Presentation of the project at ICSB 2022

INSIDe: Integrative modeling of the spread of serious infectious diseases
PRESENTER: Vanessa Nakonecnij
ABSTRACT: The modeling of the spread of SARS-CoV 2 and in particular of local outbreaks has been crucial for analyzing the pandemic and its guiding policy. Building surveillance models and pipelines with reliable forecasts is crucial for the prevention of future infectious disease outbreaks. Yet, the insights and forecasts provided by existing models are necessarily limited by their resolution and the data used for inference. Models which are based solely on a single data source, like reported numbers of new infections, might have biased forecasts. Case reports might not be representative, whereas serological studies are costly and might have poor time-resolution. Wastewater monitoring, however, has proven to serve as an early indicator for the rise of reported infections and hospitalizations. The current challenge is the integration of different data sources and the interconnection of their respective modeling and simulation frameworks. We will address this with a modular, open-source platform allowing for: (i) the assembly and simulation of complex models consisting of multiple submodels (ii) the data-driven inference of unknown model parameters (e.g. the effect of NPIs) and the design of observation (testing) strategies. The INSIDe platform will combine three state-of-the-art software frameworks: ++SYSTEMS for the fine-grained simulation of flow patterns in wastewater systems, MEmilio for the simulation of the spatio-temporal spread of infectious diseases and pyABC for data-driven modeling of multi-scale processes. Combining these frameworks, we can facilitate the integration of different information. Integrative modeling will improve the assessment of the current state of epi-/pandemics, achieve more robust and reliable predictions, reduce uncertainty and thus allow decision makers to employ more precise non-pharmaceutical interventions (NPIs) to prevent outbreaks of a disease.