Optimizing noninvasive sampling of a zoonotic bat virus

Giles, John R.; Peel, Alison J.; Wells, Konstans; Plowright, Raina K.; McCallum, Hamish; Restif, Olivier

doi:10.1002/ece3.7830

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Optimizing noninvasive sampling of a zoonotic bat virus

John R. Giles, Alison J. Peel, Konstans Wells

, Raina K. Plowright, Hamish McCallum, Olivier Restif

Ecology and Evolution, Volume: 11, Issue: 18

Swansea University Author: Konstans Wells

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DOI (Published version): 10.1002/ece3.7830

Abstract

Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets...

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Published in:	Ecology and Evolution
ISSN:	2045-7758 2045-7758
Published:	Wiley 2021
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URI:	https://cronfa.swan.ac.uk/Record/cronfa57697
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However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. 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spelling	2021-11-10T09:59:06.7542424 v2 57697 2021-08-27 Optimizing noninvasive sampling of a zoonotic bat virus d18166c31e89833c55ef0f2cbb551243 0000-0003-0377-2463 Konstans Wells Konstans Wells true false 2021-08-27 SBI Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation. Journal Article Ecology and Evolution 11 18 Wiley 2045-7758 2045-7758 bat virus; sampling bias; under roost sampling; viral prevalence 27 8 2021 2021-08-27 10.1002/ece3.7830 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University Another institution paid the OA fee DARPA PREEMPT program Cooperative Agreement. Grant Numbers: D18AC00031, D16AP00113; U.S. National Science Foundation. Grant Number: DEB-1716698; USDA National Institute of Food and Agriculture. Grant Number: 1015891; Queensland Government Accelerate Postdoctoral Research Fellowship; ARC DECRA fellowship. Grant Number: DE190100710 2021-11-10T09:59:06.7542424 2021-08-27T13:47:48.9626122 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences John R. Giles 1 Alison J. Peel 2 Konstans Wells 0000-0003-0377-2463 3 Raina K. Plowright 4 Hamish McCallum 5 Olivier Restif 6 57697__20707__0094669807064d558894e51d96063af3.pdf Giles_etal_2021_EcolEvol.pdf 2021-08-27T13:52:07.9219433 Output 2091723 application/pdf Version of Record true © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title	Optimizing noninvasive sampling of a zoonotic bat virus
spellingShingle	Optimizing noninvasive sampling of a zoonotic bat virus Konstans Wells
title_short	Optimizing noninvasive sampling of a zoonotic bat virus
title_full	Optimizing noninvasive sampling of a zoonotic bat virus
title_fullStr	Optimizing noninvasive sampling of a zoonotic bat virus
title_full_unstemmed	Optimizing noninvasive sampling of a zoonotic bat virus
title_sort	Optimizing noninvasive sampling of a zoonotic bat virus
author_id_str_mv	d18166c31e89833c55ef0f2cbb551243
author_id_fullname_str_mv	d18166c31e89833c55ef0f2cbb551243_***_Konstans Wells
author	Konstans Wells
author2	John R. Giles Alison J. Peel Konstans Wells Raina K. Plowright Hamish McCallum Olivier Restif
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container_title	Ecology and Evolution
container_volume	11
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description	Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.
published_date	2021-08-27T04:13:37Z
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Optimizing noninvasive sampling of a zoonotic bat virus

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