Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Monitoring Natural SARS-CoV-2 Infection in Lions (Panthera leo) at the Barcelona Zoo: Viral Dynamics and Host Responses.

To date, no evidence supports the fact that animals play a role in the epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus infectious disease 2019 (COVID-19). However, several animal species are naturally susceptible to SARS-CoV-2 infection. Besides pets (cats, dogs, Syrian hamsters, and ferrets) and farm animals (minks), different zoo animal species have tested positive for SARS-CoV-2 (large felids and non-human primates). After the summer of 2020, a second wave of SARS-CoV-2 infection occurred in Barcelona (Spain), reaching a peak of positive cases in November. During that period, four lions (Panthera leo) at the Barcelona Zoo and three caretakers developed respiratory signs and tested positive for the SARS-CoV-2 antigen. Lion infection was monitored for several weeks and nasal, fecal, saliva, and blood samples were taken at different time-points. SARS-CoV-2 RNA was detected in nasal samples from all studied lions and the viral RNA was detected up to two weeks after the initial viral positive test in three out of four animals. The SARS-CoV-2 genome was also detected in the feces of animals at different times. Virus isolation was successful only from respiratory samples of two lions at an early time-point. The four animals developed neutralizing antibodies after the infection that were detectable four months after the initial diagnosis. The partial SARS-CoV-2 genome sequence from one animal caretaker was identical to the sequences obtained from lions. Chronology of the events, the viral dynamics, and the genomic data support human-to-lion transmission as the origin of infection.