Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

Memanti­nium chloride 0.1-hydrate

The crystal structure of the title compound, C12H22N+·Cl−·0.1H2O, consists of (3,5-dimethyl-1-adamantyl)ammonium chloride (memanti­nium chloride) and uncoordinated water mol­ecules. The four six-membered rings of the memanti­nium cation assume typical chair conformations. The Cl− counter-anion links with the memanti­nium cation via N—H⋯Cl hydrogen bonding, forming channels where the disordered crystal water molecules are located. The O atom of the water mol­ecule is located on a threefold rotation axis, its two H atoms symmetrically distributed over six sites; the water mol­ecule links with the Cl− anions via O—H⋯Cl hydrogen bonding