Title: Data from: Periodic shifts in viral load increase risk of spillover from bats Authors: Tamika J. Lunn (https://orcid.org/0000-0003-4439-2045), University of Georgia, Athens, US Benny Borremans (https://orcid.org/0000-0002-7779-4107), Wildlife Health Ecology Research Organization, US Devin N Slobodian (nee Jones) (https://orcid.org/0000-0001-9215-2930), Montana State University, US Maureen K. Kessler (https://orcid.org/0000-0001-5380-5281), University of Michigan–Ann Arbor: Ann Arbor, US Adrienne S. Dale (https://orcid.org/0000-0001-8991-4308, Texas Tech University, US Claude Kwe Yinda (https://orcid.org/0000-0002-5195-5478), NIAID Rocky Mountain Laboratories: Hamilton, US Manuel Ruiz-Aravena (https://orcid.org/0000-0001-8463-7858), Mississippi State University, US Caylee A Falvo (https://orcid.org/0000-0002-1520-137X), Cornell University, US Daniel Crowley (https://orcid.org/0000-0003-4262-253X), Cornell University, US James O. Lloyd-Smith (https://orcid.org/0000-0001-7941-502X), University of California, US Vincent J. Munster (https://orcid.org/0000-0002-2288-3196), NIAID Rocky Mountain Laboratories: Hamilton, US Peggy Eby (https://orcid.org/0000-0001-5441-2682), University of New South Wales Hamish McCallum (https://orcid.org/0000-0002-3493-0412), Griffith University, AU Peter Hudson (https://orcid.org/0000-0003-0468-3403), Penn State, US Olivier Restif (https://orcid.org/0000-0001-9158-853X), University of Cambridge, UK Liam P. McGuire (https://orcid.org/0000-0002-5690-0804), University of Waterloo, CA Ina L. Smith (https://orcid.org/0000-0001-5807-3737), Health and Biosecurity CSIRO Black Mountain Laboratories, AU Raina K. Plowright (https://orcid.org/0000-0002-3338-6590), Cornell University,US Alison J. Peel (https://orcid.org/0000-0003-3538-3550), University of Sydney, US Abstract: Prediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding in Pteropus bats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load), into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone. Keywords: flying fox; Henipavirus; Pteropodidae; viral load distribution; detection limit; fruit bat; RT-PCR; test; viral replication Funding information 416The project was supported by the National Science Foundation (DEB1716698, EF-4172133763), and the DARPA PREEMPT program Cooperative Agreement #418D18AC00031. The content of the information does not necessarily reflect the position 419or the policy of the U.S. government, and no official endorsement should be inferred. 420TJL was supported by an Endeavour Postgraduate Leadership Award and a Research 421Training Program scholarship sponsored by the Australian Government. AJP was 422supported by an ARC DECRA fellowship (DE190100710), and a Queensland 423Government Accelerate Postdoctoral Research Fellowship. VJM and KCY Are 424supported by the Division of Intramural Research of NIAID. MKK was supported by 425the Fulbright U.S. Student Program, which is sponsored by the U.S. Department of 426State and the Australian-American Fulbright Commission. GENERAL INFORMATION Date of data collection: from 2017-2020 Geographic location of data collection: The study was conducted in the subtropics of eastern Australia, south-east Queensland to north-east New South Wales. Information about funding sources that supported the collection of the data: The project was supported by the National Science Foundation (DEB1716698, EF-4172133763), and the DARPA PREEMPT program Cooperative Agreement #418D18AC00031. TJL was supported by an Endeavour Postgraduate Leadership Award and a Research Training Program scholarship sponsored by the Australian Government. AJP was supported by an ARC DECRA fellowship (DE190100710) and a Queensland Government Accelerate Postdoctoral Research Fellowship. VJM and KCY Are supported by the Division of Intramural Research of NIAID. MKK was supported by the Fulbright U.S. Student Program, which is sponsored by the U.S. Department of State and the Australian-American Fulbright Commission SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: This dataset is shared under an Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/). The material can be shared and built upon, but attribution to the original authors and a statement of changes made is required. Recommended citation for this dataset: Lunn, Tamika, Borremans, Benny, Jones, Devin N. et al. 2025. Data from: Periodic shifts in viral load increase risk of spillover from bats. [Dataset]. Cornell University Library eCommons Repository. https://doi.org/10.7298/dh0d-yc86 Related R Code: Lunn, Tamika and Borrenams, Benny. 