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Optimal sampling strategies for detecting zoonotic disease epidemics.

ABSTRACT: The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

SUBMITTER: Ferguson JM

PROVIDER: S-EPMC4072525 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Optimal sampling strategies for detecting zoonotic disease epidemics.

Ferguson Jake M JM Langebrake Jessica B JB Cannataro Vincent L VL Garcia Andres J AJ Hamman Elizabeth A EA Martcheva Maia M Osenberg Craig W CW

PLoS computational biology 20140626 6

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sa ...[more]

PMID: 24968100

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Project 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.

Project description:BACKGROUND: Periodontitis is an infectious and inflammatory disease of polymicrobial etiology that can lead to the destruction of bones and tissues that support the teeth. The management of chronic periodontitis (CP) relies heavily on elimination or at least control of known pathogenic consortia associated with the disease. Until now, microbial plaque obtained from the subgingival (SubG) sites has been the primary focus for bacterial community analysis using deep sequencing. In addition to the use of SubG plaque, here, we investigated whether plaque obtained from supragingival (SupG) and tongue dorsum sites can serve as alternatives for monitoring CP-associated bacterial biomarkers. RESULTS: Using SubG, SupG, and tongue plaque DNA from 11 healthy and 13 diseased subjects, we sequenced V3 regions (approximately 200 bases) of the 16S rRNA gene using Illumina sequencing. After quality filtering, approximately 4.1 million sequences were collapsed into operational taxonomic units (OTUs; sequence identity cutoff of >97%) that were classified to a total of 19 phyla spanning 114 genera. Bacterial community diversity and overall composition was not affected by health or disease, and multiresponse permutation procedure (MRPP) on Bray-Curtis distance measures only supported weakly distinct bacterial communities in SubG and tongue plaque depending on health or disease status (P?<?0.05). Nonetheless, in SubG and tongue sites, the relative abundance of Firmicutes was increased significantly from health to disease and members of Synergistetes were found in higher abundance across all sites in disease. Taxa indicative of CP were identified in all three locations (for example, Treponema denticola, Porphyromonas gingivalis, Synergistes oral taxa 362 and 363). CONCLUSIONS: For the first time, this study demonstrates that SupG and tongue dorsum plaque can serve as alternative sources for detecting and enumerating known and novel bacterial biomarkers of CP. This finding is clinically important because, in contrast with SubG sampling that requires trained professionals, obtaining plaque from SupG and tongue sites is convenient and minimally-invasive and offers a novel means to track CP-biomarker organisms during treatment outcome monitoring.

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Optimal sampling strategies for detecting zoonotic disease epidemics.

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Optimal sampling strategies for detecting zoonotic disease epidemics.

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