Speaker
Description
The SARS-CoV-2 pandemic kept the whole world in suspense for 3 years and is still ongoing. Yet, most countries returned back to normalcy, thanks to broadly available vaccines and less severe symptoms of currently circulating variants.
Nevertheless, the virus is continuously evolving and new variants of concern are emerging, which may develop mechanisms to escape the immune response or (re-)infect individuals with fatal impacts. Hence, the surveillance of SARS-CoV-2 infections is still an important instrument to handle local outbreaks.
However, the reported number of infected people highly depends on current test strategies and policies, which change over time, or like currently in many countries, are no longer given. Low reported case numbers may occur indeed as cause of few infections. But also a low testing coverage can be the reason, be it due to asymptomatic infections or flu- or cold-like symptoms, and outbreaks remain undetected.
Here, we present a method which allows the reconstruction of the true incidence history by utilising evolutionary signals inherent in genomic data of SARS-CoV-2.
The rationale is given by the speed at which the virus evolves on population level: Transmission happens in short time, usually a few days, entailing a limited duration of intra-patient evolution. This means, the number of infected individuals is reflected by the evolutionary signal.
Our recently developed workflow GInPipe takes viral sequences along with their collection date as input. Extracting evolutionary information, such as the number of haplotypes and mutants over time, allows us to approximate incidence correlates.
By incorporating the number of officially reported cases, we are able to determine the minimum number of infected people and an estimation of under-reported cases.
