Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study

Abstract

BackgroundThe coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. MethodsWe collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (R t ) at the provincial level. FindingsWe collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33-56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged >64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4•4 days (95% CI 0•0-14•0) for the period of Dec 24 to Jan 27, to 2•6 days (0•0-9•0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5•2 days (1•8-12•4) and the mean serial interval at 5•1 days (1•3-11•6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean Rt reaching peaks between 1•08 (95% CI 0•74-1•54) in Shenzhen city of Guangdong province and 1•71 (1•32-2•17) in Shandong province. In all the locations for which we had sufficient data coverage of Rt, Rt was estimated to be below the epidemic threshold (ie, <1) after Jan 30. Interpretation Our estimates of the incubation period and serial interval were similar, suggesting an early peak of infectiousness, with possible transmission before the onset of symptoms. Our results also indicate that, as the epidemic progressed, infectious individuals were isolated more quickly, thus shortening the window of transmission in the community. Overall, our findings indicate that strict containment measures, movement restrictions, and increased awareness of the population might have contributed to interrupt local transmission of SARS-CoV-2 outside Hubei province.

Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in U.S. Counties: The Significant Effects of Black Race and Temperature

“…Recent work has primarily focused on patient demographics, underlying health comorbidities, social disparities in healthcare access and quality, and environmental variables such as pollution to identify potential risk factors and vulnerable populations. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] However, while these previous studies have examined the effects of these domains on COVID-19 spread independently, some do not control for the potentially confounding interactions between variables. In this study, we sought to investigate COVID-19 prevalence and mortality in all U.S. counties through a more comprehensive framework that accounts for effects of county-level macroeconomic, demographic, environmental, health status, and healthcare access variables.…”

“…Developing new therapies, identifying risk factors, and minimizing spread through social distancing remain top priorities in the fight against COVID-19. 4,5 Previous studies have examined the effects of various risk factors on spread of COVID-19 including patient demographics, 6,7 social determinants of health, 8 environmental variables, [9][10][11][12][13][14][15] housing, 16,17 and underlying health conditions. [18][19][20] Identifying risk factors allows public health officials to determine populations at greater risk and development targeted public health interventions.…”

The first, holistic immunological model of COVID‐19: Implications for prevention, diagnosis, and public health measures

“…1 Viral shedding begins 2-3 days before symptom onset. 7 Infectivity seems to decline significantly already after 10 days from symptom onset, 8 but the virus can be detected for a median of 20 days, up to 37 days among survivors. 9 The cumulative amount of virus exposure acquired by the patient at the start of infection cannot be measured.…”

The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China

“…3 The results of such analyses may be difficult to generalize due to the context-specific assumptions made and it can be too slow to meet a pressing need during an epidemic. 5,[19][20][21][22] Thus, an easy-to use tool for monitoring the COVID-19 epidemic is so important in practice.…”