CovCap

GISAID helps provide global health data for research purposes, but as that data is gathered in many different ways in different countries, with differing lag times and often with political biases overlaid, making interpreting haphazard, unreliable and often wrong. It's not enough to just collect the data and report on it.

The data and the behaviours associated with where that data originates needs to be understood and adjusted for, if any meaningful forecasting and interpretation can be done.

Using Machine Learning to recognise patterns in the global data collection and drawing comparisons to other countries similar in demographics, climate, geography, and UV exposure, our model provides a normalised data set.

This data is more reflective of the likely actual state of Covid-19 spread and immunity in each country and more useful to draw unbiased insights from and to provide healthcare draw forecasting.

GISAID is able to provide an early warning system that forecasts the likely draw on any country's healthcare resources, ahead of time. We are able to provide data that shows:

🔹 expected capacity in terms of hospital beds, ICU beds (complex cases) and ventilated patients (critical cases)

🔹 comparisons to know healthcare capacity in terms of hospital beds, and ICU beds.

🔹 patterns of cases and correlations to UV exposure, ethnicity, population demographics and the effects of preventative measures.