New COVID-19 research focuses on Latin America
Informing Policy, Resource Allocation and Workplace Adjustment Policies
COVID-19 has taken the world by storm, placing significant pressures on healthcare systems. Particularly in countries with limited testing resources and capacity-constrained health care systems, it is essential to determine who is at most risk for developing COVID-19. Knowing who may or may not need medical attention, and what type of medical attention enables planning and resource allocation at the local, state, and nationwide level.
To address this need, researchers from Boston University College of Engineering and the Center for Information and Systems Engineering (CISE) conducted a study to predict COVID-19 outcomes in Mexico using Machine Learning methods. Study results were published today in the International Journal of Medical Informatics in a paper titled “Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator.”
Developing Risk Assessment Models for Patient Outcomes
Yannis Paschalidis, CISE Director and Professor (ECE, SE, BME, CDS), Christos Cassandras, Head of the Division of Systems Engineering, and Professor (ECE, SE), and Salomón Wollenstein-Betech, Ph.D. candidate (SE), developed personalized models that predict an individual’s likelihood of hospitalization, mortality, need for ICU, and need for ventilation with a sample of 91,000 COVID-19 cases from records made public by the Mexican government. After analyzing the information, the researchers built models that consider pre-existing conditions and how the disease could progress. They discovered that key factors affecting the need for hospitalization and ICU included age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression.
“The virus appears to be basically the same around the world; still societies get impacted in very different ways, not only because of local and national policies but also because of different demographics and prevalence of underlying diseases,” says Paschalidis. “This work enabled us to focus on Mexico, for which not much work had been done, and develop custom models that take into account local characteristics.”
The idea [for the paper] was not to build an accurate clinical decision support tool; rather the objective was to do risk assessment to inform policy and resource allocation,” explains Wollenstein-Betech. “In particular, in countries like Mexico or other low-medium income countries, COVID has been very taxing on the health care system. Our risk assessment allows individuals to assess their own risk without going to a hospital and getting tested… so the government can assign resources to treat people who need it the most.”
The BU team used a larger data set than previous predictive models, therefore reducing bias and increasing accuracy. Said models determined by Paschalidis, Cassandras, and Wollenstein-Betech achieved a 72% accuracy for predicting hospitalization, 79% for mortality, 89% for ICU admission, and 90% for use of a ventilator.
“This is a first among a series of our work on COVID-19, utilizing data and employing data science methods to predict who is at risk for more severe versions of the disease,” says Paschalidis.
Cassandras adds that “Models of this type are extremely useful for resource allocation and staffing at hospitals, but also to inform workplace adjustment policies and prioritize vaccination when a vaccine becomes available.”
With further research, the models could spearhead new developments in policies and guidelines to better protect vulnerable populations. Read the paper here.
By Eliza Shaw, CISE Staff
