Speaker: Julie Spencer, Los Alamos National Laboratory
Abstract: The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity. ILI is defined by the CDC as a group of symptoms including a fever of at least 100 degrees Fahrenheit and a cough and/or sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a clinical syndrome is important for analysis, forecasting, and planning, the underlying viruses differ in many respects. How do respiratory pathogens with similar clinical presentations differ in their epidemiological parameters and outbreak properties? We systematically reviewed 104 studies for the epidemiological parameters of influenza A and B, respiratory syncytial virus (RSV), rhinovirus, coronavirus, adenovirus, and the outbreak coronaviruses severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS-CoV). We developed a deterministic model and simulated the progression of each virus in a hypothetical flu season, using mean parameters from the literature. We conducted global sensitivity analyses to assess the relative impact of the input variables on the response variables. According to our literature review, adenovirus has the longest mean incubation period, coronaviruses have the longest total infectious period, and RSV has the highest R0. Outbreak versus seasonal coronaviruses have almost double the mean incubation period, more than double the infectious period, and a 34% higher mean R0. Our numerical simulations indicate that RSV peaks first, while influenza and SARS/MERS peak approximately five months into the season. Of the input variables, the basic transmission rate has the clearest impact on total infections, peak height, and time to peak. In view of these differences, failure to distinguish between respiratory pathogens has implications for misdiagnosis, public health policy, and pandemic preparedness. Seasonal influenza and outbreak coronaviruses have the potential to peak much later than several other contributors to ILI, a dynamic that could be important for informing optimal vaccination timing.