The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the Hospital Policlinico Umberto I in Rome (Lazio region, Italy), is to carry out a territorial screening of the municipality using GIS applications and spatial analyses aimed at reducing—in terms of triage—code white (inappropriate) attendances, after having identified the areas of greatest provenance of improperly used emergency room access. Working in a GIS environment and using functions for geocoding, we have tested an experimental model aimed at giving a close-up geographical-sanitary look at the situation: recognizing the territorial sectors in Rome which contribute to amplifying the Policlinico Umberto I emergency room overcrowding; leading up to an improvement of the situation; promoting greater awareness and knowledge of the services available on the territory, a closer relationship between patient and regular doctor (general practitioner, GP) or Local Healthcare Unit and a more efficient functioning of the emergency room. In particular, we have elaborated a “source” map from which derive all the others and it is a dot map on which all the codes white have been geolocalized on a satellite image through geocoding. We have produced three sets made up of three digital cartographic elaborations each, constructed on the census sections, the census areas and the sub-municipal areas, according to data aggregation, for absolute and relative values, and using different templates. Finally, following the same methodology and steps, we elaborated another dot map about all the codes red to provide another kind of information and input for social utility. In the near future, this system could be tested on a platform that spatially analyzes the emergency department (ED) accesses in near-real-time in order to facilitate the identification of critical territorial issues and intervene in a shorter time to regulate the influx of patients to the ED.

This paper—which is contextualized in the discussion on the methodological pluralism and the main topics of medical geography, the complexity theory in geographies of health, the remaking of medical geography and ad hoc systems of data elaboration—focuses on radio base stations (RBSs) as sources of electromagnetic fields, to provide GIS applications and simplifying-prudential models that are able to identify areas that could potentially be exposed to hazard. After highlighting some specific aspects regarding RBSs and their characteristics and summarizing the results of a number of studies concerning the possible effects of electromagnetic fields on health, we have taken an area of north-east Rome with a high population and building density as a case study, and we have provided some methodological and applicative exemplifications for different situations and types of antennas. Through specific functionalities and criteria, drawing inspiration from a precautionary principle, these exemplifications show some particular cases in order to support: possible risk factor identification, surveillance and spatial analysis; correlation analysis between potential risk factors and outbreak of diseases and symptoms; measurement campaigns in heavily exposed areas and buildings; education policies and prevention actions. From an operative viewpoint, we have: conducted some field surveys and recorded data and images with specific geotechnological and geomatics instruments; retraced the routes by geobrowsers and basemaps and harmonized and joined up the materials in a GIS environment; used different functions to define, on aero-satellite images, concentric circular buffer zones starting from each RBS, and geographically and geometrically delimited the connected areas subject to high and different exposure levels; produced digital applications and tested prime three-dimensional models, in addition to a video from a bird’s eye view perspective, able to show the buildings in the different buffer zones and which are subject to a hazard hierarchy due to exposure to an RBS. A similar GIS-based model—reproposable with methodological adjustments to other polluting sources—can make it possible to conceive a dynamic and multiscale digital system functional in terms of strategic planning, decision-making and public health promotion in a performant digital health information system.

In this paper, we start from a contextualization about the measures used to contain the COVID-19 diffusion and the need to promote geotechnological proposals, data sharing and homogenous centralised systems for data collection and analysis. Successively, we present the "Dynamic Space-Time Diffusion Simulator in a GIS Environment to Tackle the COVID-19 Emergency" that we have elaborated on the basis of the data provided by the UOC Hygiene and Public Health Service -Local Health Unit Rome 1. Particularly, after describing the main technical process able to predispose the dynamic simulator, we underline the possible added value that it can provide in terms of infection surveillance and monitoring, precision preparedness, support to decision making and territorial screening. For this demonstrative application, we have extracted from the simulator some groups of four digital screenshots which are able to show synoptic photographs in temporal perspective concerning the total number of cases of COVID-19 in Rome (Italy) for the period February 25th -September 26th. Specifically we have selected: -four screenshots for the period February 25th -June 11th, to provide significant evidence about the first three months and a half; -four screenshots for the period March 1st -March 29th, to add an insight into the geographically and statistically meaningful month of March; -four screenshots for the period June 12th -September 26th, to supply an efficacious geovisualisation of the last three months and a half available; -four screenshots for the period February 25th -September 26th, to show a cumulative elaboration aimed at geolocating all the cases recorded in the seven months examined; -four screenshots for the period March 26th -September 26th, with a distinction about the first and second data sets, for a detailed (cumulative) zoom. This simulator, elaborated for the COVID-19 emergency, can be replicated in any circumstance for which specific data and information are available for the scientific community, shared and progressively updated in order to provide a productive contribution to the identification of serious infectious disease clusters, patterns and trends, and quickly respond to specific needs.

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Background Surveillance and containment of the spread of COVID-19 requires the use of advanced geographic information science and technology (GIS&T) to map the spread and eventually to guide interventions. A dynamic space-time diffusion model in a GIS environment was developed and succesfully tested in Rome, Italy. Methods Information on cases of SARS-CoV-2 infection confirmed by molecular diagnostics from Feb 25 to Sep 26 2020 (collected by a large Local Health Unit of Rome, Italy) was used to test a GIS simulator model able to monitor the spatial diffusion and temporal evolution of the spread of the disease. Data included information on: sex, date and place of birth, healthcare facility of hospitalization, date of notification, start date and end date of isolation, date of recovery (both clinical and laboratory confirmed), residence address. Results Globally, 3,056 cases were geocoded and analysed. The spatio-temporal analysis of the first 45 days since 25 Feb 2020 shows that the spread of COVID-19 was very fast (1,230 cases recorded on 11 Apr) and spatially widespread. Number of cases was highest in the city centre with clusters, thickets and axes in different sub-municipal areas. A slowdown occurred the following month, confirming the positive effect of the lockdown. This effect continued until 11 Jun with a small increase in the number of cases (+10.9%). The period up to 26 Sep is paradigmatic of the second wave, with a continuous increase in cases that spread from the city centre to the suburbs. Conclusions Using geocoding process and a detailed GIS mapping it is possible to identify streets, buildings and census sections where the number of cases is high and tends to increase rapidly and, at the same time, it is possible to distinguish clusters and axes that should be kept immediately under special observation as potential pools of super-diffusion. Development of its use in near-real time could bring significant advantages in controlling the spread of COVID-19. Key messages The use of GIS technology is fundamental for mapping the spread of COVID-19. A greater effort should be made by institutions to increase the digitisation of health data and the possibility of using them for both research and surveillance purposes.