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Abstract Ebola virus (EBOV) and Marburg virus (MARV) are zoonotic filoviruses that cause hemorrhagic fever in humans. Correlative data implicate bats as natural EBOV hosts, but neither a full-length genome nor an EBOV isolate has been found in any bats sampled. Here, we model filovirus infection in the Jamaican fruit bat (JFB),Artibeus jamaicensis,by inoculation with either EBOV or MARV through a combination of oral, intranasal, and subcutaneous routes. Infection with EBOV results in systemic virus replication and oral shedding of infectious virus. MARV replication is transient and does not shed. In vitro, JFB cells replicate EBOV more efficiently than MARV, and MARV infection induces innate antiviral responses that EBOV efficiently suppresses. Experiments using VSV pseudoparticles or replicating VSV expressing the EBOV or MARV glycoprotein demonstrate an advantage for EBOV entry and replication early, respectively, in JFB cells. Overall, this study describes filovirus species-specific phenotypes for both JFB and their cells. 

Land-use change may drive viral spillover from bats into humans, partly through dietary shifts caused by decreased availability of native foods and increased availability of cultivated foods. We experimentally manipulated diets of Jamaican fruit bats to investigate whether diet influences viral shedding. To reflect dietary changes experienced by wild bats during periods of nutritional stress, Jamaican fruit bats were fed either a standard diet or a putative suboptimal diet, which was deprived of protein (suboptimal-sugar diet) and/or supplemented with fat (suboptimal-fat diet). Upon H18N11 influenza A-virus infection, bats fed on the suboptimal-sugar diet shed the most viral RNA for the longest period, but bats fed the suboptimal-fat diet shed the least viral RNA for the shortest period. Bats on both suboptimal diets ate more food than the standard diet, suggesting nutritional changes may alter foraging behaviour. This study serves as an initial step in understanding whether and how dietary shifts may influence viral dynamics in bats, which alters the risk of spillover to humans. 

Synchronized seasonal excretion of multiple coronaviruses coincides with high rates of coinfection in immature bats. This repo contains instructions and source code for reproducing the statistical analyses in the manuscript. Repo Contents scripts: contains the source .R and .stan files to reproduce the anaysis. Each file is detailed below in the specific sections corresponding to the statistical analyses. data: contains the raw source data and model generated output. figures: contains the final output figures from the manuscript. These can be recreated with the CovOZ_Figures_Submission_Clean.R script. 1. System Requirements Hardware Requirements Our source code requires only a standard computer. Much of the Markov chain Monte Carlo code is run in parallel so a computer with ample memory and multiple cores can be advantageous. The runtimes below are generated using a macbook with the recommended specs (64 GB RAM, 8 cores at 2.7 GHz). The code will also work on linux or windows computer. Software Requirements Reproducing the statistical analyses requires a current version of R and stan. We use version 4.4.1 of R and version 2.32.2 of stan. Package dependencies and versions Users will need the following packages install the following packages to execute the code. Our versions are effective October 1, 2024 tidyverse 2.0.0 lubridate 1.9.3 stringr 1.5.1 rstan 2.32.6 cowplot 1.1.3 ggtext 0.1.2 jpeg 0.1-10 scales 1.3.0 tictoc 1.2.1 2. Installation Guide Running the analysis requires: installing R. Depending on wifi speeds, installing R usually takes a few minutes. installing stan. Depending on wifi speeds, installing stan usually takes a few minutes. installing the necessary R packages (listed above). Depending on wifi speeds, installing packages usually takes about 30 seconds per package. 3. Demo This source code is not an R package with a formal demo, but rather source code is included for the various analyses in section 4. 4. Instructions for Use 4.1 Coinfection Analysis Runs chi-squared tests on coinfections of beta 2d.iv and beta 2d.v. Generates summary statistics, test statistics, and p-values from manuscript. input files: individual_variant_covariates.csv script file: coinfection_final.R run time: approximately 1 second 4.2 Individual Level Dynamics of Infection: Dynamic Binary Regression Runs individual level dynamic binary regression models. Produces output file that can recreate figures. input files: individual_variant_covariates.csv script files: logistic_curves_final.R GP_regression.stan output files: logistic_curve_out.RData run time: approximately 66 minutes 4.3 Dynamics of Circulation at the Population Level Runs combined (individual and pooled data) dynamic models. Produces output file that can recreate figures. input files: combined_out_variant.csv script files: cluster_curves_final.R GP_withLL.stan output files: cluster_curves.csv run time: approximately 25 minutes 4.4 Manuscript Figures Combined script that uses output files created by previous scripts to recreate all figures in the manuscript. input files: model_output/cluster_curves.csv combined_out_variant.csv individual_variant_covariates.csv model_output/logistic_curve_out.RData script files: CovOZ_Figures_Submission_Clean.R output files: Figure2_final.png Figure3_final.png Figure4_A-D_final.png Figure6_AP.png Figure7.png SIFigure8.png SIFigure9.png run time: approximately 16 seconds 4.5 Model Comparison Integrated Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: Pred_Comparisons.R GP_withLL.stan output files: preds.RData run time: approximately 2 hours 4.6 Model Comparison Individual Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: logistic_curves_loo.R GP_regression.stan GP_regression_add.stan GP_regression_interact.stan output files: logistic_curve_loo_age.RData logistic_curve_loo_age_add_sex.RData logistic_curve_loo_age_interact_sex.RData run time: approximately 6:45 hours 

