Predicting the Spread and Infection Risk of COVID-19

counting risks for health and life while planning a daily routine
iconData Analytics
iconPredictive analytics
iconData Science
iconMachine Learning

We created the covid - 19 prediction tracker, calculating the risk of being infected and the potential number of patients contracted per precise location in Israel. Read on how we are contributing to the return to normality with pandemics growth curve modeling.


Our customer had an ambitious goal to contribute to flattening the curve in a world-leading state regarding the effective vaccination campaign, Israel. As part of the larger project, our task was to develop a solution predicting the spread and infection risk of COVID-19.We have faced a triple challenge — the velocity of the disease spread, unexpected changes in the environment, and precise predicting up to the city district. Having neural networks and deep learning techniques in our arsenal, we took on the challenge.


Here is our way of tackling tasks like this:

  • The best option to deliver precise prediction and accurate generalization in an uncertain environment is the artificial recurrent neural network (RNN), namely long short-term memory (LSTM). It mainly comes in handy in the state when the situation changes dynamically. LSTM preserves "long-term" memory, beneficial for time-series data (i.e., data listed in time order) describing infected rates.
  • Normalizing data both for epidemic's beginning and real-time prediction could become a problem, meaning the massive difference between forecasts drawn on the different stages of the epidemic's development. However, the model learned how to deal with the statistical error at every particular stage of the disease spreading, as we have developed special techniques to implement that.
  • As our model was intended to predict infection rates for a particular town or its district, we added embedding layers to the model. Embedding layers are "trying" to compress data on cities in a most representative way for a machine learning (ML) model. This function "taught" the ML model to seek interaction within all the data, providing insights per any location.

We created a solution for predicting epidemic development in a particular country, specifying an accurate forecast per geographical location (e.g., town), scaling up to cities' districts. The model's prediction scope includes around 300 towns in Israel and its sections in the cities, considering the scales' differences.

Tech Stack

Tech Stack.png


Moreover, we developed a predicted scale of risk (rating from 1 to 8) detecting the chances of being contracted in a particular location at a given time unit. Predictions are drawn on the confirmed COVID-19 data (infected, recovered, death rates) combined with social and economic indexes (e.g., social behavior data).


In essence, using our solution, users can foresee the risks for their lives while planning their daily routine. Staying safe and taking care of their health, citizens flatten the curve and slow the spread.

Our contacts