Zhong Nanshan team released the latest epidemic research results: new crown prevention and control should be fast and accurate
Since the outbreak of novel coronavirus pneumonia, many countries in the world have implemented a series of epidemic prevention and control policies, such as closing schools, closing traffic, and eliminating public activities. These measures have played an important role in containing the epidemic situation, and also caused inconvenience to people’s daily life and caused some impact on the society economy.
Whether strict prevention and control measures are necessary and which are more effective? Recently, the latest research results jointly published by academician Zhong Nanshan’s team and Tencent in the international medical journal value in health have provided more accurate answers to these questions.
The research team uses big data and artificial intelligence technology, Based on the data of 8 major epidemic prevention and control measures in 145 countries and regions during the first wave of COVID-19 (the first half of 2020), a new counterfactual reasoning model was constructed to eliminate the confounding factors that affect the development of the epidemic.
With the help of big data analysis, the researchers conducted in-depth calculation and Analysis on the impact of the implementation time and time point of the measures on epidemic control. The results show that most epidemic prevention and control measures begin to take effect about 7 to 14 days after implementation, and the “regeneration number” RT reflecting the virus transmission ability decreases rapidly. When RT is equal to 1, it means that one case will infect one secondary case on average. Moreover, the prevention and control effect increases with the passage of time, reaching the maximum effect within 25 to 32 days, RT decreases by about 30%, and then the effect decreases gradually.
The study also pointed out that the more stringent and lasting the prevention and control measures implemented in the early stage of the epidemic (the period of slow growth of infection), the fewer the number of final infections will be. However, if the prevention and control measures are implemented in the middle and late stage of the epidemic (the period of rapid growth of infection), the results are just the opposite – the more stringent the prevention measures are, the longer the duration is, the more the number of final infections will increase.
Dr. Sun Jichao, the first author of the paper, pointed out that the reason for this result was not the failure of epidemic prevention and control measures, but the reverse causality: when the number of infected people increased rapidly and broke out, strict control measures began to be formulated, and little effect had been achieved at this time. This means that it is too late to “make up for the lost” and take action as soon as possible when there are signs of the epidemic in order to achieve the maximum effect.
In addition, the researchers used the counterfactual reasoning model to separately evaluate the effects of eight common prevention and control measures, so as to find out the most effective precise prevention and control measures to inhibit virus transmission. Eight prevention and control measures include closing schools, closing workplaces, canceling public activities, restricting crowd gathering, public transport control, home life suggestions, restricting domestic mobility and international travel.
Quantitative evaluation results of 8 control measures by counterfactual reasoning model
The results showed that among the prevention and control measures generally adopted by governments, the three epidemic prevention measures of canceling large-scale public activities, closing schools and closing workplaces had the most significant inhibitory effect on virus RT. The researchers speculate that these three measures are mandatory measures with higher implementation feasibility and compliance. Therefore, they are more likely to take effect in curbing the epidemic.
Novel coronavirus pneumonia was found to be more reliable evidence through the analysis of big data. It confirmed the quantitative impact of epidemic prevention and control measures on the suppression of new crown pneumonia, and suggested that more stringent epidemic prevention measures should be implemented in the early stage of disease to better curb the epidemic.