to record R package dependencies and versions. Packages and versions used are recorded in renv.lock and code used to manage dependencies is in renv/ and other files in the root project directory. On starting an R session in the working directory, run renv::restore() to install R package dependencies.
This project uses the {targets} framework to organize build steps for analysis pipeline. The schematic figure below summarizes the steps. (The figure is generated using mermaid.js syntax and should display as a graph on GitHub. It can also be viewed by pasting the code into https://mermaid.live.)

Warning messages: 1: package ‘rmarkdown’ was built under R version 4.3.3 2: package ‘paws’ was built under R version 4.3.3 3: package ‘terra’ was built under R version 4.3.3

graph LR
subgraph Project Workflow
  subgraph Graph
    direction LR
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    xcc02e30ec90a7edd(["wahis_outbreak_history_directory"]):::skipped --> x27dbf0f2484063f3["wahis_outbreak_history"]:::queued
    x659aa62eded9787b(["wahis_outbreaks"]):::queued --> x27dbf0f2484063f3["wahis_outbreak_history"]:::queued
    x39ef63e4c3553f78(["wahis_raster_template"]):::queued --> x27dbf0f2484063f3["wahis_outbreak_history"]:::queued
    xe3c4533ec81ef618(["continent_polygon"]):::skipped --> xb49d77ffc5b097ae(["continent_bounding_box"]):::queued
    x50d291d42ebde68c(["combined_anomalies_directory"]):::skipped --> x439c9f5bc1e96cd5(["combined_anomalies_AWS"]):::queued
    xc61c34839fb8c873(["model_dates_selected"]):::queued --> x439c9f5bc1e96cd5(["combined_anomalies_AWS"]):::queued
    xf9b79e824823a870["ndvi_anomalies"]:::queued --> x439c9f5bc1e96cd5(["combined_anomalies_AWS"]):::queued
    x01b9e03cb52b7b05["weather_anomalies"]:::queued --> x439c9f5bc1e96cd5(["combined_anomalies_AWS"]):::queued
    xf9b79e824823a870["ndvi_anomalies"]:::queued --> x144f59a0db036a4b(["ndvi_anomalies_AWS_upload"]):::queued
    xe2329877730e44b5(["ndvi_anomalies_directory"]):::skipped --> x144f59a0db036a4b(["ndvi_anomalies_AWS_upload"]):::queued
    x680f7450837c9229["forecasts_anomalies"]:::queued --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    x309ea01959a83a5a(["forecasts_validate_directory"]):::skipped --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    x021b0407fd88c849(["lead_intervals"]):::skipped --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    xc61c34839fb8c873(["model_dates_selected"]):::queued --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    x0548e231345702f7["nasa_weather_transformed"]:::queued --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    xbd6b5d8fe3154d5a["weather_historical_means"]:::queued --> xcc18535b953bde28(["forecasts_anomalies_validate_AWS"]):::queued
    x27dbf0f2484063f3["wahis_outbreak_history"]:::queued --> x53890e32519c2cdc(["wahis_outbreak_history_AWS_upload"]):::queued
    x338ce62055c4090f(["wahis_outbreak_history_animations_directory"]):::skipped --> x53890e32519c2cdc(["wahis_outbreak_history_AWS_upload"]):::queued
    xba6244832b5285ba(["continent_raster_template"]):::queued --> x0548e231345702f7["nasa_weather_transformed"]:::queued
    x84fbc80b775022e1(["nasa_weather_AWS"]):::queued --> x0548e231345702f7["nasa_weather_transformed"]:::queued
    xc54ffbea58c4afd9(["nasa_weather_coordinates"]):::queued --> x0548e231345702f7["nasa_weather_transformed"]:::queued
    x711dc87df29f0a9c(["nasa_weather_transformed_directory"]):::skipped --> x0548e231345702f7["nasa_weather_transformed"]:::queued
    x0c2748f0f39a3907(["nasa_weather_years"]):::skipped --> x0548e231345702f7["nasa_weather_transformed"]:::queued
    x3b5d33025a7856bb["ecmwf_forecasts_transformed"]:::queued --> xe017ffc3bafa162a(["ecmwf_forecasts_transformed_AWS_upload"]):::queued
    x16ce463b7b647c1e(["ecmwf_forecasts_transformed_directory"]):::skipped --> xe017ffc3bafa162a(["ecmwf_forecasts_transformed_AWS_upload"]):::queued
