COVID-19

BART - A Bayesian machine learning workflow for complex spatial data

In our recent publication from 2020 we analyzed COVID-19 incidence rates using a multimethod approach. In this post I will present BART - a Bayesian machine learning algorithm - to model COVID-19 incidence rates.

COVID-19: 15km Radius WebApp

Klicke hier für die mobilfreundliche Version 📱 COVID-19 Bewegungsradius Stand: 15.1.2020 Die 15-Kilomenter Regel, nachdem sich Bewohner mit einer 7-Tage Inzidenz von über 200 nur noch 15 km um ihren Wohnort bewegen dürfen, sorgt für Verwirrung.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases. A cross-sectional case study of COVID-19 incidence in Germany

As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.