Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

Agent-based models (ABMs) are gaining importance over traditional epidemiological modeling due to advances in computing technology and catalyzed by the need for detailed epidemiological analysis of emergent diseases. Unfortunately, the advantages of ABMs are realized at the cost of significantly large execution times and high memory consumption for large-scale simulations. To address the memory issue, we designed and implemented an ABM using an innovative feature: the bitstring approach, in which the attributes of each agent are represented by an array of bits instead using traditional data structures. To cope with the high computational demands, we developed a parallel version of model aiming multicore CPUs and GPUs architectures using Thrust parallel algorithms library. The results of our model were validated comparing them with data of a spread of Influenza A in the Cascavel City, South Brazil, occurred in 2009. The model presented good qualitative results and an excellent performance on GPUs. The application of bitstring technique is proved to be relevant in economy of memory, allowing to store the same attributes using 41% less memory space and improving the data copy time between CPU and GPU up to 52%.