s u m m a r yObjectives: To improve our understanding of the global epidemiology of common respiratory viruses by analysing their contemporaneous incidence at multiple sites. Methods: 2010-2015 incidence data for influenza A (IAV), influenza B (IBV), respiratory syncytial (RSV) and parainfluenza (PIV) virus infections were collected from 18 sites (14 countries), consisting of local ( n = 6), regional ( n = 9) and national ( n = 3) laboratories using molecular diagnostic methods. Each site submitted monthly virus incidence data, together with details of their patient populations tested and diagnostic assays used.Results: For the Northern Hemisphere temperate countries, the IAV, IBV and RSV incidence peaks were 2-6 months out of phase with those in the Southern Hemisphere, with IAV having a sharp out-of-phase difference at 6 months, whereas IBV and RSV showed more variable out-of-phase differences of 2-6 months. The tropical sites Singapore and Kuala Lumpur showed fluctuating incidence of these viruses throughout the year, whereas subtropical sites such as Hong Kong, Brisbane and Sydney showed distinctive biannual peaks for IAV but not for RSV and PIV. Conclusions: There was a notable pattern of synchrony of IAV, IBV and RSV incidence peaks globally, and within countries with multiple sampling sites (Canada, UK, Australia), despite significant distances between these sites.

Detecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference. The local likelihood method is adopted to estimate the spatial relationship, while the smoothing method is employed to estimate the temporal variation. We present the construction and implementation of GWTCLR and the study of the asymptotic properties of the proposed estimator. Simulation studies were conducted to evaluate the robustness of the proposed model. GWTCLR was applied on real epidemiologic data to study the climatic determinants of human seasonal influenza epidemics. Our method obtained results largely consistent with previous studies but also revealed certain spatial and temporal varying patterns that were unobservable by previous models and methods.

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Highlights We applied a Bayesian SEIR epidemiological model to infer the local transmission dynamics of the COVID-19 in nine regions of England. The basic reproduction number ( R 0 ) for each region was estimated, and found to be significantly correlated with the population size of each region. We estimated the temporally varying effective reproduction number ( R t ) and showed that the control measures were effective. Based on data before June 2020, our model predicted that several regions have the possibility to experience a second wave of outbreaks.

Comparative seasonalities of influenza A, B and 'common cold' coronaviruses-setting the scene for SARS-CoV-2 infections and possible unexpected host immune interactions