Tools for Risk Analysis: Updating the 2006 WHO guidelines

This chapter reviews developments since the WHO Guidelines for the safe use of wastewater in agriculture were published in 2006. The six main developments are: the recognition that the tolerable additional disease burden may be too stringent for many developing countries; the benefits of focusing on single-event infection risks as a measure of outbreak potential when evaluating risk acceptability; a more rigorous method for estimating annual risks; the availability of dose-response data for norovirus; the use of QMRA to estimate Ascaris infection risks; and a detailed evaluation of pathogen reductions achieved by produce-washing and disinfection. Application of the developments results in more realistic estimates of the pathogen reductions required for the safe use of wastewater in agriculture and consequently permits the use of simpler wastewater treatment processes

Seamless fusion: multi-modal localization for first responders in challenging environments

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m

EMA-amplicon-based sequencing informs risk assessment analysis of water treatment systems

Illumina amplicon-based sequencing was coupled with ethidium monoazide bromide (EMA) pre-treatment to monitor the total viable bacterial community and subsequently identify and prioritise the target organisms for the health risk assessment of the untreated rainwater and rainwater treated using large-volume batch solar reactor prototypes installed in an informal settlement and rural farming community. Taxonomic assignments indicated that Legionella and Pseudomonas were the most frequently detected genera containing opportunistic bacterial pathogens in the untreated and treated rainwater at both sites. Additionally, Mycobacterium, Clostridium sensu stricto and Escherichia/Shigella displayed high (≥80%) detection frequencies in the untreated and/or treated rainwater samples at one or both sites. Numerous exposure scenarios (e.g. drinking, cleaning) were subsequently investigated and the health risk of using untreated and solar reactor treated rainwater in developing countries was quantified based on the presence of L. pneumophila, P. aeruginosa and E. coli. The solar reactor prototypes were able to reduce the health risk associated with E. coli and P. aeruginosa to below the 1 × 10−4 annual benchmark limit for all the non-potable uses of rainwater within the target communities (exception of showering for E. coli). However, the risk associated with intentional drinking of untreated or treated rainwater exceeded the benchmark limit (E. coli and P. aeruginosa). Additionally, while the solar reactor treatment reduced the risk associated with garden hosing and showering based on the presence of L. pneumophila, the risk estimates for both activities still exceeded the annual benchmark limit. The large-volume batch solar reactor prototypes were thus able to reduce the risk posed by the target bacteria for non-potable activities rainwater is commonly used for in water scarce regions of sub-Saharan Africa. This study highlights the need to assess water treatment systems in field trials using QMRA

Methods for estimating occupancy

© 2014 Dr. Natalie KaravarsamisThe estimation of the probability of occupancy of a site by a species is used to monitor the distribution of that species. Occupancy models have been widely applied and several limitations have been identified. In this thesis we resolve some of these. In particular we focus on limitations of maximum likelihood estimators and the associated interval estimators, and the difficulties associated with the extension from linear to generalised additive models for the relationship between occupancy and covariates. Initially we consider in detail the basic occupancy model which includes two parameters: $\psi$ and $p$. Our primary concern is the probability that the species occupies a particular site, $\psi$. The other parameter, the detection probability $p$, is a nuisance parameter. We first derive the joint probability mass function for the sufficient statistics of occupancy which allows the exact evaluation of its mean and variance, and hence its bias. We show that estimation near the boundaries of the parameter space is difficult. For small values of detection, we show that estimation of occupancy is not possible and specify the region of the parameter space where maximum likelihood estimators exist, and give the equations for the MLEs in this region. We next demonstrate that the asymptotic variance of the estimated occupancy is underestimated, yielding interval estimators that are too narrow. Methods for constructing interval estimators are then explored. We evaluate several bootstrap-based interval estimators for occupancy. Finally, instead of the full likelihood we consider a partial likelihood approach. This gives simple closed form estimators in a basic model with only a small loss of efficiency. It greatly simplifies the inclusion of linear and nonlinear covariates by allowing the use of standard statistical software for GLM and GAM frameworks and in our simulation study there is little loss of efficiency compared to the full likelihood