Predicting mobile apps spread: An epidemiological random network modeling approach

[EN] The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an appAlegre-Sanahuja, J.; Cortés, J.; Villanueva Micó, RJ.; Santonja, F. (2017). Predicting mobile apps spread: An epidemiological random network modeling approach. Transactions of the Society for Computer Simulation. 94(2):123-130. https://doi.org/10.1177/0037549717712600S12313094

Validity and Absolute Reliability of the Cobb Angle in Idiopathic Scoliosis with TraumaMeter Software

The Cobb angle value is a critical parameter for evaluating adolescent idiopathic scoliosis (AIS) patients. This study aimed to evaluate a software’s validity and absolute reliability to determine the Cobb angle in AIS digital X-rays, with two different degrees of experienced observers. Four experts and four novice evaluators measured 35 scoliotic curves with the software on three separate occasions, one month apart. The observers re-measured the same radiographic studies on three separate occasions three months later but on conventional X-ray films. The differences between the mean bias errors (MBE) within the experience groups were statistically significant between the experts (software) and novices (manual) (p < 0.001) and between the novices (software) and novices (manual) (p = 0.005). When measured with the software, the intra-group error in the expert group was MBE = 1.71 ± 0.61° and the intraclass correlation coefficient (ICC (2,1)) = 0.986, and in the novice group, MBE = 1.9 ± 0.67° and ICC (2,1) = 0.97. There was almost a perfect concordance among the two measurement methods, ICC (2,1) = 0.998 and minimum detectable change (MCD95) < 0.4°. Control of the intrinsic error sources enabled obtaining inter- and intra-observer MDC95 < 0.5° in the two experience groups and with the two measurement methods. The computer-aided software TraumaMeter increases the validity and reliability of Cobb angle measurements concerning manual measurement