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![Steps in Data Wrangling
• The overall process of extracting the primary data (State, Date, and Mobility) involved utilizing SQL que- ries to filter out the
Descartes Labs data set in groupings for the individual states of California, Florida, New York, Pennsylvania, and Texas
• another partition was applied on the date in the form of a range in order to maintain consistent statewide data from March 10,
2020, to May 28, 2020. Once completed and initially filtered under the above two constraints, an API call was created from
www.data.world to be read and manipulated further via the Python programming language (Python 3.8.1). Likewise, a Python
script was created to preprocess the dataset by managing and organizing the filtered data into data frames
• Additionally, to construct a variable for the effect or contribution of government-imposed restrictions, a logical comparison was
implemented to make a binary coded variable that would take assigned values of 0 (No Restriction present) or 1 (Restriction
present) based upon the respected date values from the data found in the Tracking Involuntary Government Restrictions (TIGR)
Dataset.
• The Cases and Deaths data were compiled from JHU CSSE, Worldometer, and IHME and integrated into the same Python script,
adding on to the initial data frame.
• Random sample from the source data: a random subset of the data was taken for each of the individual states (20%) via
Python’s random sample() function and flattened as averages to yield distinctive data records for each date in the range
• Finally, a conversion of the data's format (DataFrame to CSV) took place, which enabled the migration directly to RStudio (Version
3.6.3) for performing statistical modeling and analysis. A variety of packages were utilized in RStudio namely broom for
summarizing the model results, ggplot2 as a graphical visualization tool, readr to process the CSV data file, dpylr for data
manipulation, lindia [12] for creating regression diagnostic plots along with verifying linear model assumptions, and knitr [13] for
printing and exporting the results of our analyses.](https://image.slidesharecdn.com/multipleregressioncovidmobilitypdf-210923091042/85/Multipleregression-covidmobility-and-Covid-19-policy-recommendation-25-320.jpg)
Uploaded byKan Yuenyong
Multipleregression covidmobility and Covid-19 policy recommendation
The document presents a multiple regression analysis of mobility data during the COVID-19 pandemic, comparing sources from Apple, Google, Twitter, and Descartes Labs. It emphasizes the importance of mobility tracking for informing evidence-based policy decisions and explores the correlation between lockdown measures and mobility trends. The analysis utilizes Python and R for data wrangling and statistical modeling, with findings supporting the significance of understanding human mobility patterns in the context of public health responses.
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Multipleregression covidmobility and Covid-19 policy recommendation
- 1. Mobility Tracking DuringCovid-19: A Multiple Regression Analysis Kan Yuenyong Presented: Wednesday, September 8, 2021, Revised: Thursday, September 23, 2021 GSPA NIDA DA8120 Quantitative Research II
- 2. Why This Paper? •Multiple Regression Analysis and Covid-19 policy is the contemporary agenda • It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal • Data source from Descartes Labs, a spinoff startup from the Los Alamos National Laboratory • Open Data, can be accessed at http://github.com/ descarteslabs/DL-COVID-19. • A chance to compare between: (1) Apple mobility trend report, (2) Google community reports, (3) Twitter mobility report, and (4) Descartes Labs’ Mobility report
- 4. What is DescartesLab? • Descartes Labs is building a digital twin of the world by applying machine learning to satellite imagery and other massive data sets, such as weather data, pricing and customer data. The solution is based in the cloud, which means it can scale storage for the massive data sets, and scale compute capability to enable analysis results and data to be returned more quickly. • The Descartes Labs data refinery offers geographic data including the entire library of satellite data from the NASA Landsat and ESA Sentinel missions, the entire Airbus OneAtlas* catalog, and NOAA’s Global Surface Summary of the Day weather dataset. The data has been combined and cleaned, so it is ready for machine learning analysis. • Looking ahead, the company is building what it describes as a “digital twin” of the earth, the idea being that in doing so it can better model the imagery that it injests and link up data from different regions more seamlessly (since, after all, a climatic event in one part of the world inevitably impacts another). Notably, “digital twinning” is a common concept that we see applied in other AI-based enterprises to better predict activity: this is the approach that, for example, Forward Networks takes when building models of an enterprise’s network to determine how apps will behave and identify the reasons behind an outage. • Extracting, managing, predicting genuine big data online realtime
- 5. A comparison betweenmobility data • Human mobility data provide valuable insight into how we adjust our travel behaviors during the COVID-19 pandemic. • Human mobility records from Descartes Labs, Apple, Google, and Twitter are compared. • Multi-source mobility datasets well capture the general impact of COVID-19 pandemic on mobility in the U.S. but present unique and even contrasting characteristics • The proposed responsive index quantifies the level of mobility-based reaction in response to the COVID-19 pandemic • All selected mobility datasets suggest a statistically significant positive correlation between the responsive index and median income at the U.S. county level.
