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COVID-19 Spread Burdens Hospital Capacity, Displaces Non-COVID Patients

Apr 18, 2021 by Placekey

This seminar covers the main topics presented in A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand by Tanmoy Bhowmik and Naveen Eluru.

Relying on foot traffic and location data from Safegraph and Placekey, Bhowmik and Eluru study the impact COVID case growth, hospitalizations, and ICU usage on hospital and ICU capacity for both COVID and non-COVID patients.

The goal of their research is to predict hospital capacity issues prior to reaching that limit. By identifying the areas that are at the highest risk for reaching capacity, plans can be developed that will account for these shortcomings and mitigate the overall impact.


COVID-19 case growth leads to increased hospitalization and ICU usage for both COVID and non-COVID patients

Bhowmik and Eluru study the burden that COVID-19 case growth has on both COVID and non-COVID hospitalization and ICU rates. Their study analyzes how non-COVID hospitalization demand is being displaced due to the hospitalization and ICU surge caused by COVID. To do this, they look at both COVID and non-COVID patients.

Their hope is that this information can be used to build prediction models that can identify potentially vulnerable locations. They can then address these hospital demand issues across the nation more effectively and develop appropriate remedial measures to support the hospitals that are most affected.


5 key takeaways from this seminar on COVID’s impact on hospital and ICU capacity

Bhomik and Eluru’s paper examines the impact COVID-19 infection, transmission, and case rates have on hospital and ICU capacity. They conclude that COVID-19 infection rates caused a significant burden on hospital and ICU capacity, and that COVID-19 patients directly displaced non-COVID patients.

Their hope is that this information can inform policies and plans for handling capacity issues in hospitals and ICUs by helping to identify areas with the greatest risk.

1. COVID-19 transmission and case growth has a significant burden on hospital and ICU capacity

COVID health and safety

Time in seminar video: 4:50

The first area of focus was the impact that COVID-19 case growth has on hospital capacity. Hospitals are the epicenter of COVID-19 cases, as many people are admitted to the hospital to be evaluated and receive treatment.

Their findings show that COVID-19 case growth leads to higher hospitalizations and ICU bed usage, which places a substantial burden on our healthcare system in terms of overall hospitalizations and ICU bed usage. This creates a significant demand for hospital and ICU beds, when there is a limited quantity available at any time.

2. The increase in COVID-19 hospitalizations forces non-COVID patients out of hospitals and ICUs

Time in seminar video: 6:57

COVID-19 has affected all hospitalizations, not only COVID-19 hospitalizations. This means that non-COVID patients are also being affected by this increase, as these patients are being pushed out of the hospital and ICU, and their required treatments are being delayed.

Many non-COVID patients are being forced to reschedule their services, which are often critical to their health and well-being. The greater the burden COVID-19 has on hospital resources, the more non-COVID patients are affected.

People know that the hospital is the epicenter of where COVID-19 is being fought, which discourages people from going. This increases the severity of non-COVID health issues, as people are not seeking proper healthcare.

To analyze the impact of all of this, they developed four dependent variables to study:

  1. Hospitalization rate by COVID patients
  2. Hospitalization rate by Non-COVID patients
  3. ICU usage rates by COVID patients
  4. ICU usage rate by Non-COVID patients

Using these four dependent variables, we can view the entire impact of both COVID and non-COVID patients, as well as see how COVID-19 patients are directly impacting hospitalization and ICU usage rates, and the displacement of non-COVID patients.

3. The importance of factoring in aggregate, county-level, and independent variables

Time in seminar video: 11:13

For their analysis, they consider a number of dimensions to the study:

  • COVID-19 and non-COVID demand
  • Aggregate-level data
  • County-level data
  • Exhaustive list of factors and independent variables

This analysis lets them examine larger trends, and compare these against specific counties. They can use this information to determine which hospitals require the most attention and support. 

After this, they can use their analysis as a groundwork for building a plan to handle hospital capacity, such as increasing the number of nurses, building healthcare infrastructure in the area, and looking to outside support.

4. Increased COVID-19 transmission and hospitalizations heavily burden COVID and non-COVID hospital capacity

Time in seminar video: 20:45

They graphed their findings to display hospitalization and ICU rates, and the impact this had on hospital and ICU bed capacity.

Image CreditA Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand by Tanmoy Bhowmik and Naveen Eluru

The top two graphs show the ICU bed usage rate and the COVID transmission rate (which follow a nearly identical pattern). All values are per 100K people. These graphs also display the total number of beds, the total ICU patients admitted, and the COVID ICU usage. With this data overlaid, viewers can easily see the impact that COVID transmission has on ICU bed usage.

The bottom two graphs show the hospitalization rate and average exposure rate (which follow a nearly identical pattern). All values are per 100K people. These graphs also display the total number of hospital beds, the total number of hospitalizations, and the COVID hospitalizations. All of this data combined into one graph shows the impact COVID exposure has on the hospital occupancy rates.

In both cases, this data shows that increased COVID exposure and transmission rates lead to higher hospitalizations and ICU rates. Since both show the total hospitalizations and ICU usage as well as COVID hospitalizations and ICU usage, these graphs can be used together to analyze the impact COVID had on both COVID and non-COVID patients.

The main takeaway is that both hospitalizations and ICU bed usage rose significantly as COVID-19 cases increased, and that spikes in COVID-19 case growth directly led to issues with hospital and ICU bed capacity. Furthermore, this data can be used to predict where the highest risk is for hitting capacity in hospitals and ICUs.

5. Areas with existing health indicators in non-COVID patients are more likely to reach capacity limits

Time in seminar video: 23:02

Their findings show that as COVID-19 hospitalizations and ICU usage increases, and capacity is nearly reached, non-COVID patients are pushed out of hospitals and ICUs. This shows that COVID case growth directly impacts both COVID and non-COVID patients, and their access to healthcare.

In areas where the population has a high number of people with health indicators (for serious health issues such as cancer, heart, and diabetes), we see significant increase in hospital and ICU usage for both COVID and non-COVID patients. This is to be expected, as areas with hospitals that handle a high number of serious health issues will still have a high number of non-COVID patients requiring hospitalization and ICU beds.

This data can help us find hospitals and areas that are particularly susceptible to these shortages, and allows us to develop adequate plans to handle this influx in the future.


Tanmoy Bhowmik and Naveen Eluru’s backstory & research

Tanmoy Bhowmik is a Post Doctoral Associate working with Naveen Eluru at the University of Central Florida. His research areas include traffic safety, econometric modeling, and travel demand analysis.

Naveen Eluru is an Associate Professor at the University of Central Florida, where his research specialties include transportation planning, integrated socio-demographic and land-use modeling, sustainable urban design, transportation safety, integrating travel demand and supply models, and advanced econometric modeling. 


How Placekey enabled research on the impact COVID has on hospital and ICU capacity

For their research, Bhowmik and Eluru relied on foot traffic and location data provided by Safegraph and Placekey. Safegraph provides foot traffic data, made possible through device-tracking technology. Placekey is a universal identification system for any location, making location mapping and management extremely simple.

Both of these solutions helped provide researchers with accurate, reliable county-level data. With this data, they can be confident in the conclusions they draw about travel and transmission, and then predict future weak spots. 

To learn how you can use Placekey’s universal location identifiers for yourself, see the Placekey community page and find out how others like Bhowmik and Eluru have benefited. Here you can connect with users of Placekey to see how others are leveraging the Placekey technology in some amazing ways.

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