On the optimal presence strategies for workplace during pandemics: A COVID-19 inspired probabilistic model.

During pandemics like COVID-19, both the quality and quantity of services offered by businesses and organizations have been severely impacted. They often have applied a hybrid home office setup to overcome this problem, although in some situations, working from home lowers employee productivity. So, increasing the rate of presence in the office is frequently desired from the manager's standpoint. On the other hand, as the virus spreads through interpersonal contact, the risk of infection increases when workplace occupancy rises. Motivated by this trade-off, in this paper, we model this problem as a bi-objective optimization problem and propose a practical approach to find the trade-off solutions. We present a new probabilistic framework to compute the expected number of infected employees for a setting of the influential parameters, such as the incidence level in the neighborhood of the company, transmission rate of the virus, number of employees, rate of vaccination, testing frequency, and rate of contacts among the employees. The results show a wide range of trade-offs between the expected number of infections and productivity, for example, from 1 to 6 weekly infections in 100 employees and a productivity level of 65% to 85%. This depends on the configuration of influential parameters and the occupancy level. We implement the model and the algorithm and perform several experiments with different settings of the parameters. Moreover, we developed an online application based on the result in this paper which can be used as a recommender for the optimal rate of occupancy in companies/workplaces.

Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals’ infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease.