Global seasonal activities of respiratory syncytial virus before the COVID-19 pandemic: a systematic review (preprint)

Background: Varied seasonal patterns of respiratory syncytial virus (RSV) have been reported worldwide. We aimed to review the patterns of RSV activity globally before the COVID-19 pandemic and to explore factors potentially associated with RSV seasonality. Methods: We conducted a systematic review on articles identified in PubMed reporting RSV seasonality based on data collected before 1 January 2020. Information on the timing of the start, peak, and end of an RSV season, study location, study period, and details in study methods were extracted. RSV seasonal patterns were examined by geographic location, calendar month, analytic method and meteorological factors including temperature and absolute humidity. Correlation and regression analyses were conducted to explore the relationship between RSV seasonality and study methods and characteristics of study locations. Results: RSV seasons were reported in 209 articles published in 1973-2023 for 317 locations in 77 countries. Variations were identified in types of data, data collection and analytical methods across the studies. Regular RSV seasons were similarly reported in countries in temperate regions, with highly variable seasons identified in subtropical and tropical countries. Durations of RSV seasons were relatively longer in subtropical and tropical regions than from temperate regions. Longer durations of RSV seasons were associated with a higher daily average mean temperature and daily average mean absolute humidity. Conclusions: The global seasonal patterns of RSV provided important information for optimizing interventions against RSV infection. Heterogeneity in study methods highlighted the importance of developing and applying standardized approaches in RSV surveillance and data reporting.

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong (preprint)

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to predict the effective reproductive number "Rt", a measure of transmissibility. However, serial interval distributions may change as an epidemic progresses rather than remaining constant. Here we show that serial intervals in Hong Kong varied over time, closely associated with the temporal variation in COVID-19 case profiles and public health and social measures that were implemented in response to surges in community transmission. Quantification of the variation over time in serial intervals led to improved estimation of Rt, and provided additional insights into the impact of public health measures on transmission of infections. One-Sentence SummaryReal-time estimates of serial interval distributions can improve assessment of COVID-19 transmission dynamics and control.

Inferring time-varying generation time, serial interval and incubation period distributions for COVID-19 (preprint)

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We investigated incubation period and serial interval distributions in data on 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Inferring time-varying generation time, serial interval and incubation period distributions for COVID-19 (preprint)

Background The generation time distribution, reflecting the time between successive infections in transmission chains, is one of the fundamental epidemiological parameters for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution, reflecting the time between illness onsets of infector and infectee. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. Methods We analyzed data on observed incubation period and serial interval distributions in China, during January and February 2020, under different sampling approaches, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. Results We analyzed data on a total of 2989 confirmed cases for COVID-19 during January 1 to February 29, 2020 in Mainland China. During the study period, the empirical forward serial interval decreased from a mean of 8.90 days to 2.68 days. The estimated mean backward incubation period of infectors increased from 3.77 days to 9.61 days, and the mean forward incubation period of infectees also increased from 5.39 days to 7.21 days. The estimated mean forward generation time decreased from 7.27 days (95% confidence interval: 6.42, 8.07) to 4.21 days (95% confidence interval: 3.70, 4.74) days by January 29. We used simulations to examine the sensitivity of our modelling approach to a number of assumptions and alternative dynamics. Conclusions The proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.

Temporal dynamics in viral shedding and transmissibility of COVID-19 (preprint)

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.