The healthcare industry is turning to real-world data and predictive analytics in pandemics to better understand, monitor, and prepare for whatever the virus may bring. For instance, if they expect an upsurge in cases in the next four days and the beds at their hospitals are all filled up, they will have time to construct temporary beds or arrange from external sources. Thorough knowledge base of COVID-19 with the help of machine learning is paramount for us to model and understand the spread of COVID-19, and eventually mitigate the repercussions of the virus on public economy, society, and health. The information can also help leaders recognize which geographic locations may need more critical care resources or stricter interventions.
Predictive modeling in healthcare and forecasts of new cases and hospitalizations will help inform public health decision making by projecting the likely impact of the COVID-19 pandemic in coming weeks. With talks of reopening economy, policymakers are relying on predictive modeling in healthcare for help in predicting where new coronavirus cases may surge as stay-at-home orders expire.
Informing Next Steps
Blackstone’s cutting-edge predictive healthcare tool evaluates the possible outcomes of interventions such as social distancing and quarantine and the model can be set to show possible trajectories under different hypothetical scenarios within different locations, such as “with social distancing” and “without social distancing” disease propagation in Istanbul. The modelling frameworks shows how different types, durations, and intensities, of interventions impact the spread of the virus over time. The forecasts based on maximum and minimum social distancing efforts can help in gauging the impact of a policy on disease spread and may help governments draw back from a complete return to normalcy. Based on the predictive analytics and healthcare tool’s findings, protective efforts like mask wearing and hand washing, as well as stringent social distancing, may slow down the spread of the virus.
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