- 6. Mobility data comparisonon four platforms
- 7. Mobility data comparisonon four platforms
- 9. Research Question ->Policy Recommendation • Whether lockdown policy is relevant to control Covid-19 outbreak? • Do people voluntarily stay home, or because of the government’s order, or both? • How we can track people’s mobility & activity? And with what tool? • What is the model to explain lockdown policy? • Evidence-based Policy (EBP) on recommendation for the Pincer tactic: (1) a (quasi) lockdown to slowdown the spreading + (2) at least 70% of total population vaccination to build herd immunity; optimizability between national health security vs economic security • Next stage = Crossing the Rubicon (Open the country + New normal health policy to contain the Covid-19 + Full economic stimulus campaign)
- 10. Data as ofearly July 2021 Data as of early September 23, 2021 Thailand Moving Average Trend in Google mobility report Orange = 7 days moving average; Dark Blue = 20 days moving average
- 11. Original code ofGoogle Mobility report in Thailand is from my Kaggle: https://www.kaggle.com/kanyuenyong/covid-19-community-mobility-reports-in-thailand, updated version, i.e moving average, is in my local Anaconda-Jupyter platform
- 16. The recent availablevaccine technologies Covid-19/Operation Warp Speed gao-21-319.pdf
- 17. Researchers discover hiddenSARS-CoV-2 'gate' that opens to allow COVID infection https://phys.org/news/2021-08-hidden-sars-cov-gate-covid-infection.html “N343”, a possible key to pave the way for “universal” Covid-19 therapeutic and vaccine
- 18.
- 19.
- 20. Evidence-Based Policy The latestdevelopment of policy science can be called “evidence-based policy” (EBP) focusing on systems thinking that “recognizes the world as a complex system composed of a large number of influences that, for the most part, are interacting simultaneously, rather than focus on component parts in isolation” and thus a causal linkage between evidence and relevance knowledge as well as between evidence and short-term/intermediate/long-term policy outcomes. The EBP has been largely applied in health policy and medicine such as Cochrane Collaboration (C1) and the Campbell Collaboration (C2), both C1 and C2 use research synthesis and meta-analysis to assess the causally relevant outcomes as well as medical intervention (Dunn, 2018: 41).
- 21. https://covid19scenariomodelinghub.org/viz.html Scenario Projection withdifferent variables
- 22. Possible Vaccine Approval(scenario) https://www.covid19-predictions.org/?page=editparameters
- 23.
- 25. Steps in DataWrangling • The overall process of extracting the primary data (State, Date, and Mobility) involved utilizing SQL que- ries to filter out the Descartes Labs data set in groupings for the individual states of California, Florida, New York, Pennsylvania, and Texas • another partition was applied on the date in the form of a range in order to maintain consistent statewide data from March 10, 2020, to May 28, 2020. Once completed and initially filtered under the above two constraints, an API call was created from www.data.world to be read and manipulated further via the Python programming language (Python 3.8.1). Likewise, a Python script was created to preprocess the dataset by managing and organizing the filtered data into data frames • Additionally, to construct a variable for the effect or contribution of government-imposed restrictions, a logical comparison was implemented to make a binary coded variable that would take assigned values of 0 (No Restriction present) or 1 (Restriction present) based upon the respected date values from the data found in the Tracking Involuntary Government Restrictions (TIGR) Dataset. • The Cases and Deaths data were compiled from JHU CSSE, Worldometer, and IHME and integrated into the same Python script, adding on to the initial data frame. • Random sample from the source data: a random subset of the data was taken for each of the individual states (20%) via Python’s random sample() function and flattened as averages to yield distinctive data records for each date in the range • Finally, a conversion of the data's format (DataFrame to CSV) took place, which enabled the migration directly to RStudio (Version 3.6.3) for performing statistical modeling and analysis. A variety of packages were utilized in RStudio namely broom for summarizing the model results, ggplot2 as a graphical visualization tool, readr to process the CSV data file, dpylr for data manipulation, lindia [12] for creating regression diagnostic plots along with verifying linear model assumptions, and knitr [13] for printing and exporting the results of our analyses.
- 29. The Mobility Model •Y=β0 + β1X1 + β2X2 + β3X3 + ε • Where Y = Mobility Index, X1 = Restriction (Policy), X2 = Cases, X3 = Deaths, ε has a normal distribution with zero mean and unit variance
- 33. Future research recommendation •To tackle with high-complicated data set, Logistic model tree (LMT), a combination between Logistic regression and Model Tree Learning with supervised training algorithm will be chosen • A quantum based algorithm, i.e Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem to resolve a complex big data analytics (i.e. data from Descartes lab can be categorized in this manner) with Hadamard transformation to ultra-speeding up the simulation • • Evidence-Based Policy (EBP) is a problem of policy optimizability (between emergence/forces in complex adaptive system) practiced in the futuristic algorithmic governance (plus holacracy)
- 34. GSPA NIDA DA8120 QuantitativeResearch II Q&A? END