    x2b83f10567783884(["wahis_rvf_outbreaks_preprocessed"]):::queued --> x659aa62eded9787b(["wahis_outbreaks"]):::queued
    xb49d77ffc5b097ae(["continent_bounding_box"]):::queued --> xa5bc51cd67d5e6c0(["modis_ndvi_task_id_continent"]):::queued
    x3f3ba2f9e89a9591(["modis_ndvi_token"]):::completed --> xa5bc51cd67d5e6c0(["modis_ndvi_task_id_continent"]):::queued
    x16e1cc582647deec["wahis_outbreak_history_animations"]:::queued --> x3a8830b5def8250b(["wahis_outbreak_history_animations_AWS_upload"]):::queued
    x338ce62055c4090f(["wahis_outbreak_history_animations_directory"]):::skipped --> x3a8830b5def8250b(["wahis_outbreak_history_animations_AWS_upload"]):::queued
    x5448b80c3909d641(["glw_directory"]):::skipped --> x01e625a4f1dd2c42(["glw_preprocessed_AWS_upload"]):::queued
    x82990a83bfa4db45(["glw_preprocessed"]):::queued --> x01e625a4f1dd2c42(["glw_preprocessed_AWS_upload"]):::queued
    xc61c34839fb8c873(["model_dates_selected"]):::queued --> xcfd8f1e8f3ea9117["aggregated_data_rsa"]:::queued
    x97fc33c6215703a3(["rsa_polygon"]):::skipped --> xcfd8f1e8f3ea9117["aggregated_data_rsa"]:::queued
    x1f222a4448edddc4(["days_of_year"]):::skipped --> x42d785b9e0106385(["ndvi_historical_means_AWS"]):::queued
    xb06c08f4a8f21445(["lag_intervals"]):::skipped --> x42d785b9e0106385(["ndvi_historical_means_AWS"]):::queued
    x92b237aaa434cba4(["ndvi_date_lookup"]):::queued --> x42d785b9e0106385(["ndvi_historical_means_AWS"]):::queued
    x7fef416d6ce259f3(["ndvi_historical_means_directory"]):::skipped --> x42d785b9e0106385(["ndvi_historical_means_AWS"]):::queued
    x5130788afbe32544["modis_ndvi_transformed"]:::queued --> x92b237aaa434cba4(["ndvi_date_lookup"]):::queued
    xa4eb23442420052a["sentinel_ndvi_transformed"]:::queued --> x92b237aaa434cba4(["ndvi_date_lookup"]):::queued
    x659aa62eded9787b(["wahis_outbreaks"]):::queued --> xa33032ce29b67c7f(["wahis_distance_matrix"]):::queued
    x39ef63e4c3553f78(["wahis_raster_template"]):::queued --> xa33032ce29b67c7f(["wahis_distance_matrix"]):::queued
    x27dbf0f2484063f3["wahis_outbreak_history"]:::queued --> x16e1cc582647deec["wahis_outbreak_history_animations"]:::queued
    x338ce62055c4090f(["wahis_outbreak_history_animations_directory"]):::skipped --> x16e1cc582647deec["wahis_outbreak_history_animations"]:::queued
    x27dbf0f2484063f3["wahis_outbreak_history"]:::queued --> x2b50e7687b4412ab(["wahis_outbreak_history_animations_AWS"]):::queued
    x338ce62055c4090f(["wahis_outbreak_history_animations_directory"]):::skipped --> x2b50e7687b4412ab(["wahis_outbreak_history_animations_AWS"]):::queued
    x8894af119fe2eaa1(["landcover_directory"]):::skipped --> xbc982b2f29054bd9(["landcover_preprocessed_AWS_upload"]):::queued
    xdecc37cc7e708cec(["landcover_preprocessed"]):::queued --> xbc982b2f29054bd9(["landcover_preprocessed_AWS_upload"]):::queued
    x42a5375a64b48216(["aspect_directory"]):::skipped --> xfe5a910dc093a019(["aspect_preprocessed_AWS_upload"]):::queued
    x155e2f0b29a20e05(["aspect_preprocessed"]):::queued --> xfe5a910dc093a019(["aspect_preprocessed_AWS_upload"]):::queued
    x439c9f5bc1e96cd5(["combined_anomalies_AWS"]):::queued --> xfde5ff2681c50d89(["combined_anomalies"]):::queued
    x50d291d42ebde68c(["combined_anomalies_directory"]):::skipped --> xfde5ff2681c50d89(["combined_anomalies"]):::queued
    x680f7450837c9229["forecasts_anomalies"]:::queued --> xfde5ff2681c50d89(["combined_anomalies"]):::queued
    xf9b79e824823a870["ndvi_anomalies"]:::queued --> xfde5ff2681c50d89(["combined_anomalies"]):::queued
    x01b9e03cb52b7b05["weather_anomalies"]:::queued --> xfde5ff2681c50d89(["combined_anomalies"]):::queued
    x42a5375a64b48216(["aspect_directory"]):::skipped --> x049b29595ee19108(["aspect_AWS"]):::queued
    xba6244832b5285ba(["continent_raster_template"]):::queued --> x049b29595ee19108(["aspect_AWS"]):::queued
    x9c9060069417a49a(["wahis_rvf_outbreaks_raw"]):::skipped --> x2b83f10567783884(["wahis_rvf_outbreaks_preprocessed"]):::queued
