A simple model to assess Wuhan lock-down effect and region efforts during COVID-19 epidemic in China Mainland

Since COVID-19 emerged in early December, 2019 in Wuhan and swept across China Mainland, a series of large-scale public health interventions, especially Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement, have been taken by the government to control the epidemic. Based on Baidu Migration data and the confirmed cases data, we identified two key factors affecting the later ( e . g February 27, 2020) cumulative confirmed cases in non-Wuhan region ( y ). One is the sum travelers from Wuhan during January 20 to January 26 ( x ₁ ), which had higher infected probability but lower transmission ability because the human-to-human transmission risk of COVID-19 was confirmed and announced on January 20. The other is the “seed cases” from Wuhan before January 19, which had higher transmission ability and could be represented with the confirmed cases before January 29 ( x ₂ ) due to a mean 10-day delay between infection and detection. A simple yet effective regression model then was established as follow: y = 70.0916+0.0054× x ₁ +2.3455× x ₂ ( n = 44, R ² = 0.9330, P <10 ⁻⁷ ). Even the lock-down date only delay or in advance 3 days, the estimated confirmed cases by February 27 in non-Wuhan region will increase 35.21% or reduce 30.74% - 48.59%. Although the above interventions greatly reduced the human mobility, Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement do have a determining effect on the ongoing spread of COVID-19 across China Mainland. The strategy adopted by China has changed the fast-rising curve of newly diagnosed cases, the international community should learn from lessons of Wuhan and experience from China. Efforts of 29 Provinces and 44 prefecture-level cities against COVID-19 were also assessed preliminarily according to the interpretive model. Big data has played and will continue playing an important role in public health.