Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data.

We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.

Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data.

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.

The origin and early spread of SARS-CoV-2 in Europe.

The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures.

Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients.

This study coupled a landscape-scale metagenomic survey of denitrification gene abundance in soils with in situ denitrification measurements to show how environmental factors shape distinct denitrification communities that exhibit varying denitrification activity. Across a hydrologic gradient, the distribution of total denitrification genes (nap/nar + nirK/nirS + cNor/qNor + nosZ) inferred from metagenomic read abundance exhibited no consistent patterns. However, when genes were considered independently, nirS, cNor and nosZ read abundance was positively associated with areas of higher soil moisture, higher nitrate and higher annual denitrification rates, whereas nirK and qNor read abundance was negatively associated with these factors. These results suggest that environmental conditions, in particular soil moisture and nitrate, select for distinct denitrification communities that are characterized by differential abundance of genes encoding apparently functionally redundant proteins. In contrast, taxonomic analysis did not identify notable variability in denitrifying community composition across sites. While the capacity to denitrify was ubiquitous across sites, denitrification genes with higher energetic costs, such as nirS and cNor, appear to confer a selective advantage in microbial communities experiencing more frequent soil saturation and greater nitrate inputs. This study suggests metagenomics can help identify denitrification hotspots that could be protected or enhanced to treat non-point source nitrogen pollution.

Greenhouse Gas Emissions from Septic Systems in New York State.

Onsite septic systems use microbial processes to eliminate organic wastes and nutrients such as nitrogen; these processes can contribute to air pollution through the release of greenhouse gases (GHGs). Current USEPA estimates for septic system GHG emissions are based on one study conducted in north-central California and are limited to methane; therefore, the contribution of these systems to the overall GHG emission budget is unclear. This study quantified and compared septic system GHG emissions from the soil over leach fields and the roof vent, which are the most likely locations for gas emissions during normal septic system operation. At each of eight septic systems, we measured fluxes of CH, CO, and NO using a static chamber method. The roof vent released the majority of septic system gas emissions. In addition, the leach field was a significant source of NO fluxes. Comparisons between leach field and vent emissions suggest that biological processes in the leach field soil may influence the type and quantity of gas released. Overall, our results suggest that (i) revisions are needed in USEPA guidance (e.g., septic systems are not currently listed as a source of NO emissions) and (ii) similar studies representing a wider range of climatic and geographic settings are needed. The total vent, sand filter, and leach field GHG emissions were 0.17, 0.045, and 0.050 t CO-equivalents capita yr, respectively. In total, this represents about 1.5% of the annual carbon footprint of an individual living in the United States.