ABSTRACT
BackgroundThe 2019 novel Coronavirus (COVID-19) emerged in Wuhan, China in December 2019 and has been spreading rapidly in China. Decisions about its pandemic threat and the appropriate level of public health response depend heavily on estimates of its basic reproduction number and assessments of interventions conducted in the early stages of the epidemic. MethodsWe conducted a mathematical modeling study using five independent methods to assess the basic reproduction number (R0) of COVID-19, using data on confirmed cases obtained from the China National Health Commission for the period 10th January - 8th February. We analyzed the data for the period before the closure of Wuhan city (10th January - 23rd January) and the post-closure period (23rd January - 8th February) and for the whole period, to assess both the epidemic risk of the virus and the effectiveness of the closure of Wuhan city on spread of COVID-19. FindingsBefore the closure of Wuhan city the basic reproduction number of COVID-19 was 4.38 (95% CI: 3.63 - 5.13), dropping to 3.41 (95% CI: 3.16 - 3.65) after the closure of Wuhan city. Over the entire epidemic period COVID-19 had a basic reproduction number of 3.39 (95% CI: 3.09 - 3.70), indicating it has a very high transmissibility. InterpretationCOVID-19 is a highly transmissible virus with a very high risk of epidemic outbreak once it emerges in metropolitan areas. The closure of Wuhan city was effective in reducing the severity of the epidemic, but even after closure of the city and the subsequent expansion of that closure to other parts of Hubei the virus remained extremely infectious. Emergency planners in other cities should consider this high infectiousness when considering responses to this virus. FundingNational Natural Science Foundation of China, China Medical Board, National Science and Technology Major Project of China
ABSTRACT
Objective@#The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (R0) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention.@*Method@#The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R0.@*Result@#Among the four methods, the EG method fitted the data best. The estimated R0 was 3.49 (95% CI: 3.42-3.58) by using EG method. The R0 was estimated to be 2.95 (95%CI: 2.86-3.03) after taking control measures.@*Conclusion@#In the early stage of the epidemic, it is appropriate to estimate R0 using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.
ABSTRACT
Objective The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number ( R 0 ) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. Method The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R 0 . Result Among the four methods, the EG method fitted the data best. The estimated R 0 was 3.49 (95% CI : 3.42-3.58) by using EG method. The R 0 was estimated to be 2.95 (95% CI : 2.86-3.03) after taking control measures. Conclusion In the early stage of the epidemic, it is appropriate to estimate R 0 using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.
ABSTRACT
The Global Burden of Disease (GBD) study has been instrumental in guiding global health policy development since the early 1990s. The GBD 2010 project provided rich information about the key causes of mortality, disability-adjusted life years, and their associated risk factors in Japan and provided a unique opportunity to incorporate these data into health planning. As part of the latest update of this project, GBD 2013, the Japanese GBD collaborators plan to update and refine the available burden of disease data by incorporating sub-national estimates of the burden of disease at the prefectural level. These estimates will provide health planners and policy makers at both the national and prefectural level with new, more refined tools to adapt local public health initiatives to meet the health needs of local populations. Moreover, they will enable the Japanese health system to better respond to the unique challenges in their rapidly aging population and as a complex combination of non-communicable disease risk factors begin to dominate the policy agenda. Regional collaborations will enable nations to learn from the experiences of other nations that may be at different stages of the epidemiological transition and have different exposure profiles and associated health effects. Such analyses and improvements in the data collection systems will further improve the health of the Japanese, maintain Japan's excellent record of health equity, and provide a better understanding of the direction of health policy in the region.
