Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study

Background The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. Methods For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. Findings We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17.3% (95% CI 13.3-21.4) to 40.6% (35.2-45.9) and attack rate by 5.1% (1.5-7.2) to 24.8% (20.8-27.5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. Interpretation Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

RiskEstim: A Software Package to Quantify COVID-19 Importation Risk

We present an R package developed to quantify coronavirus disease 2019 (COVID-19) importation risk. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is urgent and imperative to trigger public health responses, especially in the early stages of the COVID-19 pandemic and emergence of new SARS-CoV-2 variants. We provide a general modeling framework to estimate COVID-19 importation risk using estimated pre-symptomatic prevalence of infection and air traffic data from the multi-origin places. We use Hong Kong as a case study to illustrate how our modeling framework can estimate the COVID-19 importation risk into Hong Kong from cities in Mainland China in real time. This R package can be used as a complementary component of the pandemic surveillance system to monitor spread in the next pandemic.

COVID-19 Importation Risk From Olympic Athletes Prior to the Tokyo 2020 Olympics

The COVID-19 pandemic delayed the Tokyo 2020 Olympics for 1 year and sparked an unprecedented outbreak in Japan in early July 2021 due to the relaxation of social distancing measures for foreign arrivals. Approximately 11,000 athletes from 205 countries would gather at the Tokyo Olympics held from July 23 through August 8, 2021. Based on the prevalence of infection in different source locations and athlete numbers, we estimated that seven countries would introduce least one infection of COVID-19 to Tokyo and at most eleven unidentified infections after the three requested COVID-19 tests.</p>