The SI / SIR / SIRD models are epidemiological models based on first-order differential equations. These are used here to understand the characteristics of contagious illness over time using global data under the conditions of containment. A number of countries have been selected for which containment measures have lead to a significant reduction in the rate of spread. Data analysis and statistical treatment are performed using a frequentist framework. For this purpose the data processing framework ROOT developed by the European Laboratory CERN is used. Results are presented in terms of lower and upper curves for the cumulative number of positive cases as a function of time. These are estimated on the basis of a 68%  Confidence Level. Predictions are updated on a daily basis. The accuracy of these predictions improves with time. 

 

Disclaimer: Predictions displayed are valid as long as the application and adherence to containment interventions remain unchanged. Now this is not the case for many countries. The predictions for the worse case scenarios are indicative of short term predictions only.

 

Risk Index: This index quantifies the deviation of the data from the hypothesis of a single wave. It gives a measure of the risk for a second wave. Where the Risk Index is greater than 10%, the risk for a second wave becomes statistically compelling. 

 

We want to thank the SA-CERN program hosted by iThemba LABS of the National Research Foundation and funded by the Department of Science and Innovation.

 

Data sources:

    - Johns Hopkins University CSSE: https://coronavirus.jhu.edu/map.html 

    - Worldometers: https://www.worldometers.info/world-population/population-by-country/  

    - World Population Review: https://worldpopulationreview.com/