Snow's case revisited: New tool in geographic profiling of epidemiology

AbstractGeographic Profiling technique is used to find the origin of a series of crimes. The method was recently extended to other fields. One of the best renowned data in epidemiology is that by John Snow during an outburst of cholera in London. We wrote Python scripts to perform the analyses to apply the Geographic Profiling for individuating the starting origin of an infection by using the old Snow's data set. We modified the method by applying a weight to each point of the map where cases of cholera were reported. The weight was proportional to the number of cases in a given location.This modification of the Geographic Profiling method allowed to individuate in the map an area of maximum probability of the infection source, which was a few meters wide and including the historically known source of cholera, that is the “classical” water pump at Broad Street.The method appears to be a useful complement in order to individuate the source of epidemics when available data about the cases of the infections can be summarized on a map

Tracing outliers in the dataset of Drosophila suzukii records with the Isolation Forest method

The analysis of big data is a fundamental challenge for the current and future stream of data coming from many different sources. Geospatial data is one of the sources currently less investigated. A typical example of always increasing data set is that produced by the distribution data of invasive species on the concerned territories. The dataset of Drosophila suzuki invasion sites in Europe up to 2011 was used to test a possible method to pinpoint its outliers (anomalies). Our aim was to find a method of analysis that would be able to treat large amount of data in order to produce easily readable outputs to summarize and predict the status and, possibly, the future development of a biological invasion. To do that, we aimed to identify the so called anomalies of the dataset, identified with a Python script based on the machine learning algorithm “Isolation Forest”. We used also the K-Means clustering method to partition the dataset. In our test, based on a real dataset, the Silhouette method yielded a number of clusters of 10 as the best result. The clusters were drawn on the map with a Voronoi tessellation, showing that 8 clusters were centered on industrial harbours, while the last two were in the hinterland. This fact led us to guess that: (1) the main entrance mechanisms in Europe may be the wares import fluxes through ports, occurring apparently several times; (2) the spreading into the inland may be due to road transportation of wares; (3) the outliers (anomalies) found with the isolation forest method would identify individuals or populations that tend to detach from their original cluster and hence represent indications about the lines of further spreading of the invasion. This type of analysis aims hence to identify the future direction of an invasion, rather than the center of origin as in the case of geographic profiling. Isolation Forest provides therefore complimentary results with respect to PGP. The recent records of the invasive species, mainly localized close to the outliers position, are an indication that the isolation forest method can be considered predictive and proved to be a useful method to treat large datasets of geospatial data

The effect of different sports specialization on ankle joint mobility of young players

The aim of the study was to investigate the effects of practicing different sports on ankle joint mobility (AJM) in young subjects. In 344 players of 5 different sports (soccer, classical ballet, gymnastics, volleyball and basketball), mean age 12.0±2.4 years, sex (male/female: 237/107), BMI 19.0±2.8 (Kg/m 2 ), AJM was evaluated by using an inclinometer while the trunk flexibility was evaluated by the Sit and Reach test. Compared to all other groups, soccer players showed a significant reduction of AJM (p<.005) that is already present in younger subjects and that tends to worsen with aging (p<.04). On the contrary, the young dancers of classic ballet showed a significant increase in the AJM (p<.002). Basketball, volleyball and gymnastics groups showed a similar AJM. The higher AJM showed by females compared to males (128.5±21.0° vs 144.6±18.5°; p<.001) was not significant when the group of soccer players and dancers were excluded from the calculation. All groups investigated did not show a different mobility between the two ankles or the dominant and non-dominant limb. The age of the subjects investigated was not correlated with AJM. The group of gymnasts showed a significant increase in trunk flexibility (p<.001) compared to all other groups. Sport practice can significantly modify AJM both by increasing and reducing it. Such process should be timely assessed in order to prevent these alterations along with the related possible negative effects in the short and long term

Type 1 diabetes, sport practiced, and ankle joint mobility in young patients: What is the relationship?

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Forces distribution during plantar stand among the myo-osteo-joint components of the foot. Simulations and analysis on a human anatomical network model

The anatomical network analysis allows to explore the network of relationships among the anatomical parts of the human body. In our previous work the features of (2) a wide anatomical - biomechanical network were investigated. The human foot represents a highly complex anatomical structure that carries motor-sensor functions and load distribution through a network of bones, muscles, joints and tendons. The main contacts with the ground during standing position match the metatarsal region for the forefoot and the calcaneus for the backfoot. Studies on the transmission of load in the forefoot area have shown that the latter cannot be considered as a metatarsal arch but rather as a continuous line in physiological condition of metatarsal motility (1, 3). Moreover, electromyographic studies (4, 5, 6) can only give information on the activation of the extrinsic muscles of the foot during walking, without providing any response about the distribution of the load, and the different role played by the numerous anatomical structures involved (7). Here we present a weighted anatomical network of the foot, where every single node has a numerical value deriving from both the Young’s modulus calculation and the number of connections with other nodes. The network consists of 116 nodes interconnected by 219 links and represents the biomechanical structure of the foot as activated by the plantar support. By the collection of the data, the nodes cluster of the foot can be extrapolated and by detecting of the direct pressure on the plantar support, the virtual foot network can be reconstructed