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During the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent-something that is unknown at the time of sequencing-and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses., Competing Interests: The authors declare no conflict of interest.

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020-the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland's border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland's strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.

In many systems, host-parasite evolutionary dynamics have led to the emergence and maintenance of diverse parasite and host genotypes within the same population. Genotypes vary in key attributes: Parasite genotypes vary in ability to infect, host genotypes vary in susceptibility, and infection outcome is frequently the result of both parties' genotypic identities. These host-parasite genotype-by-genotype (GH × GP) interactions influence evolutionary and ecological dynamics in important ways. Interactions can be produced through genetic variation; however, here, we assess the role of variable gene expression as an additional source of GH × GP interactions. The bumblebee Bombus terrestris and its trypanosome gut parasite Crithidia bombi are a model system for host-parasite matching. Full-transcriptome sequencing of the bumblebee host revealed that different parasite genotypes indeed induce fundamentally different host expression responses and host genotypes vary in their responses to the infecting parasite genotype. It appears that broadly and successfully infecting parasite genotypes lead to reduced host immune gene expression relative to unexposed bees but induce the expression of genes responsible for controlling gene expression. Contrastingly, a poorly infecting parasite genotype induced the expression of immunologically important genes, including antimicrobial peptides. A targeted expression assay confirmed the transcriptome results and also revealed strong host genotype effects. In all, the expression of a number of genes depends on the host genotype and the parasite genotype and the interaction between both host and parasite genotypes. These results suggest that alongside sequence variation in coding immunological genes, variation that controls immune gene expression can also produce patterns of host-parasite specificity.