Spatio-temporal outbreaks of campylobacteriosis and the role of fresh-milk vending machines in the Czech Republic: A methodological study.

Inspired by local outbreaks of campylobacteriosis in the Czech Republic in 2010 linked to the debate about alleged health risks of the raw milk consumption, a detailed study was carried out. Firstly, scanning was utilised to identify spatio-temporal clusters of the disease from 2008 to 2012. Then a spatial method (geographical profiling originally developed for criminology) served as assessment in selecting fresh-milk vending machines that could have contributed to some of the local campylobacteriosis outbreaks. Even though an area of increased relative risk of the disease was identified in the affected city of Ceské Budejovice during January and February 2010, geoprofiling did not identify any vending machines in the area as the potential source. However, possible sources in some nearby cities were suggested. Overall, 14 high-rate clusters including the localisation of 9% of the vending machines installed in the Czech Republic were found in the period 2008-2012. Although the vending machines are subject to strict hygiene standards and regular testing, a potential link between a small number of them and the spatial distribution of campylobacteriosis has been detected in the Czech Republic. This should be taken into account in public health research of the disease.

The Crisis Map of the Czech Republic: the nationwide deployment of an Ushahidi application for disasters.

Crisis mapping is a legitimate component of both crisis informatics and disaster risk management. It has become an effective tool for humanitarian workers, especially after the earthquake in Haiti in 2010. Ushahidi is among the many mapping platforms on offer in the growing field of crisis mapping, and involves the application of crowdsourcing to create online and interactive maps of areas in turmoil. This paper presents the Crisis Map of the Czech Republic, which is the first such instrument to be deployed nationwide in Central Europe. It describes the methodologies used in the preparatory work phase and details some practices identified during the creation and actual employment of the map. In addition, the paper assesses its structure and technological architecture, as well as its potential possible development in the future. Lastly, it evaluates the utilisation of the Crisis Map during the floods in the Czech Republic in 2013.

Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

BACKGROUND: Visual analytics aims to connect the processing power of information technologies and the user's ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution. METHODS: We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease's weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. RESULTS: Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. CONCLUSIONS: We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself.