Exploring individuals’ effective preventive measures against epidemics through reinforcement learning

Individuals’ preventive measures,as an effective way to suppress epidemic transmission and to protect themselves from infection,have attracted much academic concern,especially during the COVID-19 pandemic.In this paper,a reinforcement learning-based model is proposed to explore individuals’ effective preventive measures against epidemics.Through extensive simulations,we find that the cost of preventive measures influences the epidemic transmission process significantly.The infection scale increases as the cost of preventive measures grows,which means that the government needs to provide preventive measures with low cost to suppress the epidemic transmission.In addition,the effective preventive measures vary from individual to individual according to the social contacts.Individuals who contact with others frequently in daily life are highly recommended to take strict preventive measures to protect themselves from infection,while those who have little social contacts do not need to take any measures considering the inevitable cost.Our research contributes to exploring the effective measures for individuals,which can provide the government and individuals useful suggestions in response to epidemics.

Simulation of proton–neutron collisions in inverse kinematics and its possible application

Neutrons have played a vital role in many nuclear physics fields.In some cases,the inverse kinematics of neutrons colliding with other nuclei are also worth studying.In this study,the inverse kinematics of thermal neutrons colliding with high-energy protons is simulated by using the Monte Carlo method.Thermal neutrons are taken as target particles,whereas protons are incident particles.The simulation implies that,after collision,the energy of the output neutron at 0°equals the energy of the incident proton.A possible application of the result is proposed that might yield single-energy neutrons.Some key parameters of the conceptual design were evaluated,demonstrating that the design may reach high-neutron-energy resolution.

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks

Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents’ preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between layers. Based on the above-mentioned analysis, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection,infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travels of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This study probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.