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.

Epidemiology of infections with SARS-CoV-2 Omicron BA.2 variant in Hong Kong, January-March 2022 (preprint)

Hong Kong reported 12,631 confirmed COVID-19 cases and 213 deaths in the first two years of the pandemic but experienced a major wave predominantly of Omicron BA.2.2 in early 2022 with over 1.1 million reported SARS-CoV-2 infections and more than 7900 deaths. Our data indicated a shorter incubation period, serial interval, and generation time of infections with Omicron than other SARS-CoV-2 variants. Omicron BA.2.2 cases without a complete primary vaccination series appeared to face a similar fatality risk to those infected in earlier waves with the ancestral strain.

Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong (preprint)

Transmission heterogeneity is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics, though previous efforts to estimate how heterogeneity changes over time are limited. Using contact tracing data, we compared the epidemiology of SARS and COVID-19 infection in Hong Kong in 2003 and 2020-21 and estimated time-varying transmission heterogeneity (kt) by fitting negative binomial models to offspring distributions generated across variable observation windows. kt fluctuated over time for both COVID-19 and SARS on a continuous scale though SARS exhibited significantly greater (p < 0.001) heterogeneity compared to COVID-19 overall and in-time. For COVID-19, kt declined over time and was significantly associated with increasingly stringent non-pharmaceutical interventions though similar evidence for SARS was inconclusive. Underdetection of sporadic COVID-19 cases led to a moderate overestimation of kt, indicating COVID-19 heterogeneity of could be greater than observed. Time-varying or real-time estimates of transmission heterogeneity could become a critical indicator for epidemic intelligence in the future.

Analysis of factors associated early diagnosis in coronavirus disease 2019 (COVID-19) (preprint)

Background The pandemic of coronavirus disease 2019 (COVID-19) has become the first concern in international affairs as the novel coronavirus (SARS-CoV-2) is spreading all over the world at a terrific speed. The accuracy of early diagnosis is critical in the control of the spread of the virus. Although the real-time RT-PCR detection of the virus nucleic acid is the current golden diagnostic standard, it has high false negative rate when only apply single test. Objective Summarize the baseline characteristics and laboratory examination results of hospitalized COVID-19 patients. Analyze the factors that could interfere with the early diagnosis quantitatively to support the timely confirmation of the disease. Methods All suspected patients with COVID-19 were included in our study until Feb 9th, 2020. The last day of follow-up was Mar 20th, 2020. Throat swab real-time RT-PCR test was used to confirm SARS-CoV-2 infection. The difference between the epidemiological profile and first laboratory examination results of COVID-19 patients and non-COVID-19 patients were compared and analyzed by multiple logistic regression. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to assess the potential diagnostic value in factors, which had statistical differences in regression analysis. Results In total, 315 hospitalized patients were included. Among them, 108 were confirmed as COVID-19 patients and 207 were non-COVID-19 patients. Two groups of patients have significance in comparing age, contact history, leukocyte count, lymphocyte count, C-reactive protein, erythrocyte sedimentation rate (p<0.10). Multiple logistic regression analysis showed age, contact history and decreasing lymphocyte count could be used as individual factor that has diagnostic value (p<0.05). The AUC of first RT-PCR test was 0.84 (95% CI 0.73-0.89), AUC of cumulative two times of RT-PCR tests was 0.92 (95% CI 0.88-0.96) and 0.96 (95% CI 0.93-0.99) for cumulative three times of RT-PCR tests. Ninety-six patients showed typical pneumonia radiological features in first CT scan, AUC was 0.74 (95% CI 0.60-0.73). The AUC of patients age, contact history with confirmed people and the decreased lymphocytes were 0.66 (95% CI 0.60-0.73), 0.67 (95% CI 0.61-0.73), 0.62 (95% CI 0.56-0.69), respectively. Taking chest CT scan diagnosis together with patients age and decreasing lymphocytes, AUC would be 0.86 (95% CI 0.82-0.90). The age threshold to predict COVID-19 was 41.5 years, with a diagnostic sensitivity of 0.70 (95% CI 0.61-0.79) and a specificity of 0.59 (95% CI 0.52-0.66). Positive and negative likelihood ratios were 1.71 and 0.50, respectively. Threshold of lymphocyte count to diagnose COVID-19 was 1.53x109/L, with a diagnostic sensitivity of 0.82 (95% CI 0.73-0.88) and a specificity of 0.50 (95% CI 0.43-0.57). Positive and negative likelihood ratios were 1.64 and 0.37, respectively. Conclusion Single RT-PCR test has relatively high false negative rate. When first RT-PCR test show negative result in suspected patients, the chest CT scan, contact history, age and lymphocyte count should be used combinedly to assess the possibility of SARS-CoV-2 infection.

Estimating the generation interval for COVID-19 based on symptom onset data (preprint)

Background: Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions: Estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.

Transmission interval estimates suggest pre-symptomatic spread of COVID-19 (preprint)

Background: As the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission. Methods: We collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations. Results: The mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2)days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65,2.09) secondary cases per infective. Conclusions: Estimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.