B
ackground and Objective COVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an efficient approach to manage the pandemic in the long term, without straining the healthcare system and economy.
In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under different conditions, using a probabilistic cellular automata configuration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules.
M
ethods
The cellular automata, initially configured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different infection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and susceptible individuals. A transmittable and transition neighbourhoods are defined to identify the most probable individuals infected from a single host in a time step.
R
esults
The model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match.
C
onclusions
The study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.
The performance of a proton exchange membrane (PEM) fuel cell strongly depends on the nature of reactant distribution and the effectiveness of liquid water removal. In this work, three different configurations of a mixed flow distributor are studied analytically and numerically to find out the effect of nonuniform under-rib convection on reactant and liquid water distribution in the cell. In a mixed flow distributor, the rate of under-rib convection is found to be different under each rib in the same flow sector which results in different rates of removal of liquid water. This helps to retain some water to hydrate the membrane, whereas the excess is removed to avoid flooding. It is found that under-rib convection aids to get better reactant distribution, reduces pressure drop, and provides better control over liquid water removal which is helpful in developing efficient water management strategies for PEM fuel cells.
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