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Wastewater Based Epidemiology (WBE) has become an integral part of the public health effort to track the levels of SARS-CoV-2 within communities. Detection of SARS-CoV-2 in wastewater can be challenging due to relatively low levels of virus within the sample. The wastewater matrix is also comprised of commercial and domestically derived contaminants, as well as RNases, all of which can adversely affect RT-qPCR analysis. To improve SARS-CoV-2 detection within wastewater samples we investigated both the effect of template dilution (as a means to reduce RT-qPCR inhibition) and sample stabilisation via addition of DNA/RNA Shield™ and/or RNA Later™ (to prevent RNA degradation via RNases) as a means to improve viral fragment detection. Using both methodologies a significant improvement in SARS-CoV-2 detection from wastewater samples was observed. No adverse effects of stabilising agent addition on downstream Next Generation Sequencing workflows were detected.

A randomized experiment that measures the impact of a social policy in a sample of the population reveals whether the policy will work on average with universal application. An experiment that includes only the subset of the population that volunteers for the intervention generates narrower “proof-of-concept” evidence of whether the policy can work for motivated individuals. Both forms of learning carry value, yet evaluations rarely combine the two designs. The U.S. Social Security Administration conducted an exception, the Benefit Offset National Demonstration (BOND). This article uses BOND to examine the statistical power implications and potential gains in policy learning—relative to costs—from combining volunteer and population-representative experiments. It finds that minimum detectable effects of volunteer experiments rise little when one adds a population-representative experiment, but those of a population-representative experiment double or quadruple with the addition of a volunteer experiment.

As of 8 July 2022, the World Health Organization (WHO) have reported 1010 probable cases of acute hepatitis of unknown aetiology in children worldwide, including approximately 250 cases in the United Kingdom (UK). Clinical presentations have often been severe, with liver transplantation a frequent clinical outcome. Human adenovirus F41 (HAdV-F41) has been detected in most children with acute hepatitis, but its role in the pathogenesis of this infection has yet to be established. Wastewater-based epidemiology (WBE) has become a well-established tool for monitoring the community spread of SARS-CoV-2, as well as other pathogens and chemicals. In this study, we adopted a WBE approach to monitoring levels of HAdV-F40/41 in wastewater before and during an acute hepatitis outbreak in Northern Ireland. We report increasing detection of HAdV-F40/41 in wastewater, concomitant with increasing numbers of clinical cases. Amplicon whole genome sequencing further classified the wastewater-derived HAdV as belonging to the F41 genotype which in turn was homologous to clinically derived sequences. We propose that WBE has the potential to inform community surveillance of HAdV-F41 and can further contribute to the ongoing global discussion supporting HAdV-F41 involvement in acute hepatitis cases.

The Benefit Offset National Demonstration (BOND) tested a $1 reduction in benefits per $2 earnings increase above the level at which Social Security Disability Insurance benefits drop from full to zero under current law. BOND included a rare and large “population-representative” experiment: It applied the rule to a nationwide, random sample of beneficiaries. BOND also included a parallel “volunteer” experiment: The same offset rules applied to a nationwide random sample of volunteers. BOND’s dual experiments offer a unique opportunity to consider the merits and limitations of these two types of experiments. The findings provide a way to illustrate the limitations of volunteer experiments relative to population experiments when, as in BOND, the goal is to produce impact estimates that are valid for the entire population, not just would be volunteers. We also highlight and compare ethical issues, outreach challenges, and cost considerations for the two types of designs.

Microbiocidal products for decontaminating both animate and inanimate surfaces that may act as vectors for disease transmission are a well-established method for inactivating viruses of clinical significance. There are a wide variety of such microbiocidal products that can be purchased commercially, many with different active ingredients. We have recently shown that Rosin soap (derived from Tall Oil that has been produced by trees) is a highly effective virucidal product against enveloped viruses like influenza A virus and SARS-CoV-2 when tested in solution. Here we further demonstrate that Rosin soap retains its virucidal activity against influenza A virus and SARS-CoV-2 when applied to surfaces, such as plastic, glass and steel, either before or after virus inoculation. The virucidal activity extended out to seven days post administration. Together, our results show the potential for Rosin soap-based disinfectants to be used as a surface coating to protect against clinically relevant enveloped viruses, such as influenza viruses and coronaviruses.

The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions ( Panthera leo ) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.

Animal-attached devices have transformed our understanding of vertebrate ecology. To minimize any associated harm, researchers have long advocated that tag masses should not exceed 3% of carrier body mass. However, this ignores tag forces resulting from animal movement. Using data from collar-attached accelerometers on 10 diverse free-ranging terrestrial species from koalas to cheetahs, we detail a tag-based acceleration method to clarify acceptable tag mass limits. We quantify animal athleticism in terms of fractions of animal movement time devoted to different collar-recorded accelerations and convert those accelerations to forces (acceleration × tag mass) to allow derivation of any defined force limits for specified fractions of any animal's active time. Specifying that tags should exert forces that are less than 3% of the gravitational force exerted on the animal's body for 95% of the time led to corrected tag masses that should constitute between 1.6% and 2.98% of carrier mass, depending on athleticism. Strikingly, in four carnivore species encompassing two orders of magnitude in mass ( ca 2–200 kg), forces exerted by ‘3%' tags were equivalent to 4–19% of carrier body mass during moving, with a maximum of 54% in a hunting cheetah. This fundamentally changes how acceptable tag mass limits should be determined by ethics bodies, irrespective of the force and time limits specified.

Evidence‐based policy at the local level requires predicting the impact of an intervention to inform whether it should be adopted. Increasingly, local policymakers have access to published research evaluating the effectiveness of policy interventions from national research clearinghouses that review and disseminate evidence from program evaluations. Through these evaluations, local policymakers have a wealth of evidence describing what works, but not necessarily where. Multisite evaluations may produce unbiased estimates of the average impact of an intervention in the study sample and still produce inaccurate predictions of the impact for localities outside the sample for two reasons: (1) the impact of the intervention may vary across localities, and (2) the evaluation estimate is subject to sampling error. Unfortunately, there is relatively little evidence on how much the impacts of policy interventions vary from one locality to another and almost no evidence on the implications of this variation for the accuracy with which the local impact of adopting an intervention can be predicted using findings from an evaluation in other localities. In this paper, we present a set of methods for quantifying the accuracy of the local predictions that can be obtained using the results of multisite randomized trials and for assessing the likelihood that prediction errors will lead to errors in local policy decisions. We demonstrate these methods using three evaluations of educational interventions, providing the first empirical evidence of the ability to use multisite evaluations to predict impacts in individual localities—i.e., the ability of “evidence‐based policy” to improve local policy.

Chemical methods of virus inactivation are used routinely to prevent viral transmission in both a personal hygiene capacity but also in at-risk environments like hospitals. Several ‘virucidal’ products exist, including hand soaps, gels and surface disinfectants. Resin acids, which can be derived from Tall oil produced from trees, have been shown to exhibit anti-bacterial activity. However, whether these products or their derivatives have virucidal activity is unknown. Here, we assessed the capacity of Rosin soap to inactivate a panel of pathogenic mammalian viruses in vitro. We show that Rosin soap can inactivate the human enveloped viruses: influenza A virus (IAV), respiratory syncytial virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For IAV, rosin soap could provide a 100,000-fold reduction in infectivity. However, Rosin soap failed to affect the non-enveloped encephalomyocarditis virus (EMCV). The inhibitory effect of Rosin soap against IAV infectivity was dependent on its concentration but not dependent on incubation time nor temperature. Together, we demonstrate a novel chemical inactivation method against enveloped viruses, which could be of use in preventing virus infections in certain settings.ImportanceViruses remain a significant cause of human disease and death, most notably illustrated through the current Covid-19 pandemic. Control of virus infection continues to pose a significant global health challenge to the human population. Viruses can spread through multiple routes, including via environmental and surface contamination where viruses can remain infectious for days. Methods to inactivate viruses on such surfaces may help mitigate infection. Here we present evidence identifying a novel ‘virucidal’ product in Rosin soap, which is produced from Tall oil from coniferous trees. Rosin soap was able to rapidly and potently inactivate influenza virus and other enveloped viruses.

