U.S.-China University Study: Interventions reduce the number of people infected in Wuhan by 96%.
Last Update: 2020-11-30
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, a modelling study published online by Nature, a leading academic journal, showed that a multi-pronged approach to public health interventions from January to March 2020 reduced the cumulative number of infections in Wuhan by 96.0% as of March 8. At the same time, between January and March, up to 87 per cent of COVID-19 cases may not be detected, and undetected or confirmed cases include asymptomatic, pre-symptomatic or mildly infected persons. These groups may play an important role in the spread of the disease, and if the restrictions are lifted prematurely, they can trigger a rebound in the outbreak.
The study was jointly conducted by a team of researchers from Hua zhong university of science and technology and Harvard University in the United States, entitled "Reconstruction of the full transmission dynamics of COVID-19 in Wuhan (Wuhan COVID-19 outbreak full transmission dynamic reconstruction)," by Professor Wang Chaolong of the School of Epidemiology and Health Statistics of Hua zhong University of Science and Technology and tenured professor in the Department of Biostatology at Harvard University, and
Lin Xihong of the National School of Medicine.
coVID-19 infected with asymptomatic, pre-symptomatic or mild illness are thought to play an important role in the rapid spread of the disease, which is generally difficult to detect and isolate. The authors believe that full dynamic reconstruction of the outbreak will help to better understand the proportion and impact of UNdiagnosed infections of COVID-19, which may be of reference value for the prevention and control of the outbreak.
Wang Chaolong and others studied the propagation dynamics of the COVID-19 outbreak in Wuhan and assessed the effectiveness of interventions using data from 32,583 laboratory-confirmed cases from December 8, 2019 to March 8, 2020. They used the data to simulate the outbreak, which began on January 1, 2020, and divided it into five periods based on key events and interventions, such as the Lunar New Year and centralized quarantine.
by the team showed that the initial propagation rate was very high, with the number of basic regenerations (R0) in the first phase at about 3.54, dropping to about 0.28 in the final stages of the study. The results show that the gradual public health interventions from January to March 2020 were multi-pronged, reducing the cumulative number of infections in Wuhan by 96.0% as of March 8.
team matched their models with epidemiological data to prove that there may have been a large number of unsolt cases in Wuhan. During the period under their study, an estimated 87 per cent of infections were not detected, even under the
scenario of the initial detection of all cases, with a lower limit of 53 per cent.
public health interventions, such as quarantine and social alienation, appear to be an effective means of stopping the spread of undiagnosed cases and controlling outbreaks, according to the team. They also stressed the importance of conducting surveys such as serological studies to confirm these estimates.
team also used fitted models from the data to predict the probability of a second wave of outbreaks. Assuming that all restrictions are lifted on the 14th day after the first notification of no new cases, the probability of an outbreak rebound is expected to be very high, up to 97 per cent, and the source of infection is mainly from unexcovered cases of mild or asymptomatic disease. A surge in the number of cases is expected in the 34th day after the lifting of the ban. Another, more rigorous scenario assumes that all restrictions are lifted without new cases for 14 consecutive days, when the probability of a rebound falls to 32 per cent and the surge in the number of cases is extended to the 42nd day after the lifting of the ban.
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