5 research outputs found
Spatio-temporal propagation of COVID-19 pandemics
The new coronavirus known as COVID-19 is spread world-wide since December
2019. Without any vaccination or medicine, the means of controlling it are
limited to quarantine and social distancing. Here we study the spatio-temporal
propagation of the first wave of the COVID-19 virus in China and compare it to
other global locations. We provide a comprehensive picture of the spatial
propagation from Hubei to other provinces in China in terms of distance,
population size, and human mobility and their scaling relations. Since strict
quarantine has been usually applied between cities, more insight about the
temporal evolution of the disease can be obtained by analyzing the epidemic
within cities, especially the time evolution of the infection, death, and
recovery rates which affected by policies. We study and compare the infection
rate in different cities in China and provinces in Italy and find that the
disease spread is characterized by a two-stages process. At early times, at
order of few days, the infection rate is close to a constant probably due to
the lack of means to detect infected individuals before infection symptoms are
observed. Then at later times it decays approximately exponentially due to
quarantines. The time evolution of the death and recovery rates also
distinguish between these two stages and reflect the health system situation
which could be overloaded
Confidence Backup Updates for Aggregating MDP State Values in Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) algorithms estimate the value of MDP states based on rewards received by performing multiple random simulations. MCTS algorithms can use different strategies to aggregate these rewards and provide an estimation for the states’ values. The most common aggregation method is to store the mean reward of all simulations. Another common approach stores the best observed reward from each state. Both of these methods have complementary benefits and drawbacks. In this paper, we show that both of these methods are biased estimators for the real expected value of MDP states. We propose an hybrid approach that uses the best reward for states with low noise, and otherwise uses the mean. Experimental results on the Sailing MDP domain show that our method has a considerable advantage when the rewards are drawn from a noisy distribution
Erratum: Spatio-temporal propagation of COVID-19 pandemics
Original article: EPL, 131 (2020) 5800