CovCap

AI-Driven Global Health Data Forecasting for Healthcare Readiness

CovCap is a collaboration project with GISAID that brings global Covid-19 data together with Artificial Intelligence, to provide more accurate research data for:

🔸 Forecasting of country specific healthcare capacities & limits

🔸 Infection (spread) patterns (independent of external bias)

🔸 Actual mortality rate & affected demographics

🔸 Understanding of external limiting factors

The Challenge

Accurate, unbiased global health data is crucial for effective public health management and emergency preparedness, yet often, data collection is inconsistent, subject to political biases, and suffers from significant lag times. This haphazard data makes it exceptionally difficult to forecast healthcare capacities, understand infection spread patterns, and determine true mortality rates, particularly during global health crises.

Our Solution

Yowie Tech Studios collaborated with GISAID on CovCap, an AI & Big Data platform designed to overcome these challenges using global COVID-19 data. Our solution leveraged Machine Learning to identify patterns across diverse datasets, drawing comparisons between countries with similar demographics, climates, and environmental factors. This process generated a normalised, unbiased data set, more reflective of the actual state of disease spread and immunity. The platform provided an early warning system, forecasting the likely draw on a country's healthcare resources ahead of time.

The Impact

CovCap transformed how GISAID and public health bodies could ground-breakingly assess and respond to health crises. It offered clear insights into expected hospital bed, ICU, and ventilated patient capacities, alongside correlations between cases and factors like UV exposure, ethnicity, and preventative measures. This enabled more accurate research, unbiased insights, and critical foresight for healthcare resource planning, significantly enhancing readiness and response capabilities during pandemics.

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