Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1-53.9% in recall, 0-12.1% in precision and 0-38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006-0.143 in KL-divergence and 0.001-0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological sample and a real HIV-1 dataset and demonstrated better results than other methods available.
Background: COVID-19 results in persisting symptoms but there is little systematically collected data estimating recovery time following infection. Methods:We followed 94% of all COVID-19 cases diagnosed in the Australian state of New South Wales between January and May 2020 using 3-4 weekly telephone interviews and linkage to hospitalisation and death data to determine if they had recovered from COVID-19 based on symptom resolution. Proportional hazards models with competing risks were used to estimate time to recovery adjusted for age and gender.Findings: In analyses 2904 cases were followed for recovery (median follow-up time 16 days, range 1-122, IQR 11-24).There were 2572 (88.6%) who reported resolution of symptoms (262/2572 were also hospitalised), 224 (7.8%) had not recovered at last contact (28/224 were also hospitalised), 51 (1.8%) died of COVID-19, and 57 (2.0%) were hospitalised without a documented recovery date. Of those followed, 20% recovered by 10 days, 60% at 20, 80% at 30, 91% at 60, 93% at 90 and 96% at 120 days. Compared to those aged 30-49 years, those 0-29 years were more likely to recover (aHR 1.22, 95%CI 1.10-1.34) while those aged 50-69 and 70 + years were less likely to recover (aHR respectively 0.74, 95%CI 0.67-0.81 and 0.63, 95%CI 0.56-0.71). Men were faster to recover than women (aHR 1.20, 95%CI 1.11-1.29) and those with pre-existing co-morbidities took longer to recover than those without (aHR 0.90, 95%CI 0.83-0.98).Interpretation: In a setting where most cases of COVID-19 were ascertained and followed, 80% of those with COVID-19 recover within a month, but about 5% will continue to experience symptoms 3 months later.
Aim: To estimate the length of stay distributions of hospitalised COVID-19 cases during a mixed Omicron-Delta epidemic in New South Wales, Australia (16 Dec 2021 -- 7 Feb 2022), and compare these to estimates produced over a Delta-only epidemic in the same population (1 Jul 2021 -- 15 Dec 2022). Background: The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the `length of stay') is a key factor in determining how incident caseloads translate into health system burden as measured through ward and ICU occupancy. Results: Using data on the hospital stays of 19,574 individuals, we performed a competing-risk survival analysis of COVID-19 clinical progression. During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70+ respectively, 2.16 (95\% CI: 2.12--2.21), 3.93 (95\% CI: 3.78--4.07) and 7.61 days (95\% CI: 7.31--8.01), compared to 3.60 (95\% CI: 3.48--3.81), 5.78 (95\% CI: 5.59--5.99) and 12.31 days (95\% CI: 11.75--12.95) across the preceding Delta epidemic (15 Jul 2021 -- 15 Dec 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95\% CI: 1.80--2.30), 2.92 (95\% CI: 2.50--3.67) and 6.02 days (95\% CI: 4.91--7.01) for the same age groups.
Assessing biodiversity is an important step in the study of microbial ecology associated with a given environment. Multiple indices have been used to quantify species diversity, which is a key biodiversity measure. Measuring species diversity of viruses in different environments remains a challenge relative to measuring the diversity of other microbial communities. Metagenomics has played an important role in elucidating viral diversity by conducting metavirome studies; however, metavirome data are of high complexity requiring robust data preprocessing and analysis methods. In this review, existing bioinformatics methods for measuring species diversity using metavirome data are categorised broadly as either sequence similarity-dependent methods or sequence similarity-independent methods. The former includes a comparison of DNA fragments or assemblies generated in the experiment against reference databases for quantifying species diversity, whereas estimates from the latter are independent of the knowledge of existing sequence data. Current methods and tools are discussed in detail, including their applications and limitations. Drawbacks of the state-of-the-art method are demonstrated through results from a simulation. In addition, alternative approaches are proposed to overcome the challenges in estimating species diversity measures using metavirome data.
Summary Hepatitis A incidence has declined in most countries through a combination of prevention measures, augmented through the use of a highly effective vaccine. In Australia, the proportion of the population susceptible to hepatitis A infection has declined over time due to high rates of opportunistic vaccination as well as the sustained inflow of seropositive immigrants from high‐endemicity countries. These factors have contributed to a rapid decline in incidence. An age‐structured hepatitis A transmission model incorporating demographic changes was fitted to seroprevalence and disease notification data and used to project incidence trends and transmission potential for hepatitis A in the general population. Robustness of findings was assessed through worst‐case scenarios regarding vaccine uptake, migration and the duration of immunity. The decline in age‐specific seroprevalence until the introduction of hepatitis A vaccine in 1994 was well explained through a declining basic reproduction number (R0) that remained >1. Accounting for existing immunity, we estimated that the effective reproduction number (Reff) <1 in the general population of Australia since the early 1990s, declining more rapidly after the introduction of the hepatitis A vaccine. Future projections under a variety of scenarios support Reff remaining <1 with continued low incidence in the general population. In conclusion, our results suggest that sustained endemic transmission in the general Australian population is no longer possible although risks of sporadic outbreaks remain. This suggests potential for local elimination of hepatitis A infection in Australia, provided that elimination criteria can be defined and satisfied in risk groups. The methodology used here to investigate elimination potential can easily be replicated in settings such as in the USA where sequential seroprevalence studies are supported by routine notification data.
Background The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the ‘length of stay’) is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant. Methods Using data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression. Results During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0–39, 40–69 and 70 +, respectively, 2.16 (95% CI: 2.12–2.21), 3.93 (95% CI: 3.78–4.07) and 7.61 days (95% CI: 7.31–8.01), compared to 3.60 (95% CI: 3.48–3.81), 5.78 (95% CI: 5.59–5.99) and 12.31 days (95% CI: 11.75–12.95) across the preceding Delta epidemic (1 July 2021–15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80–2.30), 2.92 (95% CI: 2.50–3.67) and 6.02 days (95% CI: 4.91–7.01) for the same age groups. Conclusions Hospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response.
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