The many respiratory viruses that cause influenza-like illness are generally reported and tracked by health professionals as one generic entity, despite the fact that the viruses and their various treatments can vary dramatically. In the United States alone, some form of influenza-like illness, or ILI, infects nearly 50 million people every year. Now, new work from Los Alamos National Laboratory and the University of New Mexico shows that more detailed modeling of the specific viruses can be vital for early identification and treatment of infection, while also facilitating better pandemic preparedness.
“Influenza-like illness is an inherently confusing term, because during the past 20 years in the U.S., influenza viruses have been only 23% to 43% of total ILI,” said Julie Spencer, the first author on the paper. “An infant with flu symptoms could have a relatively harmless rhinovirus or the more dangerous RSV, but how do we know? One of our goals with our new paper is to motivate increased diagnostic testing at the point of care for increased national health security.” The research was recently published in the Journal of Theoretical Biology.
ILI is defined by the Centers for Disease Control and Prevention as a group of symptoms that include a fever of at least 100 degrees Fahrenheit, a cough and/or a sore throat. But while tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Indeed, the recent COVID-19 pandemic also falls into this category.
“Modeling multiple respiratory infections allows us to be prepared for emerging threats and previously unknown pathogens. As COVID-19 has shown us, this is required for pandemic preparedness,” said Harshini Mukundan, one of the senior authors on the paper.
Most existing models explore either a single respiratory virus or ILI as a whole. However, the researchers determined a clear need for models capable of comparing several individual viruses that cause respiratory illness, as the subtleties of each virus can mean different treatment and mitigation protocols. In addition, with bacterial and viral infections sometimes being difficult to differentiate, millions of unnecessary prescriptions are written for antibiotics each year.
In the paper, the researchers present a flexible model and simulations of epidemics for a range of viruses that can present as ILI, including influenza, respiratory syncytial virus (RSV), rhinovirus, seasonal coronavirus, adenovirus and SARS/MERS, accompanied by a global sensitivity analysis.
All the viruses modeled present clinically with similar symptoms, but their severity, transmission dynamics and therapeutic strategies varied considerably. Thus, there is a vast potential not only for misdiagnosis, but for missing early signals of future novel emerging diseases.
Models such as the one presented in the new manuscript can help delineate the differences among these classes of viruses, potentially allowing for streamlined responses. The authors also found a clear need for comprehensive viral testing at the point of care, in order to facilitate suitable therapeutic intervention or control strategies for prevention of further transmission.
Paper: Distinguishing Viruses Responsible for Influenza-Like Illness, Journal of Theoretical Biol. 2022 Apr 28;111145. doi: 10.1016/j.jtbi.2022.111145. Online ahead of print.
Funding: This work was supported in part by the U.S. Department of Homeland Security. This work was also supported by a grant from the University of New Mexico College of Arts and Sciences.