2025. Code from: Periodic shifts in viral load increase risk of spillover from bats. [Software]. Cornell University Library eCommons Repository. https://doi.org/10.7298/ga50-xw35 Publications that cite or use the data: Lunn, T. J., Borremans, B., Jones, D. N., Kessler, M. K., Dale, A. S., Yinda, K. C., Ruiz-Aravena, M., Falvo, C. A., Crowley, D., Lloyd-Smith, J. O., Munster, V. J., Eby, P., McCallum, H., Hudson, P., Restif, O., McGuire, L. P., Smith, I. L., Bat One Health Group, Plowright, R. K., & Peel, A. J. (2023). Periodic shifts in viral load increase risk of spillover from bats. bioRxiv, 2023.09.06.556454. https://doi.org/10.1101/2023.09.06.556454 The above manuscript and its associated supplementary file was deposited as a preprint to bioRxiv under the following licensing information: CC-BY-NC-ND (Anyone can share this material, provided it remains unaltered in any way, this is not done for commercial purposes, and the original authors are credited and cited). Links to other publicly accessible locations of the data: Links/relationships to ancillary data sets: Was data derived from another source? If yes, list source(s): NO DATA & FILE OVERVIEW Note: Each dataset (.csv file) is separated by tab ('\') File List: Data are stored according to the following folder structure: raw_data: raw data that have been processed continuous_ur_s.csv Ct-to-genome-convert_HeV.csv Ct vs isolation all Ina and preempt.csv spillover.csv urv_data.csv processed_data: results of permutation analysis obs_out_data obs.out.20230620.csv obs.out.stats.gam.binomial.20230620.csv obs.out.stats.gam.poisson.20230620.csv obs.out.stats.glm.binomial.20230620.csv obs.out.stats.glm.poisson.20230620.csv perm_out_data perm.out.20230620.csv perm.out.stats.gam.binomial.20230620.csv perm.out.stats.gam.poisson.20230620.csv perm.out.stats.glm.binomial.20230620.csv perm.out.stats.glm.poisson.20230620.csv codes: R codes used to process and analyze data 00_Lunn et al 2023_20230927.R Relationship between files, if important: Additional related data collected that was not included in the current data package: Are there multiple versions of the dataset? NO If yes, name of file(s) that was updated: Why was the file updated? When was the file updated? METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: This study was conducted from July 2017 through September 2020 at five roosts spanning south-east Queensland to north-east New South Wales. Sampling of roosts occurred approximately monthly, with most sampled within the same week each month.For analyses performed on contemporaneous monthly sessions across aggregated sites, clusters of sessions were defined such that all sessions within the cluster were within 14 days of each other. At each site, we collected pooled urine samples from beneath roosting trees using plastic sheets (0.9 x 1.3 meters) distributed before sunrise (~4AM to 6AM Australian Eastern Standard Time). Sample collection began once bats had returned to the roost and concluded within 6 hours. Urine was not collected from sheets if the urine had completely evaporated or was contaminated with feces. Rain-affected sampling events were discarded and rescheduled. Methods for processing the data: We pooled urine on each sheet into a single sample (roughly 20 urine droplets to a volume of ~2ml), and aliquoted up to three cryovials for Hendra virus testing containing: AVL buffer (Qiagen) (target 140 µl of urine into 560 µl buffer), viral transport medium (VTM; Munster et al. 2009) (between 200-1000 µl of urine into 1000µl of VTM), and no buffer. Samples were transported in a CryoShipper (<-80°C) and stored at -80°C. An average of 40 pooled samples were collected per session. Instrument- or software-specific information needed to interpret the data: R Core Team (R version 4.2.2). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. --Packages----------------------- package * version tidyverse v2.0.0 bromm v1.0.3 ggplot2 v3.4.1 ggpubr v0.6.0 cowplot v1.1.1 RColorBrewer v1.1-3 patchwork v1.1.2 Standards and calibration information, if appropriate: Environmental/experimental conditions: Describe any quality-assurance procedures performed on the data: People involved with sample collection, processing, analysis and/or submission: DATA-SPECIFIC INFORMATION FOR: continuous_ur_s.csv Number of variables: 5 Number of cases/rows: 6 Variable List: site : text latitude : float longitude : float order : integer site_ordered: text site_code : text Missing data codes: Specialized formats or other abbreviations used: site_code: "RED"= Redcliffe "SUN"= Sunnybank "TOW"= Toowoomba "BUR"= Burleigh DATA-SPECIFIC INFORMATION FOR: spillover.csv Number of variables: 5 Number of cases/rows: 5 Variable List: all_spillovers_ID : text date_of_death : Date dates_for_plots : Date location_regionalcentre: text state : text Missing data codes: Specialized formats or other abbreviations used: Date = YYYY:MM:DD DATA-SPECIFIC INFORMATION FOR: urv_data.csv Number of variables: 15 Number of cases/rows: 6151 Variable List: site : text site_code : text date : text sampling_cluster : integer sampling_cluster_median_date: Date sampling_group_month : text sampling_group_year : text accession_update : text sample_id : text ct_hev : float Nml_hev : float preservative : text bff_present_update : Factor nbff_over_sheet : float nnonbff_over_sheet : float Missing data codes: NA Specialized formats or other abbreviations used: preservative: AVL: AVL buffer (Qiagen) VTM: viral transport Medium NB: No buffer DATA-SPECIFIC INFORMATION FOR: Ct-to-genome-convert_HeV.csv Number of variables: 4 Number of cases/rows: 20 Variable List: hen_bin: integer ct_hev: integer Nrx_hev: float Nml_hev: float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: Ct vs isolation all Ina and preempt.csv Number of variables: 4 Number of cases/rows: 239 Variable List: Dataset: text Ct: float isolation: integer species: text Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: obs.out.20230620.csv Number of variables: 5 Number of cases/rows: 585 Variable List: sampling_cluster_median_date: text hen_prevalence_normalized: float hen_prevalence_normalized_smooth: float ct_threshold: float prev_quantile: flot Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: obs.out.stats.gam.binomial.20230620.csv Number of variables: 3 Number of cases/rows: 13 Variable List: ct_threshold: integer AIC : float AIC.quantile: float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: obs.out.stats.gam.poisson.20230620.csv Number of variables: 3 Number of cases/rows: 13 Variable List: ct_threshold: integer AIC : float AIC.quantile: float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: obs.out.stats.glm.binomial.20230620.csv Number of variables: 3 Number of cases/rows: 13 Variable List: ct_threshold: integer AIC : float AIC.quantile: float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: obs.out.stats.glm.poisson.20230620.csv Number of variables: 3 Number of cases/rows: 13 Variable List: ct_threshold: integer AIC : float AIC.quantile: float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: perm.out.20230620.csv Number of variables: 5 Number of cases/rows: 292500 Variable List: sampling_cluster_median_date : text permutation : integer hen_prevalence_normalized : float hen_prevalence_normalized_smooth: float ct_threshold : integer Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: perm.out.stats.gam.binomial.20230620.csv Number of variables: 3 Number of cases/rows: 6500 Variable List: permutation : integer ct_threshold: integer AIC : float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: perm.out.stats.gam.poisson.20230620.csv Number of variables: 3 Number of cases/rows: 6500 Variable List: permutation : integer ct_threshold: integer AIC : float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: perm.out.stats.glm.binomial.20230620.csv Number of variables: 3 Number of cases/rows: 6500 Variable List: permutation : integer ct_threshold: integer AIC : float Missing data codes: Specialized formats or other abbreviations used: DATA-SPECIFIC INFORMATION FOR: perm.out.stats.glm.poisson.20230620.csv Number of variables: 3 Number of cases/rows: 6500 Variable List: permutation : integer ct_threshold: integer AIC : float Missing data codes: Specialized formats or other abbreviations used: