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Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.

Background: Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.Objective: We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the “testing strategy,” is based on dividing facility residents into groups and then testing no more than 1 group per day.Methods: We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution.Results: Considering the number of residents and staff and other practical constraints of the facilities, the pro

Source code for the backend of the Where2Test website. The backend connects to a database and scrapes online information needed to run the following mini-applications that make up the Where2Test COVID research project website: Retirement Home Testing Optimizer COVID-19 Workplace Occupancy Optimizer Saxony Wastewater Forecast Dashboard Regional Forecast Dashboards The backend is written in the Python programming language using the Flask web application framework.

This project consists of forecasting methods for the datasets of Covid 19 for Sachsen and Czechia, and the associated data. In a live setting it is automatically updated via a CI/CD pipeline (e.g. GitLab), and uploaded to a database that can be then accessed by a webserver backend, or a similar data consumer. This dataset has served as a basis for Where2Test website forecast dashboards for Sachsen and Czechia.

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.

We develop a software system for the spatial, temporal, and strategic optimization of the use of tests for SARS-COV-2. 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 to store the COVID-19 data from Germany, Czechia, and other areas. We have built the schema of metadata which is capable of orderly storing the data from those regions, and is scalable to the entire world. Next, the ODS is populated using batch Extract, Transfer, and Load (ETL) jobs and SQL queries are created which reduce the need for pre-processing data. The data can then support forecasting using a version-controlled Arima and Holt Winter model and other analyses to support decision making. The jobs run at a weekly interval and plan to upgrade to a daily interval. The results are finally displayed as a web app at https://www.where2test.de.

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. The data synchronization runs at a daily interval, which is displayed at this https URL.

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.

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.

Orangutans encounter habitat quality deterioration due to land-use changes and associated forest fires. This creates a complex stress scenario for the individual animals and threatens populations. The disturbance of forest canopies or loss of trees induce changes in primates' movement behavior: the preferred arboreal movement needs to be complemented by movements on the ground requiring more energy, and prolonging the search for fruiting and nesting trees. This all together leads to a change in the daily activity patterns (time spent to travel, feeding and resting) and subsequently to shift in the available energy budget decreasing the fitness of the animals. While agent-based models have been developed and widely employed to check the impact of disturbances or conservation management on the habitat use, population development and viability, there has been less emphasis on studying the direct impact of canopy disturbances on the arboreal movement of individual primates, which can be the origin of cascading effects on animals health, vitality and subsequent life processes. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement between trees in a forest depending on distances among trees and canopy structure. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure involving mechanical work of locomotion. The model was tested with forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. Information on tree constellations, sizes etc. were used to reconstruct a virtual forest with similar characteristics (e.g., trees network in terms of connections between overlapping canopies) as in the real world. In order to parameterise the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm on high computing clusters and e

We provide post-processing data of daily infected COVID-19 cases for a municipality (Obec) level. The current data for municipality level is prepared on Czech_Obec_COVID19_Infections.csv. The file consists of five columns such as region, name, date, infected and population. The region denotes the ID of a county/state followed by its name in the next column. The inserted date of data is prepared in the third column followed by the number of dead and infected cases. Last but not least, the population of the county is provided in the last column. This data hub was partially funded by the Where2Test project, which is financed by SMWK with tax funds on the basis of the budget approved by the Saxon State Parliament. This data hub was also partially funded by the Center of Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.

Software to synchonise the data between various data sources and casus database server. For Unix users please use MigrateWhere2test_0.7Unix.zip and for WIndows users please use MigrateWhere2test_0.7Win.zip. In order to use the scripts, please use the following instructions: Windows 1. Create the postgreq sql database and set the port 5432  2. Create folder C:\Workspaces and unzip the unix file.  3. Create folder in workspaces, com.com.casus.env.where2test.migration\COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.  4. Set run Develop and run the .bat file on the folder MigrateWhere2test_0.7Unix to run in localhost. Unix 1. Create PostgreSQL with port 32771. 2. Create folder /home/wildan/Workspaces and unzip the unix file.  3. Create folder in workspaces, com.com.casus.env.where2test.migration.unix/COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.  4. Set run Develop and run the .sh file on the folder MigrateWhere2test_0.7Unix to run in localhost.

