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The global extent and temporally asynchronous pattern of COVID-19 spread have repeatedly highlighted the role of international borders in the fight against the pandemic. Additionally, the deluge of high resolution, spatially referenced epidemiological data generated by the pandemic provides new opportunities to study disease transmission at heretofore inaccessible scales. Existing studies of cross-border infection fluxes, for both COVID-19 and other diseases, have largely focused on characterizing overall border effects. Here, we couple fine-scale incidence data with localized regression models to quantify spatial variation in the inhibitory effect of an international border. We take as a case study the border region between the German state of Saxony and the neighboring regions in northwestern Czechia, where municipality-level COVID-19 incidence data are available on both sides of the border. Consistent with past studies, we find an overall inhibitory effect of the border, but with a clear asymmetry, where the inhibitory effect is stronger from Saxony to Czechia than vice versa. Furthermore, we identify marked spatial variation along the border in the degree to which disease spread was inhibited. In particular, the area around Löbau in Saxony appears to have been a hotspot for cross-border disease transmission. The ability to identify infection flux hotspots along international borders may help to tailor monitoring programs and response measures to more effectively limit disease spread.

This dataset includes all data discussed in the publication "A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations".

This model code includes all scripts necessary  to reproduce the results of the publication "A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations".

The COVID-19 pandemic has had unprecedented health and economic impacts worldwide. Various fields of science approached this topic to better understand the pandemic’s spread characteristics and provide a factual base for nonpharmaceutical interventions. One of the interesting non-invasive methodologies, wastewater epidemiology, looks for the correlation between the outcomes of the wastewater-based PCR analysis and the epidemiological situation in their catchment (e.g., Bogler et al., 2020; Farkas et al., 2020). This approach may efficiently predict the outbreak in the endemic phase when the conventional testing is usually underrated. Therefore a visual analytical tool may be beneficial for observing the spatiotemporal correlations of the wastewater indicators and the focal incidence levels. However, based on the analysis of existing tools (Naughton et al., 2021), most dashboards integrating wastewater indicators do not use information about geographical space at all, or the functionality of the map component is limited to the identification of the particular wastewater plant. In the where2test project (www.where2test.de), we established a scalable operational data store (ODS) to automatically collect, store and integrate heterogeneous epidemiology-related data for various administrative levels of several European countries, focusing primarily on the region of Saxony. The project includes various activities, including spatiotemporal analyses (Mertel et al., 2022), epidemiological models and applications (Davoodi et al., 2022), and forecasts. In this paper, we present a prototype of an interactive dashboard to explore the correlation between the local incidence as estimated by conventional testing and the wastewater-based PCR indicators (cT values) in Saxony. Another goal of this application is to focus on the geospatial aspect of these data. The top part of the dashboard features an interactive timeline linked to a multidimensional heatmap. Here, the user can navigat

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 \textit{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. The data synchronization runs at a daily interval, which is displayed at https://www.where2test.de.

Comparing the non-pharmaceutical intervention (NPI) strategies different nations employed to combat COVID-19 is a key step in preparing for future pandemics. Conventional approaches to this problem focus on identifying and ranking individual NPI effects. These efforts are complicated by vastly different political, economic, and social conditions among nations, which we refer to collectively as national framework conditions (NFCs). Furthermore, NPIs are typically applied as packages of interventions, which makes identifying their independent effects challenging. In addition, conventional approaches to studying NPI effects frequently neglect the economic and social consequences of these measures. Here, we introduce a novel, scenario-based approach to understanding NPI effects across nations. Our method couples simple epidemiological, behavioral, and economic models, and allows us to transfer NPI strategies from a reference nation to a focal nation while preserving the packaged nature of NPIs and controlling for NFCs. We conclude by considering future extensions to our framework and discussing its potential to facilitate NPI inter-comparisons worldwide.

The COVID-19 pandemic has entered a new endemic phase with the recent, highly transmissible omicron variant. To navigate this transition, the Where2Test group develops an integrated suite of models, datasets, optimization algorithms, and user-friendly webapps to help users understand and manage SARS-CoV-2 infection risk in a range of different organizational settings. All of these applications are available on www.where2test.de.

Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of e.g. Earth Sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields, it can perform kriging and variogram estimation and much more. We demonstrate its abilities by virtue of a series of example application detailing their use.

The SARS-CoV-2 virus has spread around the world with over 100 million infections to date, and currently many countries are fighting the second wave of infections. With neither sufficient vaccination capacity nor effective medication, non-pharmaceutical interventions (NPIs) remain the measure of choice. However, NPIs place a great burden on society, the mental health of individuals, and economics. Therefore the cost/benefit ratio must be carefully balanced and a target-oriented small-scale implementation of these NPIs could help achieve this balance. To this end, we introduce a modified SEIRD-class compartment model and parametrize it locally for all 412 districts of Germany. The NPIs are modeled at district level by time varying contact rates. This high spatial resolution makes it possible to apply geostatistical methods to analyse the spatial patterns of the pandemic in Germany and to compare the results of different spatial resolutions. We find that the modified SEIRD model can successfully be fitted to the COVID-19 cases in German districts, states, and also nationwide. We propose the correlation length as a further measure, besides the weekly incidence rates, to describe the current situation of the epidemic.

We are going to analyse the Herten aquifer, which is situated in Southern Germany. Multiple outcrop faces where surveyed and interpolated to a 3D dataset.

GeoStatTools provides geostatistical tools for various purposes: - random field generation - simple, ordinary, universal and external drift kriging - conditioned field generation - incompressible random vector field generation - (automated) variogram estimation and fitting - directional variogram estimation and modelling - data normalization and transformation - many readily provided and even user-defined covariance models - metric spatio-temporal modelling - plotting and exporting routines

Recent studies have shown that rhizosphere hydraulic properties may differ from those of the bulk soil. Specifically, mucilage at the root–soil interface may increase the rhizosphere water holding capacity and hydraulic conductivity during drying. The goal of this study was to point out the implications of such altered rhizosphere hydraulic properties for soil–plant water relations. We addressed this problem through modeling based on a steady-rate approach. We calculated the water flow toward a single root assuming that the rhizosphere and bulk soil were two concentric cylinders having different hydraulic properties. Based on our previous experimental results, we assumed that the rhizosphere had higher water holding capacity and unsaturated conductivity than the bulk soil. The results showed that the water potential gradients in the rhizosphere were much smaller than in the bulk soil. The consequence is that the rhizosphere attenuated and delayed the drop in water potential in the vicinity of the root surface when the soil dried. This led to increased water availability to plants, as well as to higher effective conductivity under unsaturated conditions. The reasons were two: (i) thanks to the high unsaturated conductivity of the rhizosphere, the radius of water uptake was extended from the root to the rhizosphere surface; and (ii) thanks to the high soil water capacity of the rhizosphere, the water depletion in the bulk soil was compensated by water depletion in the rhizosphere. We conclude that under the assumed conditions, the rhizosphere works as an optimal hydraulic conductor and as a reservoir of water that can be taken up when water in the bulk soil becomes limiting.