The Seasonality of Respiratory Syncytial Virus in Western Australia Prior to Implementation of SARS-CoV-2 Non-Pharmaceutical Interventions (preprint)

Background: Respiratory syncytial virus (RSV) seasonality is dependent on the local climate. We assessed the stability of RSV seasonality prior to the SARS-CoV-2 pandemic in Western Australia (WA), a state spanning temperate and tropical regions. Method RSV laboratory testing data were collected from January 2012 to December 2019. WA was divided into three regions determined by population density and climate; Metropolitan, Northern and Southern. Season threshold was calculated per region at 1.2% annual cases, with onset the first of ≥2 weeks above this threshold and offset as the last week before ≥2 weeks below. Results The incidence of RSV in WA was 6.3/10,000. The Northern region had the highest incidence (15/10,000), more than 2.5 times the Metropolitan region (IRR 2.7; 95% CI, 2.6-2.9). Test percentage positive was similar in the Metropolitan (8.6%) and Southern (8.7%) regions, with the lowest in the Northern region (8.1%). RSV seasons in the Metropolitan and Southern regions occurred annually, with a single peak and had consistent timing and intensity. The Northern tropical region did not experience a distinct season. Proportion of RSV A to RSV B in the Northern region differed from the Metropolitan region in 5 of the 8 years studied. Conclusions Incidence of RSV in WA is high, especially in the Northern region, where climate, an expanded at-risk population, and increased testing may have contributed to greater numbers. Before the SARS-CoV-2 pandemic, RSV seasonality WA was consistent in timing and intensity for the Metropolitan and Southern regions.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts (preprint)

BackgroundCOVID-19 is a new multi-organ disease, caused by the SARS-CoV-2 virus, resulting in considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. A better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have been developed for its pathophysiology. The viruss rapid and extensive spread and therapeutic responses made this particularly difficult. Initially, no large patient datasets were publicly available, and their data remains limited. The medical literature was flooded with unfiltered, technical and sometimes conflicting pre-review reports. Clinicians in many countries had little time for academic consultations, and in-person meetings were unsafe. Methods and FindingsIn early 2020, we began a major project to develop causal models of the pathophysiological processes underlying the diseases clinical manifestations. We used Bayesian network (BN) models, because they provide both powerful tools for calculation and clear maps of probabilistic causal influence between semantically meaningful variables, as directed acyclic graphs (DAGs). Hence, they can incorporate expert opinion and numerical data, and produce explainable results. Dynamic causal BNs, which represent successive "time slices" of the system, can capture feedback loops and long-term disease progression. To obtain the likely causal structures, we used extensive elicitation of expert opinion in structured online sessions. Centered in Australia, with its exceptionally low COVID-19 burden, we managed to obtain many consultation hours. Groups of clinical and other subject matter specialists, all independent volunteers, were enlisted to filter, interpret and discuss the literature and develop a current consensus. We aimed to capture the experts understanding, so we encouraged discussion and inclusion of theoretically salient latent (i.e., unobservable) variables, documented supporting literature while noting controversies, and allowed experts to propose mechanisms by extrapolation from other diseases. Intermediary experts with some combined expertise facilitated the exchange of knowledge to BN modelers and vice versa. Our method was iterative and incremental: we systematically refined and checked the group output with one-on-one follow-up meetings with the original and new experts to validate previous results. In total, 35 experts contributed 126 face-to-face hours, and could review our products. ConclusionsOur method demonstrates and describes an improved procedure for developing BNs via expert elicitation, which can be implemented rapidly by other teams modeling emergent complex phenomena. The results presented are two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology, with three anticipated applications: (i) making expert knowledge freely available in a readily understandable and updatable form; (ii) guiding design and analysis of observational and clinical studies, by identifying potential mediators, confounders, and modifiers of treatment effects; (iii) developing and validating parameterized automated tools for causal reasoning and decision support, in clinical and policy settings. We are currently developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

Off-season RSV epidemics in Australia after easing of COVID-19 restrictions (preprint)

Human respiratory syncytial virus (RSV) is an important cause of acute respiratory infection (ARI) with the most severe disease in the young and elderly. Non-pharmaceutical interventions (NPIs) and travel restrictions for controlling COVID-19 have impacted the circulation of most respiratory viruses including RSV globally, particularly in Australia, where during 2020 the normal winter epidemics were notably absent. However, in late 2020, unprecedented widespread RSV outbreaks occurred, beginning in spring, and extending into summer across two widely separated states of Australia, Western Australia (WA) and New South Wales (NSW) including the Australian Capital Territory (ACT). Genome sequencing revealed a significant reduction in RSV genetic diversity following COVID-19 emergence except for two genetically distinct RSV-A clades. These clades circulated cryptically, likely localized for several months prior to an epidemic surge in cases upon relaxation of COVID-19 control measures. The NSW/ACT clade subsequently spread to the neighbouring state of Victoria (VIC) and caused extensive outbreaks and hospitalisations in early 2021. These findings highlight the need for continued surveillance and sequencing of RSV and other respiratory viruses during and after the COVID-19 pandemic as mitigation measures introduced may result in unusual seasonality, along with larger or more severe outbreaks in the future.

Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling (preprint)

Background In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network (BN) models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real world predictive value of individual RT-PCR results. Methods We elicited knowledge from domain experts to describe the test process from viral exposure to interpretation of the laboratory test, through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions. Results Causal relationships elicited describe the interactions of multiple variables and their impact on a RT-PCR result. Some interactions are infrequently observable and accounted for across the testing cycle such as pre-testing factors, sample collector experience and RT-PCR platform. By setting the input variables as evidence for a given subject and preliminary parameterisation, three scenarios were simulated to demonstrate potential uses of the model. Conclusions The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a persons true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results.