Anda VITOLS’ COVID-19 RESOURCES for data scientists
spring - 2020
OVERVIEW OF SITUATION IN WATERLOO REGION
  1. Public health (understanding & dealing with covid19)
  2. Regional business and economics (determining needs during pandemic)
  3. Municipal infrastructure, monitoring, reinforcement (supportive implementation during pandemic)
  4. Community involvement & volunteering (practical help for front-line workers, marginalized, and for well-being of everyone)
For voluntary compliance of public health recommendations, realistic and practical support is needed in municipal infrastructure, businesses, and employees during the shutdown periods to avoid bankruptcies and financial hardships. A consistent, cooperative message between public health and all levels of government is needed for public trust.

Overall the solution to a stealth pandemic like COVID19, favours a community-based attitude, not a fear-based approach using law enforcement tactics. Interacting with respect and kindness with each other is key. Why? Because people function best from a position of kindness, than a position of fear. People follow principles and procedures better than rules and threats. In difficult situations, people need a sense of control, at least in their immediate surroundings, to maintain well-being.

So far Waterloo Region is cooperating very well overall, and we are encouraged to continue. That’s fantastic! So far in Canada, compliance has been voluntary. Even though enforcement laws are in place for this pandemic, rarely have they been used. That’s comforting!

Therefore, from a data and social science point of view, we are studying a large a group of people that embrace our differences and are caring of each other. Only a very small percent of the population is rebellious and destructive.

PANDEMIC DATA SCIENCE MODELING

Pandemic Epidemiologist Models
Methods to describe spread of disease
  • Derivative - rate of change over time
  • Bayes - rate of probabilities over time
  • Statistics - snapshot of variable(s) at different points in time
Methods to look for heterogeneity (similarities, groups, clusters)
Goal1: IF identify unique group
  • THEN can potentially isolate it from the rest of population while disease takes normal course
Goal2: IF identify unique factors that
  • increase - the spread of disease
  • decrease - the spread of disease
    • THEN  public health provides recommendations to decrease risk of disease

Medicine
Machine learning is incredibly useful. Medical research is a field in data science where ML has rarely been used for manipulative purposes. It is easy to use inappropriate datasets and make biased conclusions in social sciences. In medicine, however, properties and methods of biological components have hashes that machine learning algorithms need for exploratory and diagnostic research. The conclusions educated guesses, but the datasets are reliable.
Methods to describe virus behavior:
  • Virus structure & mutations
  • Incubation
    • important for epidemiologists to know incubation time and contagiousness levels during that time
  • Immunity
    • important for epidemiologists to know how immune people are after recovery from different levels of exposure
Methods to describe testing:
  • Types of tests - dna, blood
  • Accuracy of tests (decrease false positives, and false negatives)
  • Increased testing advantages
    • increase target groups tested, so more accurate treatment can be administered
    • ability to more accurately understand the spread of the virus
Methods to describe vaccine behavior:

COVID-19 EPIDEMIOLOGY EDUCATION
March 2020
Who: Leonid Zhuhov
Mathematical Modeling of Epidemics. Lecture 1: basic SI/SIS/SIR models explained.
Mathematical Modeling of Epidemics. Lecture2: Epidemics on networks

April 2020
Institution: COMBINE: computation & mathematics for biological networks
Program: Network Epidemiology Online Workshop Series
  1. Net-COVID Session1A: Network Epidemiology Tutorial by Laurent Hebert-Dufresne  https://www.youtube.com/watch?v=5NGFDnJKiKA
  2. Net-COVID Session2A: Network Epidemiology Tutorial by YY Ahn  https://www.youtube.com/watch?v=8XHBYdHBhDI&list=PLVWaQYnj_BZVQal-KQ0rf8CcZJPIhpuO3&index=2
  3. Net-COVID Session3A: Human mobility and control measures in the COVID-19 epidemic by Sam Scarpino  https://www.youtube.com/watch?v=BrrGxJT6-iA&list=PLVWaQYnj_BZVQal-KQ0rf8CcZJPIhpuO3&index=3
  4. Net-COVID Session4A: Math Models of Epidemic Spreading in the Time of COVID-19 by Ginestra Bianconi  https://www.youtube.com/watch?v=vZ2Ezsffkqs&list=PLVWaQYnj_BZVQal-KQ0rf8CcZJPIhpuO3&index=4

REPORTS
Independent reports
Explains effect of spread disease during the incubation period, when people are usually asymptomatic
Explains simple mathematical model of spread of disease

Meta (reports database)

Wikipedia
2020 corona virus pandemic in Canada - Wikipedia
2020 corona virus pandemic in Ontario - Wikipedia

Kaggle
Datasets:
Reports:

COVID19 DATA SETS USED
CSSEGIS and Data
  • - c/o John Hopkins School of Engineering- Baltimore MD
  • Data on cases by location worldwide

Master
Daily: exp. march 16 2020

National Library of Medicine
Data on the disease structure
Search results for: SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome

Kaggle

COVID19 Datasets for Machine Learning
curated by Sasha Luccioni (Mila)
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