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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 

1) Pathogens can spill over and infect new host species by overcoming a series of ecological and biological barriers. Hendra virus (HeV) circulates in Australian flying foxes and provides a data‐rich study system for identifying environmental drivers underlying spillover events. The frequency of spillover events to horses has varied interannually since the virus was first discovered in 1994. These observations suggest that HeV spillover events are driven, in part, by environmental factors, including loss of flying fox habitat and climate variability. 2) We explicitly examine the impact of environmental variation on the risk of HeV spillover at three spatial scales relevant to this system. We use a dataset of 60 spillover events and boosted regression tree methods to identify environmental features (including concurrent and lagged temperature, rainfall, vegetation indices, land cover, and climate indices) at three spatial scales (1‐km, 20‐km, 100‐km radii) associated with horse contacts and reservoir species ecology. 3) We find that temperature, local (1‐km radius) human population density, and landscape (100‐km radius) forest cover and pasture are the most influential environmental features associated with HeV spillover risk. By including multiple spatial scales and temporal lags in environmental features, we can more accurately quantify risk across space and time than with models that use a single scale. For example, high quality vegetation at the local scale and within a foraging radius (20‐km) in the concurrent month and previous years, combined with poorer quality vegetation at the landscape scale in the concurrent month increase risk of HeV spillover. These and other environmental associations likely influence the dynamic foraging behaviour of reservoir flying foxes and drive contacts that facilitate spillover into horse populations. 4) Synthesis and application: Current management of HeV spillover focuses on local‐scale interventions – primarily through vaccination and detection of infected horses. Our study finds that HeV spillover risk is also driven by environmental changes over much larger scales and demonstrates management practices would benefit from incorporating landscape interventions alongside local interventions. 

Abstract 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 threat 1 . Many correlational studies associate spillover with changes in land use or other anthropogenic stressors 2,3 , although the mechanisms underlying the observed correlations have not been identified 4 . 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. 

Abstract Substantial global attention is focused on how to reduce the risk of future pandemics. Reducing this risk requires investment in prevention, preparedness, and response. Although preparedness and response have received significant focus, prevention, especially the prevention of zoonotic spillover, remains largely absent from global conversations. This oversight is due in part to the lack of a clear definition of prevention and lack of guidance on how to achieve it. To address this gap, we elucidate the mechanisms linking environmental change and zoonotic spillover using spillover of viruses from bats as a case study. We identify ecological interventions that can disrupt these spillover mechanisms and propose policy frameworks for their implementation. Recognizing that pandemics originate in ecological systems, we advocate for integrating ecological approaches alongside biomedical approaches in a comprehensive and balanced pandemic prevention strategy. 

Abstract The COVID-19 pandemic and its aftermath are the most significant socio-economic crises in modern history. The pandemic’s devastating impacts have prompted urgent policy and regulatory action to reduce the risks of future spillover events and pandemics. Stronger regulatory measures for the trade of wildlife are central to discussions of a policy response. A variety of measures, including broad bans on the trade and sale of wildlife to banning specific species for human consumption are among a suite of discussed options. However, the wildlife trade is diverse, complex, and important for the livelihoods of millions of people globally. We argue that reducing the risk of future pandemics stemming from the wildlife trade must follow established principles of governance which include being equitable, responsive, robust, and effective. We demonstrate how incorporating these principles will support the development of context-specific, culturally sensitive, and inclusive responses that recognize the on-the-ground complexity of disease emergence and the social-ecological systems in which the wildlife trade occurs.