Disease and climate data fusion for modelling

An application case for Brazil

Raphael Saldanha

Inria, GHR collaborator

2023-11-22

Introduction

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

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 today

Climate data

• Data sources
  • Weather stations, rain gauges
  • Satellites
• Data products
  • Statistical surface interpolations
  • Model reanalysis

ERA5-Land reanalysis

• Copernicus, ECMWF
• Global coverage
• Hourly data
• 1950 to the present (one week lag)
• 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, 1950-2022

• ERA5-Land hourly data to daily aggregates
• Average, maximum and minimum temperature, total precipitation
• Surface pressure, dewpoint, \(u\) and \(v\) components of wind
• Zonal statistics computation for the 5,570 Brazilian municipalities
  • Minimum, maximum, average, sum, standard deviation, cell count

Workflow

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flowchart TD

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

hdata --> agg(Daily aggregated \n data)

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

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

Average temperature

Next steps…

• Continuous update
• Human settlements, population-weighted zonal statistics
• Compute climate time-series features: heat waves, persistent rains, etc.
• Expand methodology to other countries

Thanks!

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