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In this work, we describe and forecast the fourth wave of the SARS-CoV-2 epidemic, driven by the Delta variant, using pDyn — a detailed epidemiological agent-based model. It is designed to explain the spatiotemporal dynamics of the SARS-CoV-2 spread across Polish society, predicting the number and locations of disease-related states for agents living in the virtual society in response to varying properties of the pathogen and the social structure and behavior. We evaluate the validity of the dynamics generated by the model, including the succession of pathogen variants, immunization dynamics, and the ratio of vaccinated individuals among confirmed cases. Additionally, we assess the model’s predictive power in estimating pandemic dynamics (peak timing, peak value, and wave length) of disease-related states (number of confirmed cases, hospitalizations, ICU hospitalizations, and deaths) both at the national level and in the highest administrative units in Poland (voivodships). When testing the model’s validity, we compared real-world data (excluding data used for calibration) to our model estimates to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time (October 28, 2021). To assess the accuracy of pDyn’s predictions, we retrospectively compared simulation results with real-world data acquired after the simulation date, evaluating pDyn as a tool for predicting future epidemic spread. Our results indicate that pDyn accurately predicts and can help us better understand the mechanisms underlying the SARS-CoV-2 dynamics.

In this work properties of the dynamic regional lockdown approach to suppress the COVID-19 epidemic spread in Poland were investigated. In particular, an agent based model was used with the aim to indicate an optimal lockdown strategy, defined here as the one which minimizes mean lockdown time over regional unit provided health service is not overwhelmed. With this approach the lockdown extent was also considered by varying restrictions between complete regional school closure and/or significant social distancing in semi-public spaces. In result, a cooperative effect was discovered in the case when closure of schools was accompanied by severe restrictions of social contacts in semi-public spaces. Moreover, the regional lockdown approach implemented here on the level of counties (units of population around 100k) proofed to be successful, that is allowed to identify optimal entrance and release thresholds for lockdown. The authors believe that until significant portion of population is vaccinated such a strategy might be applied.

We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October – 19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Collaboration between multiple computing centres, referred as federated computing is becoming important pillar of High Performance Computing (HPC) and will be one of its key components in the future. To test technical possibilities of future collaboration using 100Gb optic fiber link (Connection was 900 km in length with 9ms RTT time) we prepared two scenarios of operation. In the first one, Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) in Warsaw and Centre of Informatics - Tricity Academic Supercomputer & networK (CI-TASK) in Gdansk prepared a long distance geographically distributed computing cluster. System consisted of 14 nodes (10 nodes at ICM facility and 4 at TASK facility) connected using InfiniBand. Our tests demonstrate that it is possible to perform computationally intensive data analysis on systems of this class without substantial drop in performance for a certain type of workloads. Additionally, we show that it is feasible to use High Performance Parallex [1], high level abstraction libraries for distributed computing, to develop software for such geographically distributed computing resources and maintain desired efficiency. In the second scenario, we prepared distributed simulation-postprocessing-visualization workflow using ADIOS2 [2] and two programming languages (C++ and python). In this test we prove capabilities of performing different parts of analysis in seperate sites.

The paper presents an evaluation of all-reduce collective MPI algorithms for an environment based on a geographically-distributed compute cluster. The testbed was split into two sites: CI TASK in Gdansk University of Technology and ICM in University of Warsaw, located about 300 km from each other, both connected by a fast optical fiber Ethernet-based 100 Gbps network (900 km part of the PIONIER backbone). Each site hosted a set of 10 compute nodes interconnected locally by the InfiniBand switches with the traffic forwarded by specialized hardware: IBEX G40 - QDR InfiniBand RDMA based Extension Platform. A set of six all-reduce algorithms, consisting of two ring-based (including a PAP-aware pre-reduced ring), two binomial-tree based and two hierarchical ones, was tested for balanced and imbalanced process arrival patterns (PAPs). The results showed high and stable bandwidth with large data transmission latency of the branch connecting the remote sites (about 13 ms in comparison to 10 µs locally), and for the tested algorithms there was an advantage of hierarchical approach, and then binomial tree. Finally, we also observed some performance increase in PAP-aware solution in comparison to its regular counterpart. The main conclusion is that for the distributed cluster environment with imbalanced PAPs, there is a need for designing new hierarchical algorithms with PAP-aware support.

Great amount of stored information used in connection with Machine Learning and statistical methods enables high quality insight and analysis of data that leads to design of high precision predictive and classification systems. In the process of analysis, selection of most informative features is crucial for later quality of the designed system. In this report, we propose two implementations of multidimensional feature selection (MDFS) algorithm (Piliszek et al. in Mdfs-multidimensional feature selection. arXiv preprint. arXiv:1811.00631, 2018) that can be used in distributed environments for detection of all-relevant variables in data sets with discrete decision variable. While most methods discard information about interactions between features, MDFS is designed towards identification of informative variables that are not relevant when considered alone but are relevant in groups. We have developed software using C++ and High Performance ParalleX (HPX) (Kaiser et al. in STEllAR-GROUP/hpx: HPX V1.3.0: the C++ Standards library for parallelism and concurrency. 2019. 10.5281/zenodo.3189323, 2019) to achieve best performance, great scalability and portability. HPX is a library that uses lightweight threads, asynchronous communication, and asynchronous task submission based on the declarative criteria of work. These features enabled us to deeply explore granularity and parallelism of the MDFS algorithm. Software is prepared entirely in C++; therefore, calculations can be performed using CPUs on desktops, distributed systems, and any system with C++ compiler support. During testing on Cray XC40 (Okeanos) using artificially prepared data, we achieved 196 times acceleration on 256 nodes compared to a single node. From this point, ICM computing facility is capable of massively parallel feature engineering. The main purpose of the software is to enable researchers for more accurate genomics data analysis in search for multiple correlations in potential sources of the diseases.