Disease and climate data fusion for modelling

An application case for Brazil

Raphael Saldanha

Inria

2023-12-18

Introduction

• Postdoctoral researcher at Inria, a French research institute for digital science and technology
• Fiocruz, Climate and Health Observatory collaborator
• BSC, Global Health Resilience collaborator

Climate sensitive diseases

• Direct relationship: floods, droughts, heat waves…

• Indirect relationship

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flowchart LR

climate(Climate) --> vector(Disease vectors) --> health(Human health)
climate --> health
climate --> social(Social & economic \n determinants) --> health

• Climate necessary conditions to vector viability, reproduction and disease transmission efficiency
• Climate indicators may act as proxy variables to vector distribution on statistical models

A time-lagged relationship

• Vector life cycle in a time perspective
• Climate conditions from the past leads to the disease incidence of tomorrow

Climate data

• Data sources
  • In situ: Weather stations, rain gauges
  • Remote: Satellites, drones
• Data products
  • Statistical surface interpolations
  • Model reanalysis

Climate reanalysis

ERA5-Land reanalysis

• Copernicus, ECMWF
• Global coverage
• Hourly data
• 1950 to the present (one week delay)
• Spatial resolution ~9km
• Several climate indicators

Data structures

• Climate indicators: grid data
• Disease incidence: tabular, individual cases aggregated by spatial regions and time spans

Fusioning data

Case example

Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities

• 8 climate indicators: maximum, minimum and average temperature, total precipitation, surface pressure, dewpoint, \(u\) and \(v\) components of wind
• 6 zonal statistics computation for the 5,570 Brazilian municipalities
  • Minimum, maximum, average, sum, standard deviation, cell count
• Time coverage: 1950-2022, daily data

Workflow

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flowchart TD

era5(ERA5-Land \n indicators) --> hdata(Hourly data)
bb(Latin America \n bounding box) --> hdata

hdata --> agg(Aggregation to \n daily data)

agg --> mun(Municipal boundaries)
mun --> zs(Zonal statistics)

ERA5-Land hourly to daily aggregation

• Usage of {KrigR} package to access the Copernicus Climate Data Store API, crop data at server side, download and perform the time aggregation.

download_ERA(Variable = "2m_temperature", DataSet = "era5-land", 
             DateStart = "2022-12-01", DateStop = "2022-12-31",
             TResolution = "day", TStep = 1,
             FUN = "max",
             Extent = extent(c(-118.47,-34.1,-56.65, 33.28)), 
             Dir = "dir_name", FileName = "file_name.nc", 
             API_User = "api_user", API_Key = "api_key")

• Took ~15 days to download and process the data from 8 climate indicators covering the Latin America region

ERA5-Land Daily datasets

• Open data, available at Zenodo
• 7,105 files, 658.7 GB
• Reproducible R scripts
• Plans to continuously update this dataset and add more indicators

https://rfsaldanha.github.io/data-projects/era5land-daily-latin-america.html

Zonal statistics

• Challenge to handle the amount of data
• ~ 4 billion computational tasks
• Strategy
  • Group the tasks into chunks and compute in parallel
  • Save results into columnar-oriented databases for fast data retrieval (duckdb and parquet)
  • DAG (Directed-Acyclic Graph) approach to orchestrate computation, with the {targets} package

Computation

• Zonal statistics weighted by the fraction of the cell that is covered, with the {exactextractr} package

exact_extract(
  x = rst,
  y = pol,
  fun = "mean"
)

Results

ERA5-Land indicators	Daily time-aggregating functions	Spatial zonal statistics
Temperature (2m)	mean, max, min	max, min, stdev, count
Dewpoint temp. (2m)	mean	max, min, stdev, count
\(u\) component of wind	mean	max, min, stdev, count
\(v\) component of wind	mean	max, min, stdev, count
Surface pressure	mean	max, min, stdev, count
Total precipitation	sum	max, min, stdev, count, sum

6,085,749,761 records

Temperature

Precipitation

Rio de Janeiro municipalities. January 1, 2010.

Resolution and spatial variability

• Brazilian municipalities size variation
  • Altamira (PA): 159,533 km2
  • Santa Cruz de Minas (MG): 3 km2
• ERA5-Land cell: ~ 100 km2
• Several intersecting cells and different climate regimes
• Few intersecting cells or fractions

Next steps…

• Continuous update
• Human settlements, population-weighted zonal statistics
• Compute climate time-series features: heat waves, persistent rains, etc.
• Adopt climate products with finer resolutions when possible (CHIRPS)
• Expand methodology to other countries
• Methodological paper on reviewing phase

Thanks!

Contact, data links, R packages and short tutorials available at rfsaldanha.github.io