Developing multi-virus transmission-dynamic models and mixing matrices using insights from temporal variations in infection and contact patterns before, during, and after the COVID-19 pandemic: Learning from the past to prepare for the future

The COVID-19 pandemic revealed the critical role infectious disease (ID) mathematical modeling can play to help inform policy decisions to improve population health. It also highlighted key limitations that hindered model projections. In addition to limited epidemiological/vaccination data, there were no Canada-specific social contact mixing data available, apart from our CONNECT study, and there was a lack of complex/flexible ID models that could rapidly be adapted to include new viruses to identify optimal targeted public health interventions. More than ever, valid ID modeling tools will be needed to inform public health, with the 1) rise in vaccine-preventable infectious diseases globally (e.g. measles), 2) added burden of COVID-19 to other IDs, which has a major impact on hospital capacity, and 3) risk of viruses with pandemic potential (e.g. H5N1). The magnitude of outbreaks and risk of pandemics will increase by the sustained rise in vaccine hesitancy and budget cuts in public health globally. It is imperative that we learn from the past pandemic, so that we are better prepared for the future. By combining our unique social contact study (CONNECT) and epidemiological data with state-of-the-art ID modeling, this project will contribute to methodological development, answer key public health questions in ID control/prevention, and increase pandemic preparedness in Canada. First, based on the only Canadian study on social contacts before and during the pandemic (CONNECT), we will make available mixing matrices to modelers across Canada. Second, using insights from temporal variations in infection and contact patterns before, during, and after the pandemic, we will develop two complementary multi-virus models that can simultaneously project the dynamics of COVID-19, influenza, and RSV and examine the population-level impact of interventions. Finally, the multi-virus models will be developed and made available so they can be rapidly adapted for pandemic response.