Infrastructure for spatiotemporal exploration of interregional and international interaction of epidemiological data (DEMO PAPER)

The recent waves of COVID-19 highlighted the importance of understanding and quantifying spatiotemporal interactions to infer, model, and predict disease spread in real time. In this demonstration paper, we present a robust infrastructure for interactive exploration of interregional and international spatiotemporal interactions via time-lagged correlations of increases in COVID-19 incidence. This infrastructure consists of: (i) an operational data store (ODS) coupled with automated scripts for downloading, cleaning, and processing data from heterogeneous sources;(ii) a server application handling on-demand analyses of the database data through a RESTful API;and (iii) a web application providing the interactive dashboard to explore various correlation and geostatistical metrics of the integrated data in spacetime. The environment allows users to study focal spatiotemporal trends and the potential of regions to export and import the virus. Moreover, the application has the potential to reveal the effect of the national border to mitigate the interaction, particularly the spread of the virus. The infrastructure serves COVID-19 data from Germany, Poland, and Czechia, with the possibility of extension to other regions and topics. The dashboard is under active development and accessible on www.where2test.de/correlation. © 2022 Owner/Author.

A scalable pipeline for COVID-19: the case study of Germany, Czechia and Poland

Throughout the coronavirus disease 2019 (COVID-19) pandemic, decision makers have relied on forecasting models to determine and implement non-pharmaceutical interventions (NPI). In building the forecasting models, continuously updated datasets from various stakeholders including developers, analysts, and testers are required to provide precise predictions. Here we report the design of a scalable pipeline which serves as a data synchronization to support inter-country top-down spatiotemporal observations and forecasting models of COVID-19, named the where2test, for Germany, Czechia and Poland. We have built an operational data store (ODS) using PostgreSQL to continuously consolidate datasets from multiple data sources, perform collaborative work, facilitate high performance data analysis, and trace changes. The ODS has been built not only to store the COVID-19 data from Germany, Czechia, and Poland but also other areas. Employing the dimensional fact model, a schema of metadata is capable of synchronizing the various structures of data from those regions, and is scalable to the entire world. Next, the ODS is populated using batch Extract, Transfer, and Load (ETL) jobs. The SQL queries are subsequently created to reduce the need for pre-processing data for users. The data can then support not only forecasting using a version-controlled Arima-Holt model and other analyses to support decision making, but also risk calculator and optimisation apps [1, 2]. The data synchronization runs at a daily interval, which is displayed at https://www.where2test.de. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)