Wednesday, March 3, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Data Science

Would Bayesian approaches improve COVID-19 forecasts?

April 21, 2020
in Data Science
Would Bayesian approaches improve COVID-19 forecasts?
585
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Data Scientists worldwide are forecasting about COVID

You might also like

The Effect IoT Has Had on Software Testing

Why Cloud Data Discovery Matters for Your Business

DSC Weekly Digest 01 March 2021

Mostly, they are doing excellent work.

But still, most forecasts vary widely

That’s not to say that they are inaccurate

There are a few inherent reasons for this

Firstly, the lack of domain knowledge in epidemiology and biostatistics. Most data scientists lack knowledge in epidemiology and biostatistics essential for COVID-19 forecast. 

But assuming that you do acquire the knowledge, most models are only as good as the data – and there is very little data.

Hence, most models apply data from other contexts / geographies.

For example, UK models are based on data from Italy

There are four basic elements used to model most disease outbreaks: 

  • The number of people each infected individual will subsequently infect in the early stages of the outbreak, the R0,
  • The duration of the outbreak
  • The case fatality ratio, the probability that an infected person dies and
  • The asymptomatic ratio

All of these rely on data which is not perfect and not necessarily applicable across geographies

Moreover, it depends on what features the model will emphasise

In the UK, the two best known models (Imperial college and Oxford) emphasise different elements and come to different conclusions as to the spread(but both agree on social distancing) ref Bloomberg – It helps to understand what goes into Imperial and Oxford’s predictive coronavirus models, and what they can and can’t do.

Hence, a model cannot really say where COVID-19 will explode next – even though it could tell us likely scenarios.

For the ‘where next’ element – we need contact tracing to capture the data 

So, this all points to the need for data for models whichever way you look at it

 

With one exception

Would a Bayesian approach yield better results under the circumstances?

Isaac Faber – whose insights I recommend – says

Modelling for the pandemic has shown that this debate should still be front and center. The frequentists are mostly in the spotlight advising world leaders. If you listen close you will hear a common refrain ‘we just need more data.’ This is, of course, the age-old problem of statistical significance. However, today, we aren’t in a harmless lab study, these data are only realized through death. This, more than any other argument, shows how dangerous frequentist methods are in practice. The methods don’t give you enough confidence when you need it the most. You need to wait for a sufficient pile of bodies. This probably contributed to why many gov’ts were slow to act; their advisors didn’t have enough data to be confident. The Bayesian has no problem acting quickly with expert judgment (a prior belief) and small data. Want to join the Bayesians? Start here:

Source Isaac Faber – linkedin

This sounds like taking the familiar frequentist vs Bayesian debate post COVID

I don’t know how accurate this line of thinking is .. but its certainly worth exploring

Its also an excellent way to illustrate these two approaches from a problem-solving / teaching perspective

 


Credit: Data Science Central By: ajit jaokar

Previous Post

Google's Cloud Healthcare API is ready for prime time

Next Post

Slack expands data residency program to Australia

Related Posts

The Effect IoT Has Had on Software Testing
Data Science

The Effect IoT Has Had on Software Testing

March 3, 2021
Why Cloud Data Discovery Matters for Your Business
Data Science

Why Cloud Data Discovery Matters for Your Business

March 2, 2021
DSC Weekly Digest 01 March 2021
Data Science

DSC Weekly Digest 01 March 2021

March 2, 2021
Companies in the Global Data Science Platforms Resorting to Product Innovation to Stay Ahead in the Game
Data Science

Companies in the Global Data Science Platforms Resorting to Product Innovation to Stay Ahead in the Game

March 2, 2021
Importance of Data Science in Modern Age
Data Science

Importance of Data Science in Modern Age

March 2, 2021
Next Post
Slack expands data residency program to Australia

Slack expands data residency program to Australia

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

An open-source machine learning framework to carry out systematic reviews
Machine Learning

An open-source machine learning framework to carry out systematic reviews

March 3, 2021
The Ways in Which Big Data can Transform Talent Management and Human Resources | by Amelia Jackson | Feb, 2021
Neural Networks

The Ways in Which Big Data can Transform Talent Management and Human Resources | by Amelia Jackson | Feb, 2021

March 3, 2021
Introducing Research Tuesdays: Tuesday’s daily brief
Digital Marketing

Introducing Research Tuesdays: Tuesday’s daily brief

March 3, 2021
Ransomware puzzle: These two pieces of malware look very different, but they evolved from the same root
Internet Security

Ransomware puzzle: These two pieces of malware look very different, but they evolved from the same root

March 3, 2021
Researchers Unearth Links Between SunCrypt and QNAPCrypt Ransomware
Internet Privacy

Researchers Unearth Links Between SunCrypt and QNAPCrypt Ransomware

March 3, 2021
The Effect IoT Has Had on Software Testing
Data Science

The Effect IoT Has Had on Software Testing

March 3, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • An open-source machine learning framework to carry out systematic reviews March 3, 2021
  • The Ways in Which Big Data can Transform Talent Management and Human Resources | by Amelia Jackson | Feb, 2021 March 3, 2021
  • Introducing Research Tuesdays: Tuesday’s daily brief March 3, 2021
  • Ransomware puzzle: These two pieces of malware look very different, but they evolved from the same root March 3, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates