Effects of non-pharmaceutical interventions on the burden of respiratory infections during and after the pandemic
The medium- and long-term effects of nonpharmaceutical interventions (NPIs) used during the COVID-19 pandemic on respiratory infections such as respiratory syncytial virus, influenza, and pneumococcal disease remain poorly understood. However, they play a critical role in assessing the total burden of disease according to NPIs.
Our goal is to develop an integrated model to simulate the transmission of various respiratory infections and the collateral effects of NPIs on their medium- and long-term disease burden.
The RESPINOW project consists of four subprojects working together to create an integrated model to simulate the transmission of various respiratory infections and to better understand the post-pandemic dynamics of these infections.
Subproject 1 - Evidence Synthesis
Harmonizing available data to synthesize a global dataset of immunity markers, infection prevalence, and burden of disease from respiratory infections during and after NPIs.
Subproject 2 - Population-based surveys
Assessing dynamics of RSV, influenza, SARS-CoV-2 and pneumococcal immunity, contact patterns and adherence to NPIs in a well-characterized German population cohort of adults and children intra- and post-pandemically.
Subproject 3 - Integrated modelling
Modelling the spread of multiple pathogens in realistic environments to quantify the impact of specific interventions on the endemic equilibrium.
Subproject 4 - Short-term forecasts
Nowcasting and short-term prediction of multiple respiratory diseases in real time.
Dan, S., Chen, Y., Chen, Y., Monod, M., Jaeger, V.K., Bhatt, S., Karch, A., Ratmann, O. on behalf of the Machine Learning & Global Health network
Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model
Submitted.
Preprint – doi: 10.48550/ARXIV.2210.11358
Project details
Responsible persons
Project period
May 2022 - April 2025
Cooperation partners
Helmholtz Centre for Infection Research, Braunschweig (HZI)
University of Heidelberg
Max Planck Institute for Dynamics and Self-Organization, Göttingen
Karlsruhe Institute of Technology
Natural and medical science institute (NMI)
University of Cologne
University of Halle
Kaiserslautern University of Technology
Robert Koch Institute
Wroclaw University of Science and Technology
Funding
Federal Ministry of Education and Research (BMBF)
External links
Dan, S., Chen, Y., Chen, Y., Monod, M., Jaeger, V.K., Bhatt, S., Karch, A., Ratmann, O. on behalf of the Machine Learning & Global Health network
Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model
Submitted.
Preprint – doi: 10.48550/ARXIV.2210.11358