Never before has health informatics played a greater role in public health than during the COVID-19 pandemic. While the Health IT industry is certainly challenged due to the overall disruption to the healthcare system, the pandemic undoubtedly underscores the opportunity and importance of health informatics, such as telehealth, remote patient monitoring, patient engagement, AI-based drug discovery, precision medicine, and clinical decision support, but nowhere more pressing than the fields of bioinformatics, clinical surveillance, and predictive modeling.
One of the challenges that policymakers, public health officials, and economists are struggling to define is the true Infection Fatality Rate (IFR) of COVID-19. Case Fatality Rate (CFR) and Infection Fatality Rate (IFR) are often used interchangeably, but the definitions can be meaningfully different especially when there is the potential for a large discrepancy between Diagnosed and Infected cases, as is the case with COVID-19.
IFR = (Number of Deaths) / (Number of People Infected) x 100
CFR = (Number of Deaths / (Number of People Diagnosed) x 100
Farzad Mostashari shared a particularly insightful perspective via Twitter on April 17. Paraphrasing his thoughts, there are a few key considerations to make in using the case-rate curves to inform public policy.
1. The data we have today is based on report date, rather than date of symptom onset. In previous epidemics, like those for Salmonella, the lag between symptom onset and report date has been around 2-3 weeks. Such a lag creates potentially misleading trends – “Say you cleared a backlog of old samples last week, diagnosed a lot of old cases, it would look like a bump and then a decline.”
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Source: Hit Consultant
