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Strategies emerge for better managing healthcare systems during pandemics

Better resource-allocation and mitigation strategies could result in fewer COVID-19 cases and deaths.

Jeff Lagasse, Associate Editor

(Photo by John Fedele/Getty Images)(Photo by John Fedele/Getty Images)

Healthcare systems could save lives and minimize losses by optimizing resource allocation and implementing mitigation strategies, according to two new studies. 

Colorado State University researchers explored how healthcare systems might perform under multiple disasters and multiple waves of COVID-19, and how they can keep functioning when they're needed most.

In the first study, published in Nature Communications, civil and environmental engineering Ph.D. student Emad Hassan and associate professor Hussam Mahmoud investigated the compound effects of pandemics and natural disasters on healthcare systems. They combined wildfire data with projections of the spread of COVID-19 to evaluate different strategies for managing patient demand in the event of concurrent disasters. 

They found that better resource-allocation and mitigation strategies, such as organized evacuation, protecting shelter residents and using non-acute hospital beds, could result in fewer COVID cases and deaths.


Wildfires occur every year in the U.S., and they increase the demands on healthcare systems. Wildfire smoke victims may require some of the same resources needed by COVID-19 patients, such as ventilators. For these reasons, Hassan and Mahmoud chose to study wildfire in conjunction with the pandemic.

To measure hospital functionality under the simultaneous burdens of pandemic and wildfire, they used a healthcare system model they developed previously and a new disease transmission model they created to predict the number of people in different stages of COVID-19. They modified a well-known disease transmission model, SEIR, to include additional stages of the disease.

SEIR stands for Susceptible, Exposed, Infectious and Recovered. The CSU researchers augmented the model to include susceptible, insusceptible, exposed, infective, quarantined, hospitalized, ICU admitted, ventilator-dependent, recovered and deceased.

Using the two models they developed, along with publicly available data on the number and type of beds available in each hospital, Hassan and Mahmoud calculated the extent of disease transmission under various scenarios in every county in the U.S.

They then determined the risk of easing restrictions and the resulting shortage of beds by type – inpatient vs. intensive care, for example. Last, they identified the counties where patient demand could exceed health system capacity and presented mitigation strategies that could reduce the number of cases requiring medical services.

Their analysis, published in PLOS One, predicts how additional waves of COVID-19 might unfold.

The study shows that stricter COVID-19 preventive measures – including mask mandates, social distancing, and shutting down schools, workplaces and indoor activities – could reduce the number of hospitalized cases by as much as 12.8%. On the other hand, lifting restrictions could cause a spike in hospitalizations 13.7 times higher than the second wave's peak – pushing hospitals in many counties beyond their capacity.

Hassan and Mahmoud hope planners, policymakers and state officials can use their research to make decisions about additional resources and when to ease restrictions. Their disease-transmission model, which predicts how many patients will be in each stage of the disease, could help hospitals allocate resources accordingly.


In February, epidemiologists found that a combination of robust vaccination programs and strict physical distancing rules could avoid recurring peaks of COVID-19 without the need to rely on stay-at-home restrictions.

The impact of social distancing in containing future resurgences of COVID-19 depends greatly on the intensity of measures, the population density and the availability of vaccines across geographical areas and time.

Twitter: @JELagasse
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