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?:abstract
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BACKGROUND SARS-CoV-2 and influenza are lipid-enveloped viruses with differential morbidity and mortality but shared modes of transmission. OBJECTIVE With a descriptive epidemiological framing, we assessed whether recent historical patterns of regional influenza burden are reflected in the observed heterogeneity in COVID-19 cases across regions of the world. METHODS Weekly surveillance data reported by the World Health Organization from January 2017-December 2019 for influenza and through October 31, 2020 for COVID-19 were used to assess seasonal and temporal trends for influenza and COVID-19 cases across the seven World Bank regions. RESULTS In regions with more pronounced influenza seasonality, COVID-19 epidemics have largely followed trends similar to those seen for influenza from 2017-2019. COVID-19 epidemics in countries across Europe and Central Asia and North America, have been marked by a first peak during the spring, followed by significant reductions in COVID-19 cases in the summer months, and a second wave in the fall. In Latin America and Caribbean, COVID-19 epidemics in several countries peaked in the summer, corresponding to months with highest influenza activity in the region. Countries from regions with less pronounced influenza activity including South Asia and Sub-Saharan Africa, showed more heterogeneity in COVID-19 epidemics seen to date. However, similarities in COVID-19 and influenza trends were evident within select countries irrespective of region. CONCLUSIONS Ecological consistency in COVID-19 trends seen to date with influenza trends suggest the potential for shared individual, structural, and environmental determinants of transmission. Using a descriptive epidemiological framework to assess shared regional trends for rapidly emerging respiratory pathogens with better-studied respiratory infections may provide further insights into the differential impacts of non-pharmacologic interventions and intersections with environmental conditions. Ultimately, forecasting trends and informing interventions for novel respiratory pathogens like COVID-19 should leverage epidemiologic patterns in the relative burden of past respiratory pathogens as prior information.
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