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We report temporal patterns of viral shedding in 94 laboratory-confirmed COVID-19 patients and modelled COVID-19 infectiousness profile from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% of transmission could occur before first symptoms of the index. Disease control measures should be adjusted to account for probable substantial pre-symptomatic transmission.

Background The estimation of influenza-associated excess mortality in countries can help to improve estimates of the global mortality burden attributable to influenza virus infections. We did a study to estimate the influenza-associated excess respiratory mortality in mainland China for the 2010-11 through 2014-15 seasons. MethodsWe obtained provincial weekly influenza surveillance data and population mortality data for 161 disease surveillance points in 31 provinces in mainland China from the Chinese Center for Disease Control and Prevention for the years 2005-15. Disease surveillance points with an annual average mortality rate of less than 0•4% between 2005 and 2015 or an annual mortality rate of less than 0•3% in any given years were excluded. We extracted data for respiratory deaths based on codes J00-J99 under the tenth edition of the International Classification of Diseases. Data on respiratory mortality and population were stratified by age group (age <60 years and ≥60 years) and aggregated by province. The overall annual population data of each province and national annual respiratory mortality data were compiled from the China Statistical Yearbook. Influenza surveillance data on weekly proportion of samples testing positive for influenza virus by type or subtype for 31 provinces were extracted from the National Sentinel Hospitalbased Influenza Surveillance Network. We estimated influenza-associated excess respiratory mortality rates between the 2010-11 and 2014-15 seasons for 22 provinces with valid data in the country using linear regression models. Extrapolation of excess respiratory mortality rates was done using random-effect meta-regression models for nine provinces without valid data for a direct estimation of the rates. Findings We fitted the linear regression model with the data from 22 of 31 provinces in mainland China, representing 83•0% of the total population. We estimated that an annual mean of 88 100 (95% CI 84 200-92 000) influenza-associated excess respiratory deaths occurred in China in the 5 years studied, corresponding to 8•2% (95% CI 7•9-8•6) of respiratory deaths. The mean excess respiratory mortality rates per 100 000 person-seasons for influenza A(H1N1)pdm09, A(H3N2), and B viruses were 1•6 (95% CI 1•5-1•7), 2•6 (2•4-2•8), and 2•3 (2•1-2•5), respectively. Estimated excess respiratory mortality rates per 100 000 person-seasons were 1•5 (95% CI 1•1-1•9) for individuals younger than 60 years and 38•5 (36•8-40•2) for individuals aged 60 years or older. Approximately 71 000 (95% CI 67 800-74 100) influenzaassociated excess respiratory deaths occurred in individuals aged 60 years or older, corresponding to 80% of such deaths. Interpretation Influenza was associated with substantial excess respiratory mortality in China between 2010-11 and 2014-15 seasons, especially in older adults aged at least 60 years. Continuous and high-quality surveillance data across China are needed to improve the estimation of the disease burden attributable to influenza and the best public health interventions...

and colleagues at ETH Zurich very helpfully alerted us to a syntactical error in our original code, specifically that the likelihood as we had originally specified gave rise to zero probability for two transmission pairs with the most negative serial intervals. Following their lead, we also applied a normalization factor in the likelihood to account for the uncertainty in the symptom-onset dates of the index cases. However, assuming a uniform distribution, the likelihood would differ only by a multiplicative constant and would give the same estimates. We used the bootstrap method to estimate the 95% confidence intervals (CIs).

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Background Human influenza virus infections cause a considerable burden of morbidity and mortality worldwide each year. Understanding regional influenza‐associated outpatient burden is crucial for formulating control strategies against influenza viruses. Methods We extracted the national sentinel surveillance data on outpatient visits due to influenza‐like‐illness (ILI) and virological confirmation of sentinel specimens from 30 provinces of China from 2006 to 2015. Generalized additive regression models were fitted to estimate influenza‐associated excess ILI outpatient burden for each individual province, accounting for seasonal baselines and meteorological factors. Results Influenza was associated with an average of 2.5 excess ILI consultations per 1000 person‐years (py) in 30 provinces of China each year from 2006 to 2015. Influenza A(H1N1)pdm09 led to a higher number of influenza‐associated ILI consultations in 2009 across all provinces compared with other years. The excess ILI burden was 4.5 per 1000 py among children aged below 15 years old, substantially higher than that in adults. Conclusions Human influenza viruses caused considerable impact on population morbidity, with a consequent healthcare and economic burden. This study provided the evidence for planning of vaccination programs in China and a framework to estimate burden of influenza‐associated outpatient consultations.

We used national sentinel surveillance data in China for 2005–2016 to examine the lineage-specific epidemiology of influenza B. Influenza B viruses circulated every year with relatively lower activity than influenza A. B/Yamagata was more frequently detected in adults than in children.

Background COVID-19 has caused a heavy disease burden globally, but impact of process and the timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Since infection times are typically unobserved, there are relatively few estimates of the generation time distribution. Methods We developed a statistical framework to jointly estimate the generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of pre-symptomatic transmission, and basic reproduction number () for COVID-19. Results The estimated mean incubation period was 4.8 days (95% confidence interval (CI), 4.1-5.6), and the mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, and insensitive to the width of exposure windows. Conclusions Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. can be biased when it is derived based on serial interval as the proxy of generation time.