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The Roles of Data Science and Risk Assessments During the COVID-19 Pandemic
Kelly H. Zou, Jim Z. Li, Tarek A. Hassan, Joseph Imperato, Jorge Enrique Saenz and Danute Ducinskiene, Upjohn Division, Pfizer Inc
Coronavirus disease 2019 (COVID-19) is a disease caused by a highly contagious pathogen, which is known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Historically, there have been numerous pandemics from multiple infectious agents. Just influenza pandemics alone had four occurrences between 1918 and 2009, along with earlier ones throughout the history.
(1) 1918-1920 Pandemic (H1N1)
(2) 1957-1958 Pandemic (H2N2)
(3) 1968-1969 Pandemic (H3N2)
(4) 2009-2010 Pandemic (H1N1pdm09)
Risk Assessments for COVID-19
The worldwide numbers of confirmed COVID-19 cases and deaths continue to rise, bringing back the memories of prior devastating pandemics. A research model, from the Imperial College London, estimated that “in an unmitigated epidemic, we would predict approximately 510,000 deaths in GB and 2.2 million in the US, not accounting for the potential negative effects of health systems being overwhelmed on mortality,” where GB refers to the Great Briton and US refers to the United States.
Risk assessments of co-morbid conditions have been conducted in various countries.
Data scientists can make significant contributions towards the fight against this pandemic.
For example, the percentages of the top comorbidities from each of the following studies are listed below, where the denominators of the percentages are the respective confirmed COVID-19 subjects.
(1) Hypertension (13 percent) in a 44,672-subject study in China
(2) Chronic cardiac disease (31percent) in a 20,313-subject study in the United Kingdom
(3) Hypertension (57 percent) in a 5,700-subject study in the US
(4) Diabetes (36 percent) in a 355-subject study in Italy
There are several publicly available databases to aid the data scientists’ efforts to fight against this pandemic.
The Importance of Data Science
Data scientists can make significant contributions towards the fight against this pandemic. They can utilize their skillset including quantitative analytics, precision medicine, predictive modeling, machine learning, deep learning, and artificial intelligence to gain insights.
Specifically, there are several measures commonly used for informed decision-making. First, data scientists can help measure disease burdens and trends. The latter is particularly important given the rapid increase of infected cases, hospitalizations, and deaths. For example, data scientists can evaluate the burden of disease (incidence, prevalence, attack rate, years of life lost, case fatality rate, and population mortality rate) and trace the virus transmissibility (replication and mutation).
Furthermore, data sciences can project the spatial-temporal changes of the cases and deaths under various conditions, evaluate the accuracy of diagnostic tests (sensitivity, specificity, positive predictive value, and negative predictive value), and assess the outcomes of treatments (efficacy, effectiveness, safety, and healthcare resource utilization).
In summary, real world data can be extremely valuable to reduce uncertainty during this pandemic. Data scientists can use such an abundance of information through the rigor in methodology, analytics, and interpretation. This pandemic also presents opportunities for wider adoption of telemedicine, health technology, digital innovation, and partnerships across sectors.