We provide post-processing data of daily dead and infected COVID-19 cases for a county (Landkreise) and a state (Bundesland) level. The data are extracted from the following link the data source, and subsequently transferred to the Casus-HZDR database server (see Fig above). The age-based and gender-based data are then aggregated and prepared in csv file. The current data for county level is prepared on Germany_Counties_COVID19_Death_Infections.csv and its daily version is stored in Archive folder. Likewise, the actual data for state level is prepared on Germany_States_COVID19_Death_Infections.csv and its daily version is stored in Archive folder. The file consists of six columns such as region, name, date, dead, infected and population. The region denotes the ID of a county/state followed by its name in the next column. The inserted date of data is prepared in the third column followed by the number of dead and infected cases. Last but not least, the population of the county is provided in the last column.

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.

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.

Cellular surface recognition and behavior are driven by a host of physical and chemical features which have been exploited to influence particle–cell interactions. Mechanical and topographical cues define the physical milieu which plays an important role in defining a range of cellular activities such as material recognition, adhesion, and migration through cytoskeletal organization and signaling. In order to elucidate the effect of local mechanical and topographical features generated by the adsorption of particles to an underlying surface on primary human monocyte‐derived macrophages (MDM), a series of poly(N‐isopropylacrylamide) (pNIPAM) particles with differing rigidity are self‐assembled to form a defined particle‐decorated surface. Assembly of particle‐decorated surfaces is facilitated by modification of the underlying glass to possess a positive charge through functionalization using 3‐aminopropyltriethoxysilane (APTES) or coating with poly(L‐lysine) (PLL). MDMs are noted to preferentially remove particles with higher degrees of crosslinking (stiffer) than those with lower degrees of crosslinking (softer). Alterations to the surface density of particles enabled a greater area of the particle‐decorated surface to be cleared. Uniquely, the impact of particle adsorption is evinced to have a direct impact on topographical recognition of the surface, suggesting a novel approach for controllably affecting cell‐surface recognition and response.

Nanoparticle adsorption to substrates pose a unique challenge to understand uptake mechanisms as it involves the organization of complex cytoskeletal components by cells to perform endocytosis/phagocytosis. In particular, it is not well‐understood from a cell mechanics perspective how the adhesion of particles on substrate will influence the ease of material clearance. By using a particle model, key contributing factors underlying cell adhesion on nonporous silica particle surfaces, migration and engulfment, are simulated and studied. Following a 24 h incubation period, monocyte‐derived macrophages and A549 epithelial cells are able to adhere and remove particles in their local vicinity through induction of adhesive pulling arise from cell traction forces and phagocytic/endocytic mechanisms, in a size‐dependent manner. It is observed that such particle‐decorated surfaces can be used to address the influence of surface topography on cell behavior. Substrates which presented 480 nm silica particles are able to induce greater development and maturation of focal adhesions, which play an important role in cellular mechanoregulation. Moreover, under a chemotactic influence, in the presence of 30% fetal bovine serum, macrophages are able to uptake the particles and be directed to translocate along a concentration gradient, indicating that local mechanical effects do not substantially impair normal physiological functions.

The magneto-structural coupling of BiFeO3 (BFO)–CoFe2O4 (CFO)/LaAlO3 (LAO) heteroepitaxy with various lateral sizes of CFO pillars embedded in a BFO matrix was investigated. A struc- tural phase transformation of the BFO matrix was observed when the pillar size of CFO was increased to exceed 200 nm. Such structural transformation led to modification of magneto-elastic coupling behavior and magnetic anisotropy in the BFO–CFO/LAO system. The flexibility of tuning the pillar size of CFO, and hence, the strain and interfacial effect on the multiferroic system have significant application implications in these functional oxide nanocomposites.

Wound healing assays are extensively used to study tissue repair mechanisms; they are typically performed by means of physical (i.e., mechanical, electrical, or optical) detachment of the cells in order to create an open space in which live cells can lodge. Herein, an advanced system based on extensive photobleaching-induced apoptosis; providing a powerful tool to understand the repair response of lung epithelial tissue, consisting of a small injury area where apoptotic cells are still intact, is developed. Notably, the importance of epithelial mechanics and the presence of macrophages during the repair can be understood. The findings reveal that individual epithelial cells are able to clear the apoptotic cells by applying a pushing force, whilst macrophages actively phagocytose the dead cells to create an empty space. It is further shown that this repair mechanism is hampered when carbon nanotubes (CNTs) are introduced: formation of aberrant (i.e., thickening) F-actins, maturation of focal adhesion, and increase in traction force leading to retardation in cell migration are observed. The results provide a mechanistic view of how CNTs can interfere with lung repair.