In the last predictive analysis series on Covid-19, we had tried to predict the likely active cases in US, India and UK basis learning from Spain and Italy. The predictions turned out to be close to what the actual numbers were. Now the World is focused on how some of the Big Economies of the World where the Infection has spready deeply move from here and tackle the issue. These countries namely the US, India, Brazil and Russia will determine how the Pandemic will shape up in the months to come. While Russia had more cases but lower deaths hence we look at the biggest impacted country the US, our own home country India and equally comparable big & developing economy i.e. Brazil. While US has been able to slow down the pace but new cities are seeing a second wave; at the same time India and Brazil numbers are growing quite fast of now.

The approach of predicting the numbers followed is like the last time i.e. the Transfer Learning Approach

When we predict any time series data we typically use internal data and predict the future for example when we predict the temperature of a city we use historic temperature of the city and predict the future using a model. The model can incorporate various factors like seasonality, auto-regression (relationship with recent past) etc. But when it comes to predicting a novel phenomenon like active cases in a pandemic this will not work as we do not have enough past data as we are seeing only one cycle. Nevertheless as various countries are in different stages of the pandemic, this gives us an opportunity to learn from data of other countries to project for countries that are behind in the curve. We use this approach to predict the number of cases in the near future for the US, Brazil and India. To build the model we use an ARIMA model (Auto-regressive, Integrated and Moving Average).

In statistics and econometrics, and in particular in Time series analysis, an Autoregressive Integrated Moving Average (ARIMA) model is a generalization of an Autoregressive Moving Average (ARMA) model. Both of these models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting). ARIMA models are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the “integrated” part of the model) can be applied one or more times to eliminate the non-stationary.

Another overlay on the model we do is to transform the existing data using various transformations. We have taken Italy and Spain as the base of transfer learning. Using these we identify the right transformation and one of the parameters of the ARIMA model namely the Degree of differencing (d).  Other parameters i.e. Auto-regression period (p) and Moving average period (q) are obtained for individual countries based on their best fit data. For transformations we used four different transformations: log, log2 (double log), square root and cube root. For differencing we tried d=1, 2 and 3.  We found that log transformation with double differencing (d=2) makes the data stationary in Italy and Spain. We use these parameters and optimize for p and q to forecast for all the countries.

We predict active cases using this approach.

Active Cases = Total cases – Deaths – Recoveries

Transfer learning has given a good framework for learning data from one country and using it in another country for forecasting novel phenomena. We use this approach not only for such time series forecasts but also for other forms of machine learning like natural language processing etc.

The forecasts based on these parameters for the next one month are given below :

Source for past data:, source for projections: G-Square Solutions Pvt. Ltd.

We predict the active cases in the US to stabilize in July last week, but the cases in India and Brazil to continue going up all the way to end of July. For India we predict the active cases to double by end of July.


Source for past data:, source for projections: G-Square Solutions Pvt. Ltd.



Leave a Reply

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