Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts.

The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment. This spatial and temporal distribution of shed pathogen has been hypothesised to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyse three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasises the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.

Pathogen spillover driven by rapid changes in bat ecology.

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behaviour and viral dynamics. We present 25 years of data on land-use change, bat behaviour and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviours that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of the 25 years. Our long-term study identifies the mechanistic connections between habitat loss, climate and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics.

Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics.

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932-45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.

Severe Acute Respiratory Syndrome Coronavirus 2 Is Detected in the Gastrointestinal Tract of Asymptomatic Endoscopy Patients but Is Unlikely to Pose a Significant Risk to Healthcare Personnel.

Background and Aims: Recent evidence suggests that the gut is an additional target for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, whether SARS-CoV-2 spreads via gastrointestinal secretions remains unclear. To determine the prevalence of gastrointestinal SARS-CoV-2 infection in asymptomatic subjects, we analyzed gastrointestinal biopsy and liquid samples from endoscopy patients for the presence of SARS-CoV-2. Methods: We enrolled 100 endoscopic patients without known SARS-CoV-2 infection (cohort A) and 12 patients with a previous COVID-19 diagnosis (cohort B) in a cohort study performed at a regional hospital. Gastrointestinal biopsies and fluids were screened for SARS-CoV-2 by polymerase chain reaction (PCR), immunohistochemistry, and virus isolation assay, and the stability of SARS-CoV-2 in gastrointestinal liquids in vitro was analyzed. Results: SARS-CoV-2 ribonucleic acid was detected by PCR in the colonic tissue of 1/100 patients in cohort A. In cohort B, 3 colonic liquid samples tested positive for SARS-CoV-2 by PCR and viral nucleocapsid protein was detected in the epithelium of the respective biopsy samples. However, no infectious virions were recovered from any samples. In vitro exposure of SARS-CoV-2 to colonic liquid led to a 4-log-fold reduction of infectious SARS-CoV-2 within 1 hour (P ≤ .05). Conclusion: Overall, the persistent detection of SARS-CoV-2 in endoscopy samples after resolution of COVID-19 points to the gut as a long-term reservoir for SARS-CoV-2. Since no infectious virions were recovered and SARS-CoV-2 was rapidly inactivated in the presence of colon liquids, it is unlikely that performing endoscopic procedures is associated with a significant infection risk due to undiagnosed asymptomatic or persistent gastrointestinal SARS-CoV-2 infections.

Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.

The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.