Assessing the interactions between COVID-19 and influenza in the United States

Casey M Zipfel; Shweta Bansal

The model covariates included COVID-19 risk perception, influenza risk perception, humidity (shown to be an important driver of influenza dynamics [15, 16] ), and strain dynamics. To measure COVID-19 risk perception, we collected the weekly mean number of Google Trends searches of "coronavirus", "COVID", and "COVID-19" at the national and state level [17, 18] . To represent ILI risk perception, we collected the weekly mean number of Google Trends searches of "influenza" and "flu". To measure relative humidity, we collected data from the US Local Climatological Database, which provides summaries of climatological conditions from airport and other prominent weather stations managed by the National Weather Service, Federal Aviation Administration and Department of Defense [19] . For each state, we randomly chose a weather station that covered the appropriate time period, as a marker for humidity levels in that state. To measure strain dynamics, we used the National Respiratory and Enteric Virus Surveillance System (NREVSS) virological surveillance data to identify the proportion of respiratory specimens tested for influenza that tested positive for influenza A or influenza B [11] .