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BackgroundThe 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. MethodsWe 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. ResultsWe 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. ConclusionsThe proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.
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A fast-spreading SARS-CoV-2 variant identified in the United Kingdom in December 2020 has raised international alarm. We estimate that, in all 15 countries analyzed, there is at least a 50% chance the variant was imported by travelers from the United Kingdom by December 7th.
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BackgroundWhen a new infectious disease emerges, appropriate case definitions are important for clinical diagnosis and also for public health surveillance. Tracking case numbers over time allows us to determine speed of spread and the effectiveness of interventions. Changing case definitions during an epidemic can affect these inferences. MethodsWe examined changes in the case definition for COVID-19 in mainland China during the first epidemic wave. We used simple models assuming exponential growth and then exponential decay to estimate how changes in the case definitions affected the numbers of cases reported each day. We then inferred how the epidemic curve would have appeared if the same case definition had been used throughout the epidemic. FindingsFrom January through to early March 2020, seven versions of the case definition for COVID-19 were issued by the National Health Commission in China. As of February 20, there were 55,508 confirmed cases reported in mainland China. We estimated that when the case definitions were changed from version 1 to 2, version 2 to 4 and version 4 to 5, the proportion of infections being detected as cases were increased by 7.1-fold (95% credible interval (CI): 4.8, 10.9), 2.8-fold (95% CI: 1.9, 4.2) and 4.2-fold (95% CI: 2.6, 7.3) respectively. If the fifth version of the case definition had been applied throughout the outbreak, we estimated that by February 20 there would have been 232,000 (95% CI: 161,000, 359,000) confirmed cases. InterpretationThe case definition was initially narrow, but was gradually broadened to allow detection of more cases as knowledge increased, particularly milder cases and those without epidemiological links to Wuhan or other known cases. This should be taken into account when making inferences on epidemic growth rates and doubling times, and therefore on the reproductive number, to avoid bias. FundingCommissioned grant from the Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region.
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BackgroundA range of public health measures have been implemented to delay and reduce local transmission of COVID-19 in Hong Kong, and there have been major changes in behaviours of the general public. We examined the effect of these interventions and behavioral changes on the incidence of COVID-19 as well as on influenza virus infections which may share some aspects of transmission dynamics with COVID-19. MethodsWe reviewed policy interventions and measured changes in population behaviours through two telephone surveys, on January 20-23 and February 11-14. We analysed data on laboratory-confirmed COVID-19 cases, influenza surveillance data in outpatients of all ages, and influenza hospitalisations in children. We estimated the daily effective reproduction number (Rt), for COVID-19 and influenza A(H1N1). FindingsCOVID-19 transmissibility has remained at or below 1, indicating successful containment to date. Influenza transmission declined substantially after the implementation of social distancing measures and changes in population behaviours in late January, with a 44% (95% confidence interval, CI: 34% to 53%) reduction in transmissibility in the community, and a 33% (95% CI: 24% to 43%) reduction in transmissibility based on paediatric hospitalization rates. In the two surveys we estimated that 74.5% and 97.5% of the general adult population wore masks when going out, and 61.3% and 90.2% avoided going to crowded places, respectively. ImplicationsContainment measures, social distancing measures and changes in population behaviour have successfully prevented spread of COVID-19. The social distancing measures and behavioural changes led to a substantial reduction in influenza transmission in early February 2020. However, it may be challenging to avoid fatigue and sustain these measures and population behaviours as COVID-19 continues to spread globally. FundingHealth and Medical Research Fund, Hong Kong