TY - CHAP A1 - Chumachenko, Dmytro A1 - Nechyporenko, Alina A1 - Frohme, Marcus ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content. KW - epidemic model KW - machine learning KW - polynomial regression KW - war KW - COVID-19 KW - infectious disease simulation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19074 UR - https://ceur-ws.org/Vol-3302/short4.pdf VL - 3302 SP - 78 EP - 85 PB - RWTH Aachen ER - TY - JOUR A1 - Steglich, Patrick A1 - Schasfoort, Richard B. M. T1 - Surface Plasmon Resonance Imaging (SPRi) and Photonic Integrated Circuits (PIC) for COVID-19 Severity Monitoring JF - COVID N2 - Direct optical detection methods such as surface plasmon resonance imaging (SPRi) and photonic-integrated-circuits (PIC)-based biosensors provide a fast label-free detection of COVID-19 antibodies in real-time. Each technology, i.e., SPRi and PIC, has advantages and disadvantages in terms of throughput, miniaturization, multiplexing, system integration, and cost-effective mass production. However, both technologies share similarities in terms of sensing mechanism and both can be used as high-content diagnostics at or near to point of care, where the analyte is not just quantified but comprehensively characterized. This is significant because recent results suggest that not only the antibody concentration of the three isotypes IgM, IgG, and IgA but also the strength of binding (affinity) gives an indication of potential COVID-19 severity. COVID-19 patients with high titers of low affinity antibodies are associated with disease severity. In this perspective, we provide some insights into how SPR and PIC technologies can be effectively combined and complementarily used for a comprehensive COVID-19 severity monitoring. This opens a route toward an immediate therapy decision to provide patients a treatment in an early stage of the infection, which could drastically lowers the risk of a severe disease course. KW - COVID-19 KW - surface plasmon resonance KW - photonic integrated circuit KW - direct optical detection KW - COVID-19 antibody KW - COVID-19 severity KW - pandemic surveillance KW - immunity monitoring Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16004 SN - 2673-8112 VL - 2 IS - 3 SP - 389 EP - 397 PB - MDPI ER -