Outline

• Summary Report
• Methodology
• Discussion
• Questions

Goal:

To better understand the temporal spread of Sars-Cov2 we explore the epidemiological self-similarity of the virus

Objectives:

• Show Sars-Cov2 exhibits a self-similar nature
  
• Show Sars-Cov2 exhibits a self-repeating nature
  
• Predict future cases of Sars-Cov2 with a robust methodology that accounts for uncertainty of its nature

Summary Report

Using Fractal analysis, we reveal that sars-cov2 is indeed self-similar in nature of its spread across time and space, and further that the virus follows a long-memory process, implying that it persists over a long period (the range of data collection from March 2020 to April 2021) once exposed in a community. We also show that while quarantines indeed help with reducing the spread of the virus, they do not stop the spread given its long memory nature.

Using the fact of long memory process and self-similarity nature, we are able to predict the number of cases into the future within a single person at each time stamp as measured using data from April 2021 to August 2021. With our forecast model, we also show that our methodology is robust by giving explicit probability distributions

Data Exploration

Please see accompanied file

Is the data self similar?

We answer this key question by finding the Hurst parameter

The higher the Hurst parameter, the higher the statistical self-similarity and higher its long-memory process

How do positive Covid cases score: 0.778

How do negative Covid cases score: 0.843

Fractal dimensions against time

Modeling time series

Model Fit

Uncertainty of the model

Uncertainty of forecast

Understanding the uncertainty

The plot shows that there is a low probability of the counts being less than 30. It is also showing that the probability distribution between 0 and .6 is centered around 25 weekly cases implying directional implication for higher case counts

Forecast plot

Conclusion

As we can observe from the plot, our overall forecast is well behaved and bounded by the uncertainty bounds which indicates low uncertainty in the forecast model and thus, a robust outcome that can augment decisions of healthcare coordinators and policy makers

Outcome

• As we can observe from the data, sars-cov2 does indeed have a self-similar process
  
• The persistence of sars-cov2 over time shows that a 14-day quarantine is not enough
  
• We are able to model the epidemiological state of sars-cov2 over time with a low uncertainty

Thank You

Questions??