Serosurveys to Optimize Peak Pathogen Predictions (STOPPP) Model

GENERAL INFORMATION

Repository for the Mathematica notebook, R code and previoulsy published data used in analysesfor the manuscript:

Erin Clancey $^{1,\ast}$, Scott L. Nuismer $^2$, and Stephanie N. Seifert $^1$, Using serosurveys to optimize surveillance for zoonotic pathogens

1. Current Address: Paul G. Allen School for Global Health, Washington State University, Pullman, WA 99164 USA;
2. Department of Biological Sciences, University of Idaho, Moscow, ID 83844 USA:

$\ast$ Corresponding author; e-mail: [email protected]

Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. Here we develop a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens by predicting time windows of peak prevalence. Using simulated data sets, we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. Then we demonstrate an implementation of our method using publicly available data from two putative \textit{Ebolavirus} reservoirs, straw-colored fruit bats (\textit{Eidolon helvum}) and hammer-headed bats (\textit{Hypsignathus monstrosus}). We envision our method being used to guide the planning of field sampling to maximize the probability of detecting active infections, and in cases when longitudinal data is available, our method can also yield predictions for the times of year that are most likely to produce future spillover events. The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to predictable, but potentially short-lived, pulses of pathogen prevalence.

All data used in this study was previously published and is available from Pleydell, D. R. J. (2023). R, C and NIMBLE code for Yaounde-Ebola-E.helvum modelling paper (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8193102

Funding was provided by the Centre for Research in Emerging Infectious Diseases - East and Central Africa (CREID-ECA) grant number U01AI151799 in support of EC and SNS, PIPP Phase I: Predicting Emergence in Multidisciplinary Pandemic Tipping-points (PREEMPT) from the U.S. National Science Foundation (NSF) grant number 2200140 in support of EC, Verena (viralemergence.org) from NSF including NSF BII 2021909 and NSF BII 2213854 and the US National Institute of Allergy and Infectious Disease/National Institutes of Health (NIAID/NIH) grant number U01AI151799 in support of SNS, NIH 2R01GM122079-05A1 awarded to SNL and NSF DEB 2314616 awarded to SNL.

DATA & CODE FILE OVERVIEW

The repository is split into four subdirectoires:

1. Mathematica - This directory contains the Mathematica Notebook.
2. Simulations - This directory contains R code used to create and analyze the simulated data.
3. Work_flow_real_data - This directory contains information to follow a workflow as a demonstration of how to implement our methodology. This directory contains a README.md file explaing the workflow and two folders containing data and R code for the E. helvum and H. monstrosus populations.