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Summary: Background: Pairwise single nucleotide polymorphisms (SNPs) are a cornerstone of genomic approaches to the inference of transmission of multidrug-resistant (MDR) organisms in hospitals. However, the impact of many key analytical approaches on these inferences has not yet been systematically assessed. This study aims to make such a systematic assessment. Methods: We conducted a 15-month prospective study (2-month pilot phase, 13-month implementation phase), across four hospital networks including eight hospitals in Melbourne, VIC, Australia. Patient clinical and screening samples containing one or more isolates of meticillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus faecium, and extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae were collected and underwent whole genome sequencing. Using the genome data from the top four most numerous sequence types from each species, 16 in total, we systematically assessed the: (1) impact of sample and reference genome diversity through multiple core genome alignments using different data subsets and reference genomes, (2) effect of masking of prophage and regions of recombination in the core genome alignments by assessing SNP distances before and after masking, (3) differences between a cumulative versus a 3-month sliding-window approach to sample genome inclusion in the dataset over time, and (4) the comparative effects each of these approaches had when applying a previously defined SNP threshold for inferring likely transmission. Findings: 2275 samples were collected (397 during the pilot phase from April 4 to June 18, 2017; 1878 during the implementation phase from Oct 30, 2017, to Nov 30, 2018) from 1870 patients. Of these 2275 samples, 1537 were identified as arising from the four most numerous sequence types from each of the four target species of MDR organisms in this dataset (16 sequence types in total: S aureus ST5, ST22, ST45, and ST93; E faecium ST80, ST203, ST1421, and ST1424; K pneumoniae ST15, ST17, ST307, and ST323; and E coli ST38, ST131, ST648, and ST1193). Across the species, using a reference genome of the same sequence type provided a greater degree of pairwise SNP resolution, compared with species and outgroup-reference alignments that mostly resulted in inflated SNP distances and the possibility of missed transmission events. Omitting prophage regions had minimal effect; however, omitting recombination regions had a highly variable effect, often inflating the number of closely related pairs. Estimated SNP distances between isolate pairs over time were more consistent using a sliding-window than a cumulative approach. Interpretation: We propose that the use of a closely related reference genome, without masking of prophage or recombination regions, and of a sliding-window approach for isolate inclusion is best for accurate and consistent MDR organism transmission inference, when using core genome alignments and SNP thresholds. These approaches provide increased stability and resolution, so SNP thresholds can be more reliably applied for putative transmission inference among diverse MDR organisms, reducing the chance of incorrectly inferring the presence or absence of close genetic relatedness and, therefore, transmission. The establishment of a broadly applicable and standardised approach, as proposed here, is necessary to implement widespread prospective genomic surveillance for MDR organism transmission. Funding: Melbourne Genomics Health Alliance, and National Health and Medical Research Council of Australia.

Summary: Background: A cornerstone of Australia's ability to control COVID-19 has been effective border control with an extensive supervised quarantine programme. However, a rapid recrudescence of COVID-19 was observed in the state of Victoria in June, 2020. We aim to describe the genomic findings that located the source of this second wave and show the role of genomic epidemiology in the successful elimination of COVID-19 for a second time in Australia. Methods: In this observational, genomic epidemiological study, we did genomic sequencing of all laboratory-confirmed cases of COVID-19 diagnosed in Victoria, Australia between Jan 25, 2020, and Jan 31, 2021. We did phylogenetic analyses, genomic cluster discovery, and integrated results with epidemiological data (detailed information on demographics, risk factors, and exposure) collected via interview by the Victorian Government Department of Health. Genomic transmission networks were used to group multiple genomic clusters when epidemiological and genomic data suggested they arose from a single importation event and diversified within Victoria. To identify transmission of emergent lineages between Victoria and other states or territories in Australia, all publicly available SARS-CoV-2 sequences uploaded before Feb 11, 2021, were obtained from the national sequence sharing programme AusTrakka, and epidemiological data were obtained from the submitting laboratories. We did phylodynamic analyses to estimate the growth rate, doubling time, and number of days from the first local infection to the collection of the first sequenced genome for the dominant local cluster, and compared our growth estimates to previously published estimates from a similar growth phase of lineage B.1.1.7 (also known as the Alpha variant) in the UK. Findings: Between Jan 25, 2020, and Jan 31, 2021, there were 20 451 laboratory-confirmed cases of COVID-19 in Victoria, Australia, of which 15 431 were submitted for sequencing, and 11 711 met all quality control metrics and were included in our analysis. We identified 595 genomic clusters, with a median of five cases per cluster (IQR 2–11). Overall, samples from 11 503 (98·2%) of 11 711 cases clustered with another sample in Victoria, either within a genomic cluster or transmission network. Genomic analysis revealed that 10 426 cases, including 10 416 (98·4%) of 10 584 locally acquired cases, diagnosed during the second wave (between June and October, 2020) were derived from a single incursion from hotel quarantine, with the outbreak lineage (transmission network G, lineage D.2) rapidly detected in other Australian states and territories. Phylodynamic analyses indicated that the epidemic growth rate of the outbreak lineage in Victoria during the initial growth phase (samples collected between June 4 and July 9, 2020; 47·4 putative transmission events, per branch, per year [1/years; 95% credible interval 26·0–85·0]), was similar to that of other reported variants, such as B.1.1.7 in the UK (mean approximately 71·5 1/years). Strict interventions were implemented, and the outbreak lineage has not been detected in Australia since Oct 29, 2020. Subsequent cases represented independent international or interstate introductions, with limited local spread. Interpretation: Our study highlights how rapid escalation of clonal outbreaks can occur from a single incursion. However, strict quarantine measures and decisive public health responses to emergent cases are effective, even with high epidemic growth rates. Real-time genomic surveillance can alter the way in which public health agencies view and respond to COVID-19 outbreaks. Funding: The Victorian Government, the National Health and Medical Research Council Australia, and the Medical Research Future Fund.

Malarial and other haemosporidian parasites are widespread; however, their temporal dynamics are ill-understood. Longitudinal sampling of a threatened riparian bird revealed a consistently very low prevalence over 13 years (∼5%) despite infections persisting and prevalence increasing with age. In contrast, three key species within this tropical community were highly infected (∼20–75% prevalence) and these differences were stable. Although we found novel lineages and phylogenetic structure at the local level, there was little geographic structuring within Australasia. This study suggests that malarial parasite susceptibility is determined by host factors and that species can maintain low levels despite high community prevalence. Keywords: Avian malaria, Wildlife diseases, Purple-crowned fairy-wrens, Haemoproteus, Plasmodium