EPICO19 - EPIdemiological and logistic COvid19 model

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Objectives of the service

Image credit: TerrAria srl, Project: EPICO19

When in February 2020 the COVID-19 outbreak swept across Italy, health professionals had to face and tackle an entirely new epidemic, without the necessary tools to analyse, understand and curtail it.

The main objective of EPICO19 service is to support public health technicians and decision makers in managing the COVID-19 outbreak providing them with:

  • to map, monitor and forecast the spread of the outbreak through a machine learning model
  • to assess the effect of public health measures according to a what-if approach both in the past and in the future: e.g. modelling what would have happened in the first and second wave identifying the best interventions to better control a possible third wave
  • to monitor vulnerability and crowding of population through satellite observation, respectively modelling exposure to air pollution and counting and geo-locating vehicles with an AI engine
  • to implement a DSS (Decision Support System) integrating all the previous functionalities in a single Web-platform

Users and their needs

EPICO19 system will be tested during the Pilot in the Reggio Emilia province, the most affected one by the COVID-19 in Emilia Romagna, thanks to the end-user Reggio Emilia Health Authority  – AUSL_RE - ‘Azienda USL di Reggio Emilia’. AUSL_RE provides health care and public health services to the 42 Municipalities of the Reggio Emilia province, with a population of 535’000 inhabitants.

Main user-needs are:

  • a geographical information system to monitor and analyse the distribution of cases supporting the individuation of possible epidemic outbreaks
  • an epidemiological model based on the first a second wave, to predict the future spread of COVID-19 and comparable epidemics
  • assessing the efficacy of public health measures supporting with a what-if modelling approach the identification of the best interventions
  • assessing the role of risk factors such as crowding and air pollution, based on satellite images
  • providing a user-friendly web-tool supporting spatial and temporal analysis of health and environmental indicators and their combinations and processing
  • making available real time update and forecast of the spread of the outbreak

The key user segments targeted by our product is are the Local Health Authorities but also public institution having in charge the epidemics management(e.g. prefectures, civil protection…).

Service/ system concept

Image credit: TerrAria srl, Project: EPICO19

EPICO19 is a web application allowing to predict the spread of the outbreak in terms of cases, hospitalization, deaths… based on a machine learning engine that uses:

  • population structure, by age and sex,
  • crowding indicators through AI-processed VHR satellite images
  • air pollution exposure through Satellite Earth Observation modelling

EPICO19 Back-End (BE): the whole set of processes, DBs and pre/post-processors that make up the "engine" of the system. Main components are

  • Health parameter spatial and temporal database
  • Environmental parameter database derived from satellite images (crowding and NO2 and PM concentrations)
  • Machine learning epidemiological model to forecast health policies scenarios through a what-if approach

Front-End (FE): Graphical User Interfaces represented by a map-centric web platform; whose main outcomes are:

  • Analysing past outbreaks spread and dynamics
  • Monitoring current outbreak spread through diffusion maps with municipal and census section detail
  • Forecasting the spread outbreak and the effectiveness of lockdown/mobility restrictions to curb the outbreak in the study area
  • Reporting map and time series of health parameters and indicators

EPICO19 has a restricted access for public health experts with all EPICO19 functionalities and a public access for citizen providing selected outbreak maps and graphs.

Space Added Value

EPICO19 space assets include CAMS service and VHR imagery.

We exploit the CAMS’ analysis service (a compromise between forecast and reanalysis) which incorporates observations from satellite instruments (e.g, Sentinel-5P) and ground data, combining them with modelling to estimate PM10, PM2.5, NO2 air concentration. Air pollution is used to assess vulnerability of population and meteorological conditions that may influence the spread of the infection.

VHR satellite and aerial imagery, with a ground resolution respectively of 30-50 cm and 11 cm, are used to estimate the crowding index. Images will be analysed by an Artificial Intelligence vision proprietary algorithm by Studiomapp, pulling out up to 100 classes of objects. This enables to count the number of cars, busses or trucks, and to assess the presence of people in sites where crowding is expected to occur, such as hospitals, parking lots, supermarkets, working places, train stations, and logistic hubs.

Air pollution and crowding index are among the inputs of EPICO19 epidemiological model, which is also based on space and time distribution of age- and sex-specific COVID-19 cases. EPICO19 therefore combines advanced AI and environmental techniques based on satellite resources with machine learning techniques on epidemiological data.

Current Status

Image credit: TerrAria srl, Project: EPICO19

The full development of the EPICO19 application is yet started. In these two initial months, the Consortium has carried out these activities:

  • in strict collaboration with the Modena and Reggio Emilia Ethics Committee administration, the Consortium submitted the application and obtained the access to almost 90’000 records of personal and health data needed for the project, according to its epidemiologic methodology;
  • the acquisition plan for VHR satellite images has been developed and the strategies for CAMS data acquisition have been decided
  • a first prototype version of the epidemiological machine learning model has been designed based on the first wave dataset;
  • several meetings with the user AUSL of Reggio Emilia have been organized, to co-design EPICO19 mock-up

Status Date

Updated: 24 February 2021 - Created: 24 February 2021