Data & Covariates Access

Data & covariates access

Many factors contribute to the (re-)emergence of infectious disease threats and to the development of antimicrobial resistance, altering the epidemiology and spread of diseases in a changing global environment . They increase the potential for human-to-human transmission, strengthening the epidemic/pandemic potential of pathogens, including those transmitted by arthropod vectors such as ticks or mosquitoes.The MOOD platform acquires, processes and integrates covariate data that drive disease distributions, which are standardised in terms of resolution, spatio-temporal extent, measurement units, and geospatial projection, to fit the requirements for the modelling activities. 

TBE Covariates Dashboard

This tool allows to explore metadata about environmental and ecological covariates adopted in literature, with respect to tick-
borne encephalitis.

The dashboard is based on the results of a literature review performed on articles published between 2000 and 2021, focusing on European countries.
We included primary research studies, modeling studies proposing quantitative analysis using explanatory variables (e.g. variables related to environment, vector and hosts ecology) and data collections with abstract and full-text document available in English. Studies with no data or with duplicated data were excluded (patents, editorials, letters, modelling studies with no data). We also excluded records with no denominator, no identified reference population, full-text document unavailable, low quality (or quality not assessable), and referring to data older than 2000 or gathered outside Europe.

The visualizations include:

  •  The covariates category (environmental, host- and vector-related) and the specific variables adopted in published literature, ranked by the number of articles they
    appear in.
  •  The geographical distribution of selected articles.

 A summary table with the following features:

  •  First author, year of publication
  • Covariates explored in the study, with their values (when available),
    short description, and notes.
  • Response variable, i.e. the focus, of the study.
  • Type of analysis used in the study.
  •  Covariates data sources.

The report can be filtered by topic of interest, the type of analysis and type of covariates.
Graphs and tables can be explored by clicking on them and can be exported in .csv format.

Contact: Fondazione Edmund Mach, Applied Ecology Unit
Francesca Dagostin – francesca.dagostin@fmach.it

MOOD Dataverse for covariates

Overview

MOOD aims to provide a wide range of disease relevant spatial and  epidemiological data to project  partners and its public health users. In the long term this will be implemented through the MOOD platform which will provide the data,  extraction utilities and visualisations.  In the mean time  the required data are being identified, acquired, processed and provided to partners on an adhoc basis or via temporary online archives and geographic data archives based on developer geonetwork sites.

Data provided

Two broad categories of data provided are provided:  covariate data  and disease data.

At this stage in the project,  the data about the factors which drive the occurrence of each disease (the covariates) are the most mature.

The datasets provided have been identified by literature search,   iterative statistical assessments and expert opinion.

They include:

  • Climate (temperature, rainfall, relative humidity);
  • Environment (vegetation, agriculture,  land use);
  • Demography;
  • Topography;
  • Host and Vector suitability;
  • Movement and Mobility,  and many others.

Th Climatic and Environmental data are provided as time series for the period 2010-2021.
Summary data packs of the most relevant datasets for each disease are provided for download  as well as a generic datapack of the covariates that are relevant to a number of diseases

A key feature of all the data provided is that they are standardised in terms of format, spatial extent,  resolution and projection, and units.  All data are georeferenced.  This is essential to optimise analyses that incorporate a wide range of inputs derived from disparate sources and temporal ranges

Data access

At this stage sample covariate data are provided via interim spatial data archives and websites: