Mapping the Host-Pathogen Space to Link Longitudinal and Cross-sectional Biomarker Data:LeptospiraInfection in California Sea Lions (Zalophus californianus) as a Case Study

Abstract

28 Confronted with the challenge of understanding population-level processes, disease 29 ecologists and epidemiologists often simplify quantitative data into distinct physiological 30 states (e.g. susceptible, exposed, infected, recovered). However, data defining these states 31 often fall along a spectrum rather than into clear categories. Hence, the host-pathogen 32 relationship is more accurately defined using quantitative data, often integrating multiple 33 diagnostic measures, just as clinicians do to assess their patients. We use quantitative data 34 on a bacterial infection (Leptospira interrogans) in California sea lions (Zalophus 35 californianus) to improve both our individual-level and population-level understanding of 36 this host-pathogen system. We create a "host-pathogen space" by mapping multiple 37 biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. 38 serum chemistry values) from 13 longitudinally sampled, severely ill individuals to 39 visualize and characterize changes in these values through time. We describe a clear, 40 unidirectional trajectory of disease and recovery within this host-pathogen space. 41 Remarkably, this trajectory also captures the broad patterns in larger cross-sectional 42 datasets of 1456 wild sea lions in all states of health. This mapping framework enables us 43 to determine an individual's location in their time-course since initial infection, and to 44 visualize the full range of clinical states and antibody responses induced by pathogen 45 exposure, including severe acute disease, chronic subclinical infection, and recovery. We 46 identify predictive relationships between biomarkers and outcomes such as survival and 47 pathogen shedding, and in certain cases we can impute values for missing data, thus 48 increasing the size of the useable dataset. Mapping the host-pathogen space and using 49 quantitative biomarker data provides more nuanced approaches for understanding and 4 50 modeling disease dynamics in a system, yielding benefits for the clinician who needs to 51 triage patients and prevent transmission, and for the disease ecologist or epidemiologist 52 wishing to develop appropriate risk management strategies and assess health impacts on a 53 population scale. 54 55 Author Summary 56 A pathogen can cause a range of disease severity across different host individuals, and 57 these presentations change over the time-course from infection to recovery. These facts 58 complicate the work of epidemiologists and disease ecologists seeking to understand the 59 factors governing disease spread, often working with cross-sectional data. Recognizing 60 these facts also highlights the shortcomings of classical approaches to modeling infectious 61 disease, which typically rely on discrete and well-defined disease states. Here we show that 62 by analyzing multiple biomarkers of health and infection simultaneously, treating these 63 values as quantitative rather than binary indicators, and including a modest amount of 64 longitudinal sampling ...

Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

“…At the level of individuals, the presence and concentration of antibodies against SARS-CoV-2 are indicators of past exposure, providing insights over a much wider temporal window than other metrics. When considered jointly with PCR testing to detect viral RNA, antibodies substantially improve the probability of detecting present and past infections ( Prager et al, 2019 ). This improvement is highly valuable because RNA detection is typically limited to a relatively brief period of infection, and because PCR sensitivity varies considerably with infection severity and biological sample type ( Azkur et al, 2020 ; Yongchen et al, 2020 ).…”

Quantifying antibody kinetics and RNA shedding during early-phase SARS-CoV-2 infection

“…At the level of individuals, the presence and concentration of antibodies against SARS-CoV-2 are indicators of past exposure, providing insights into incidence over a much wider temporal window than other metrics. When considered jointly with PCR testing to detect viral shedding, antibodies substantially improve the probability of detecting present and past infections (Prager et al 2019), which is highly valuable given that RNA shedding is typically limited to a relatively brief infectious period, and because PCR sensitivity varies considerably with infection severity and biological sample type (Yongchen et al 2020;Zhang et al 2020b). Assessment of the levels of different antibody types (e.g.…”