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.

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.

BackgroundPlasmodium knowlesiis a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of theAnophelesLeucosphyrus Group. The risk of humanP. knowlesiinfection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available onP. knowlesioccurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk ofP. knowlesimalaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.Methods & ResultsWe extend and apply an environmental niche modelling framework as implemented by a previous mapping study ofP. knowlesitransmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records ofP. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation onP. knowlesitransmission.DiscussionOur map ofP. knowlesirelative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority forP. knowlesisurveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.Author SummaryPlasmodium knowlesiis a parasite that can cause malaria when it infects humans. Although most people do not experience severe illness fromPlasmodium knowlesiinfection, a small number will develop serious or even fatal disease. The parasite is found naturally in some monkeys throughout Southeast Asia, and spreads from these monkeys to humans through mosquitoes. Previous research predicted where the risk of being infected is highest according to what we know about the environment across Southeast Asia, such as if there are forests in an area or if the altitude is high. In this work, we extend this previous research with more up-to-date data on environmental conditions and infections to predict the risk of being infected withPlasmodium knowlesi. We show that the riskPlasmodium knowlesiposes to humans is high across much of Southeast Asia, and that the disease will continue to challenge national goals to eliminate malaria.

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