    xfde5ff2681c50d89(["combined_anomalies"]):::queued --> xba5d6169ff0233fa(["combined_anomalies_AWS_upload"]):::queued
    x50d291d42ebde68c(["combined_anomalies_directory"]):::skipped --> xba5d6169ff0233fa(["combined_anomalies_AWS_upload"]):::queued
    xba6244832b5285ba(["continent_raster_template"]):::queued --> x82990a83bfa4db45(["glw_preprocessed"]):::queued
    x71c93c84792ad529(["glw_AWS"]):::queued --> x82990a83bfa4db45(["glw_preprocessed"]):::queued
    x5448b80c3909d641(["glw_directory"]):::skipped --> x82990a83bfa4db45(["glw_preprocessed"]):::queued
    x4d4a15b2f0f1851f(["glw_urls"]):::skipped --> x82990a83bfa4db45(["glw_preprocessed"]):::queued
    xba6244832b5285ba(["continent_raster_template"]):::queued --> x98b1351d966647f6(["elevation_AWS"]):::queued
    x0381132b9136146c(["elevation_directory"]):::skipped --> x98b1351d966647f6(["elevation_AWS"]):::queued
    xb06c08f4a8f21445(["lag_intervals"]):::skipped --> xc61c34839fb8c873(["model_dates_selected"]):::queued
    x01b9e03cb52b7b05["weather_anomalies"]:::queued --> xc567f473073bf453(["weather_anomalies_AWS_upload"]):::queued
    xf94f7486eed9869c(["weather_anomalies_directory"]):::skipped --> xc567f473073bf453(["weather_anomalies_AWS_upload"]):::queued
    xbd6b5d8fe3154d5a["weather_historical_means"]:::queued --> xf8b72b30842f6a3c(["weather_historical_means_AWS_upload"]):::queued
    x4407a62768444c3e(["weather_historical_means_directory"]):::skipped --> xf8b72b30842f6a3c(["weather_historical_means_AWS_upload"]):::queued
    xba6244832b5285ba(["continent_raster_template"]):::queued --> x84fbc80b775022e1(["nasa_weather_AWS"]):::queued
    xc54ffbea58c4afd9(["nasa_weather_coordinates"]):::queued --> x84fbc80b775022e1(["nasa_weather_AWS"]):::queued
    x711dc87df29f0a9c(["nasa_weather_transformed_directory"]):::skipped --> x84fbc80b775022e1(["nasa_weather_AWS"]):::queued
    x0c2748f0f39a3907(["nasa_weather_years"]):::skipped --> x84fbc80b775022e1(["nasa_weather_AWS"]):::queued
    xb06c08f4a8f21445(["lag_intervals"]):::skipped --> x0df1395319c2f010(["weather_anomalies_AWS"]):::queued
    xc61c34839fb8c873(["model_dates_selected"]):::queued --> x0df1395319c2f010(["weather_anomalies_AWS"]):::queued
    x0548e231345702f7["nasa_weather_transformed"]:::queued --> x0df1395319c2f010(["weather_anomalies_AWS"]):::queued
    xf94f7486eed9869c(["weather_anomalies_directory"]):::skipped --> x0df1395319c2f010(["weather_anomalies_AWS"]):::queued
    xbd6b5d8fe3154d5a["weather_historical_means"]:::queued --> x0df1395319c2f010(["weather_anomalies_AWS"]):::queued
    x30a742f54b518a5f(["augmented_data_rsa_directory"]):::skipped --> x30a742f54b518a5f(["augmented_data_rsa_directory"]):::skipped
    xdc94d22b863438a5(["nasa_weather_variables"]):::skipped --> xdc94d22b863438a5(["nasa_weather_variables"]):::skipped
  end
linkStyle 0 stroke-width:0px;

Many of the computational steps can be time consuming and either depend on or produce large files. In order to speed up the pipeline, intermediate files can be stored on the cloud for rapid retrieval and portability between pipeline instances. We currently use an AWS S3 bucket for this purpose. The pipeline will still run without access to cloud storage but the user can benefit from adapt the _targets.R file to use their own object storage repository. AWS access keys and bucket ID are stored in the .env file.

For handling gridded binary weather data, this pipeline uses the ecCodes package which can be installed on OSX using homebrew:

brew install eccodes

and on Ubuntu using apt-get

sudo apt update
sudo apt install eccodes

EHA users: see the stripts/ repository to be able to directly download data from AWS outside of the targets workflow.

Follow the links for more information about:

ecohealthalliance / open-rvfcast

readme

An open-source framework for Rift Valley Fever forecasting

OpenRVFcast

Repository Structure and Reproducibility