Bats are reservoirs of many zoonotic viruses that are fatal in humans but do not cause disease in bats. Moreover, bats generate low neutralizing antibody titers in response to experimental viral infection, although more robust antibody responses have been observed in wild-caught bats during times of food stress. Here, we compared the antibody titers and B cell receptor (BCR) diversity of Jamaican fruit bats (Artibeus jamaicensis; JFBs) and BALB/c mice generated in response to T-dependent and T-independent antigens. We then manipulated the diet of JFBs and challenged them with H18N11 influenza A-like virus or a replication incompetent Nipah virus VSV (Nipah-riVSV). Under standard housing conditions, JFBs generated a lower avidity antibody response and possessed more BCR mRNA diversity compared to BALB/c mice. However, withholding protein from JFBs improved serum neutralization in response to Nipah-riVSV and improved serum antibody titers specific to H18 but reduced BCR mRNA diversity. 

Abstract Understanding the drivers of seasonal disease outbreaks remains a fundamental challenge in disease ecology. Periodic outbreaks can be driven by several seasonally varying factors, including pulses of susceptible individuals through births, changes in host behaviour and social aggregation and variation in host immunity. However, when these potential drivers overlap temporally, isolating their relative contributions to outbreak patterns becomes challenging.We studied Hendra virus, a zoonotic pathogen with seasonal spillovers from bats to horses and humans. Multiple seasonal factors have been hypothesized to drive Hendra virus transmission, including food shortages, birth pulses and changes in host aggregation, but their temporal overlap has made identifying primary drivers difficult.We conducted a 4‐year longitudinal study ofPteropusbats to test whether seasonal birth pulses and the resulting influx of susceptible juveniles drive Hendra virus transmission. Using a Bayesian ageing model, we aged sexually immature bats and placed them into birth cohorts. We used our age predictions to model how viral shedding and antibody responses changed as bats aged. We trackedBartonellaspp. Infection—a bacterial pathogen requiring close contact for transmission—as an indicator of transmission opportunities within each cohort for comparison.We found no evidence that seasonal birth pulses of immunologically naïve juveniles drove Hendra virus transmission. Two out of three cohorts showed substantially reduced maternal antibody transfer compared to the 2018 cohort, with seroprevalence near zero at our earliest sampling timepoints and showed no clear evidence of synchronized seroconversion. Furthermore,Bartonellainfection rates were consistent across cohorts, indicating that opportunities for pathogen transmission remained consistent across cohorts despite varying viral shedding patterns.Our findings demonstrate that birth pulses alone cannot explain observed patterns of Hendra virus outbreaks. These results highlight the importance of using multiple lines of evidence to evaluate competing mechanisms underlying seasonal disease dynamics, particularly when potential drivers coincide temporally. 

Sampling reservoir hosts over time and space is critical to detect epizootics, predict spillover and design interventions. However, because sampling is logistically difficult and expensive, researchers rarely perform spatio-temporal sampling of many reservoir hosts. Bats are reservoirs of many virulent zoonotic pathogens such as filoviruses and henipaviruses, yet the highly mobile nature of these animals has limited optimal sampling of bat populations. To quantify the frequency of temporal sampling and to characterize the geographical scope of bat virus research, we here collated data on filovirus and henipavirus prevalence and seroprevalence in wild bats. We used a phylogenetically controlled meta-analysis to next assess temporal and spatial variation in bat virus detection estimates. Our analysis shows that only one in four bat virus studies report data longitudinally, that sampling efforts cluster geographically (e.g. filovirus data are available across much of Africa and Asia but are absent from Latin America and Oceania), and that sampling designs and reporting practices may affect some viral detection estimates (e.g. filovirus seroprevalence). Within the limited number of longitudinal bat virus studies, we observed high heterogeneity in viral detection estimates that in turn reflected both spatial and temporal variation. This suggests that spatio-temporal sampling designs are important to understand how zoonotic viruses are maintained and spread within and across wild bat populations, which in turn could help predict and preempt risks of zoonotic viral spillover.