Dataset Information

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

ACCESS DATA

Publications

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

Similar Datasets

Project description:PurposeA variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible.MethodsA validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations.ResultsExternal validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly.ConclusionsOptimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

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:Surveillance schemes for methicillin-resistant Staphylococcus aureus (MRSA) are widely established at the national and international levels. Due to the simple standardization of the protocol, mainly isolates from bloodstream infections are used. However, the limitations of this simple surveillance system are well described. We conducted a comprehensive analysis of MRSA isolates in a large Slovenian region over 5 years to identify the optimal sample group for assessing the overall MRSA diversity. At the same time, this study provides to date non-available molecular characterization of Slovenian MRSA isolates. A total of 306 MRSA isolates from various sources were sequenced and phenotypically tested for resistance. The isolates exhibited significant molecular diversity, encompassing 30 multi locus sequence type (MLST) sequence types (STs), 39 ST-SCCmec genetic lineages, 49 spa types, and 29 antibiotic resistance profiles. Furthermore, the isolate pool comprised 57 resistance genes, representing 22 resistance mechanisms, and 96 virulence genes. While bloodstream isolates, commonly used in surveillance, provided insights into frequently detected clones, they overlooked majority of clones and important virulence and resistance genes. Blood culture isolates detected 21.3% spa types, 24.1% resistance phenotypes, and 28.2% MLST-SCCmec profiles. In contrast, strains from soft tissues demonstrated superior genomic diversity capture, with 65.3% spa types, 58.6% resistance phenotypes, and 71.8% MLST-SCCmec profiles. These strains also encompassed 100.0% of virulence and 82.5% of resistance genes, making them better candidates for inclusion in surveillance programs. This study highlights the limitations of relying solely on bloodstream isolates in MRSA surveillance and suggests incorporating strains from soft tissues to obtain a more comprehensive understanding of the epidemiology of MRSA.IMPORTANCEIn this study, we investigated the diversity of methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that can cause infections that are difficult to treat due to its resistance to antimicrobial agents. Currently, surveillance programs for MRSA mainly rely on isolates from bloodstream infections, employing a standardized protocol. However, this study highlights the limitations of this approach and introduces a more comprehensive method. The main goal was to determine which group of samples is best suited to understand the overall diversity of MRSA and to provide, for the first time, molecular characterization of Slovenian MRSA isolates. Our results suggest that including MRSA strains from soft tissue infections rather than just blood infections provides a more accurate and comprehensive view of bacterial diversity and characteristics. This insight is valuable for improving the effectiveness of surveillance programs and for developing strategies to better manage MRSA infections.

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.

Dataset Information

Optimal sampling strategies for detecting zoonotic disease epidemics.

Publications

Optimal sampling strategies for detecting zoonotic disease epidemics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets