COVID-19 is erupting globally, and Wuhan successfully controlled it within a month. Infections arose from infectious persons outside hospitals. After data revision, data-based and model-based analyses were implemented, and the conclusions are as follows. The incubation period of most infected people may be 6-7 days. The number of infectious persons outside hospitals in Wuhan on January 20, 2020 was about 10000 and reached more than 20000 on the day of Lockdown; it exceeded 72000 on February 4. Both data-based and model-based analyses gave out the evolution of the reproduction number, which was over 2.5 in early January, went down to 1.62 in late January and 1.20 in early February, with a sudden drop to less than 0.5 due to the strict Stay-at-home management after February 11. Strategies of Stay-at-home, Safe-protective measures, and Ark hospitals were the main contributions to control COVID-19 in Wuhan. In Wuhan, 2 inflection points of COVID-19, exactly correspond to February 5 and February 15, the 2 days when Ark hospitals were introduced, and the complete implementation of Stay-at-home. Based on the expression of the reproduction number, group immunity is also discussed. It shows that only when the group immunization rate is over 75% can COVID-19 be under control; group immunity would be full infection and the total deaths will be 220000 for a city as big as Wuhan. Sensitivity analysis suggests that 30% of people staying at home in combination with better behavior changes, such as social-distancing and frequent handwashing, can effectively contain COVID-19. However, only when this proportion is over 60% can the controlled effect and efficiency like Wuhan be obtained.

Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.