Real-time Dissection and Forecast of Infection Dynamics during a Pandemic

medRxiv (Cold Spring Harbor Laboratory)(2023)

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Abstract
Pandemic preparedness requires institutions, including public health authorities and governments, to detect, survey and control outbreaks. To maintain an accurate, quantitative and up-to-date picture of an epidemic crisis is key. For SARS-CoV-2, this was mostly achieved by ascertaining incidence numbers and the effective reproductive number ( R eff), which counts how many people an infected person is likely to infect on average. These numbers give strong hints on past infection dynamics in a population but fail to clearly characterize current and future dynamics as well as potential effects of pharmaceutical and non-pharmaceutical interventions. We show that, by using and combining infection surveillance and population-scale contact statistics, we can obtain a better understanding of the drivers of epidemic waves and the effectiveness of interventions. This approach can provide a real-time picture, thus saving not only many lives by quickly allowing adaptation of the health policies but also alleviating economic and other burdens if an intervention proves ineffective. We factorize R eff into contacts and relative transmissibility: Both signals can be used, individually and combined, to identify driving forces of an epidemic, monitoring and assessing interventions, as well as projecting an epidemic’s future trajectory. Using data for SARS-CoV-2 and Influenza from 2019 onward in Germany, we provide evidence for the usefulness of our approach. In particular, we find that the effects from physical distancing and lockdowns as well as vaccination campaigns are dominant. ### Competing Interest Statement This work was supported by grants from the Federal Government of Germany through the Federal Ministry for Economic Affairs and Climate Action (BMWK) for the project DAKI-FWS (01MK21009A) and the Federal Ministry of Education and Research (BMBF) for the project OptimAgent (031L0299). S.S., R.P., C.K., and S.R. are employees of NET CHECK GmbH. D.Z. is owner and CEO of NET CHECK GmbH. F.W.C. is a paid consultant to Whitespace Solutions, Ltd. ### Funding Statement This work was supported by grants from the Federal Government of Germany through the Federal Ministry for Economic Affairs and Climate Action (BMWK) for the project DAKI-FWS (01MK21009A) and the Federal Ministry of Education and Research (BMBF) for the project OptimAgent (031L0299). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data collection and usage in this work was reviewed and approved by law firm GvW Graf von Westphalen ([gvw.com][1]), deemed compliant with regulations under Federal German Law with regard to protection of privacy and personal information (DSGVO). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All aggregated data produced in the present study (i.e., anonymized daily contact networks, daily participant and sample numbers) are available upon reasonable request to the authors. Official infection surveillance, vaccine coverage, and virus variant data for Germany used in this work are publicly available from the Robert Koch Institute (see references in the manuscript). [1]: http://gvw.com
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Key words
infection dynamics,pandemic,dissection,real-time
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