How Financial Analytics Can Help During The Flu Season

What is Financial Analytics?

Financial analytics deals with the question of whether and with what probability certain events could occur in the future. For this purpose, historical data sources are used to train mathematical models that identify trends and patterns in the data. The model is then fed current data to predict the likelihood of future events.

In this way, financial analytics is a useful tool with which companies and organizations can identify risks early on and make better business decisions. One sector that can gain much from the use of predictive analytics is the healthcare industry.

How Can the Healthcare Industry Leverage Predictive Analytics?

Let’s take a look at three concrete ways in which predictive analytics can help the health industry combat a disease like COVID-19.

Forecasting which Patients Would Be Most at Risk 

Early in the pandemic, one of the most important use cases for predictive analytics was to determine which patients were most at risk for contracting the virus and which individuals were most likely to have a poor course of COVID-19 infection.

Optimizing Hospital Resources

Another issue that was prevalent in the early days of COVID-19 was the sudden increase in hospitalizations. This resulted in hospitals dealing with the challenges of limited availability of medical equipment and human resources and an additional burden on existing staff.

‍While hospitals are no longer in the early days of dealing with the pandemic, predictive regression models can still be extremely valuable in forecasting new localized COVID-19 cases. 

This information can be used to help hospitals better plan for the potential increase in patient numbers and more efficiently allocate resources. Hospital beds, ventilators, and hospital personnel can be better and more efficiently organized and prepared in advance of a surge.

Forecasting Regional Surges and Hotspots

There are few situations as unpredictable as a global health crisis. Knowing which areas will be hit hardest can help local decision-makers plan ahead against poor outcomes. Predictive analytics models can use current historical data on the spread of COVID-19 in specific regions to predict with high accuracy what will happen to that region in the coming days in terms of new COVID-19 cases.

‍Furthermore, historical COVID-19 data of a region can be used to predict how the virus will then spread to other regions. In this way, financial analytics can forecast regional surges and hotspots. Having this information a few days in advance allows for implementing countermeasures in the form of social distancing rules or lockdowns.

‍As an additional benefit, such forecasting can be achieved using fewer personal data. In the early days of the pandemic, the migration of the COVID-19 virus was tracked using the travel information and contacts of people who were infected with the virus. This detailed information was difficult to obtain due to privacy concerns and the availability of resources like personnel who collect, track, and evaluate this information.

‍Financial analytics models do not require information on individuals’ movements or contacts. These models can use data on the recent local spread of a virus from an outbreak in a certain region to forecast where, in what way, and how quickly the virus will spread in another region.