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Mortal Analytics - Covid 19 &
the problem of data quality
Lars Albertsson (@lalleal)
Scling
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Why this presentation?
● Non-goal: Argue for or against a particular strategy
○ We are already too polarised
● Goals:
○ What can go wrong with data quality?
○ What can we learn?
○ Data engineering as a solution
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Imperial College: We saved the world!
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https://www.bbc.com/news/health-52968523

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Imperial College model predictions for Sweden
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https://www.medrxiv.org/content/10.1101/2020.04.11.20062133v1.full.pdf

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Model and reality
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https://swprs.org/a-swiss-doctor-on-covid-19/

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Imperial College model code
●
● Screenshots are only part of functions...
● A couple of regression tests - no tests validating correct functionality
● My impression: No chance of producing high confidence result
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https://github.com/mrc-ide/covid-sim

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Imperial College: bugs are not a problem
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https://lockdownsceptics.org/code-review-of-fergusons-model/

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Example Imperial College bug handling
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https://github.com/mrc-ide/covid-sim/issues/330
Imperial College response

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Bad predictions are harmful
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● Each action has a health cost
○ Economic misery
→ social misery
→ health misery
○ Mental health
○ Drug / alcohol use
○ Domestic violence
● During Ebola pandemic,
10x more people died from fear
of hospitals than from Ebola
https://medium.com/@robert.munro/the-tech-communitys-response-to-ebola-44d2c8dbb5be

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Ways to degrade data & analytics quality
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● Deviating definitions
● Selection
● Deviating context
● Presentation
● Interpretation
● Data collection
● Data processing
● Lack of quality assessment
● Lack of quality improvement
Add senior software
engineers with
production experience.
Data engineering

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Define death
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Observed Covid-19 death definitions:
● Infection confirmed, last 30 days
● Infection confirmed, any time
● Infection assumed
● Assumed cause
● Hospitalised
● Other disease complicated by Covid-19
● Excess mortality

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Sweden on the rise?
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https://youtu.be/4uTj96ZowCU
https://www.bbc.com/news/world-europe-53175459
https://sverigesradio.se/artikel/7503606
"New Covid-19 cases per day"

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No, context is missing
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Tests executed
Test positive rate
New cases
https://youtu.be/4uTj96ZowCU https://twitter.com/JacobGudiol/status/1283308826842759168 https://twitter.com/JacobGudiol/status/1283308817787293696

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Death numbers, different views
14https://twitter.com/HaraldofW/status/1270080232104624128
https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-25-final.pdf

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Data will confess to anything
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● Absolute numbers mislead
○ Days since case x →
time shift by country size
● Relative numbers mislead
○ Diluted in large countries
○ Small regions stand out
https://swprs.org/a-swiss-doctor-on-covid-19/

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Granularity matters
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● Outbreaks in regions
● Country aggregation - information loss
○ But debate assumes homogeneous countries
● Peak of Swedish outbreak
○ Major outbreak in Stockholm + surroundings
○ Rest of Sweden on par with Nordics
● Nothing is "obvious"
https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-25-final.pdf
Swedish policy "obviously"
terrible. Compare numbers
with neighbours!

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Data collection
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"The last week is not complete, so it is
difficult to determine if the trend continues."
https://youtu.be/4uTj96ZowCU
https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-27-final.pdf

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What conclusion from this graph?
COVID-19 fatalities / day in Sweden
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https://www.folkhalsomyndigheten.se/

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Comparing apples, oranges, bananas, ...
COVID-19 fatalities / day in Sweden
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Fatalities collected during 2 day
Fatalities collected during 4 days
Fatalities collected during 10 days

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Naive data collection
● Gather the events that we have
● Put them in a database
● "Let us look at the latest data"
● You never want the latest data!
You want comparable data.
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Wrong conclusion, every day
● Fatalities data as of
April 6
April 15
April 19
21Graph by Statistisk Opinion, @StatistiskO

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Wrong conclusion, every day
● Downward trend every day!
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https://www.bloomberg.com/amp/news/articles/2020-07-17/georgia-massaged-virus-data-to-reopen-then-voided-mask-orders

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Normalise data collection to compare
23Graph by Adam Altmejd, @adamaltmejd

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Normalise data collection to compare
24Graph by Adam Altmejd, @adamaltmejd

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Forecast for analytics with fresh data
25Graph by Adam Altmejd, @adamaltmejd

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Why aren't authorities doing that?
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● Cost of processing data
● Manual handcraft
not
Industrial process
https://github.com/FohmAnalys/SEIR-model-Stockholm
We are not done
processing the data yet.
Since we do calculations
quickly, some mistakes
might happen.

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● Scaled processes
● Machine tools
● Challenges: scale,
logistics, legal,
organisation, faults, ...
Manual, mechanised, industrialised
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● Muscle-powered
● Few tools
● Human touch for every
step
● Direct human control
● Machine tools
● Low investment, direct
return

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Muscle powered analytics & machine learning
● Use hand tools to
○ Collect data
○ Aggregate for analytics
or
○ Train a model
● Typical tools:
○ Excel
○ Matlab
○ Interactive SQL
○ Interactive BI tools
○ Jupyter
○ R
○ One-off Python scripts
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"Dataset" - a data artifact of direct or indirect value

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Mechanised analytics & machine learning
● Use machine tools to semi-automatically
○ Collect data
○ Aggregate for analytics
or
○ Train a model
● Typical tools: Muscle tools +
○ Databases
○ Data warehouses + ETL
○ Hadoop, Spark, Flink
○ Java, Scala, Python, SQL
○ Kafka
○ Similar cloud services
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Datasets, produced monthly / hourly / daily / ..

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From craft to process
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From craft to process
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Multiple time windows

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From craft to process
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Multiple time windows
Assess ingress data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Assess outcome data quality
Repair broken data
Intermediate datasets, reusable between pipelines

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history,
multiple parameter settings
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history,
multiple parameter settings
Assess outcome data quality
Assess forecast success,
adapt parameters

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Towards sustainable production ML
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Multiple models,
parameters, features
Assess ingress data quality
Repair broken data from
complementary source
Choose model and parameters based
on performance and input data
Benchmark models
Try multiple models,
measure, A/B test

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Industrialised analytics / machine learning
● Build resilient, automated processes that
○ Collect & process
○ Assess & improve quality
○ Create multiple artifacts, measure, adapt
● Typical tools: Mechanised tools +
○ Data lake
○ Workflow orchestration (Luigi, Airflow)
○ Quality assessment, monitoring
○ Testing, CI/CD
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● Resilient data factory
● Every dev team,
100-1000s datasets /
day per team
Costs down - ROI from data
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● Hand-built
● Analyst team,
< 10 dataset / day
● Semi-automated
● "The data team",
10-100 datasets / day
Spotify ~2014,
20K datasets/day

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Becoming data industrialised
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● Knowledge limited to leading tech companies + startups
● Change in processes & culture
○ C.f. agile, DevOps
○ Journey of many years
● Challenge is not technical
○ Can't buy a system or tool
○ Consultants can't help

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Scling - data-value-as-a-service
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Data value through collaboration
Customer
Data factory
Data platform & lake
data
domain
expertise
Value from data!
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