The combined use of Global Positioning System (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequency. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy (specifically, location error and fix success rate) being affected by signal reception. We hypothesised that animal behaviour was the main factor affecting fix inaccuracy (particularly for collar-mounted tags sampling at high frequency). In conjunction to this, inherent GPS positional noise (‘jitter’), would be most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A Movement Verified Filtering (MVF) protocol was constructed to compare GPS-derived speed data to dynamic body acceleration (DBA). This was collected by a simultaneously deployed tri-axial accelerometer, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions ( Panthera leo ) within the Kgalagadi Transfrontier park in the Kalahari Desert, fitted with collar-mounted GPS units and tri-axial motion sensors (Daily Diary; DD) recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by > 80 % prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high resolution GPS datasets. We demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement and caution the accuracy of findings from previous studies that employed minimal GPS pre-processing . Throughout, we address the applicability of comparing fine-scale indices of GPS- and motion sensor-borne data in tandem to qualify animal behaviour.

Background Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies.

Animal-attached devices have transformed our understanding of vertebrate ecology. To minimize tag-related harm for these studies, researchers have long advocated that tag masses should not exceed 3% of the animal’s body mass. However, this proposition ignores tag forces generated as a result of animal movement.Using data from collar-attached accelerometers on diverse free-ranging terrestrial animals, we detail a tag-based acceleration method (TbAM) in which we quantify animal athleticism in terms of fractions of animal movement time devoted to different collar-recorded accelerations. The varying accelerations are converted to forces imposed on the animals based on the acceleration and tag mass and allow derivation of defined force limits, including those amounting to 3% of the animal’s mass, for specified fractions of any animal’s active time.We demonstrate how species athleticism is the principal determinant of tag forces, whereas body mass is of little importance. Forces exerted by ‘3%’ tags were mostly equivalent to 4-19% of the animals’ masses during moving, with a maximum of 54% in a hunting cheetah. Cumulative frequency curves of tag acceleration for periods when animals were active, all showed a characteristic sigmoid pattern, which was displaced further to the right as higher acceleration activities accounted for an increasing proportion of any animal’s time. Specifying that tags should exert forces that are less than 3% of the animal’s body mass for 95% of the time led to corrected tag masses constituting between 1.6% and 2.98% of our study animals’ masses, with values depending on animal athleticism.Recognition that animal athleticism affects tag forces of their carriers fundamentally changes how acceptable tag mass limits should be determined by ethics bodies. In order to have a scientifically robust acceptable threshold to limit the forces experienced by an animal carrier, we suggest practitioners derive a similar cumulative acceleration profile for their study species and use a minimum of the 95% limits on the plot (although higher limits may be more appropriate).

Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

Animal-attached devices have transformed our understanding of vertebrate ecology. However, to be acceptable, researchers must minimize tag-related harm. The long-standing recommendation that tag masses should not exceed 3% of the animal’s body mass ignores tag forces generated by movement. We used collar-attached accelerometers on four free-ranging carnivores, spanning two orders of magnitude in mass, to reveal that during movement, forces exerted by ‘3%’ tags were generally equivalent to 4-19% of the animals’ masses, with a record of 54% in a hunting cheetah. Controlled studies on domestic dogs revealed how the tag forces are dictated by animal gait and speed but appear largely invariant of body mass. This fundamentally changes how acceptable tag mass limits should be determined, requiring cognizance of animal athleticism.One Sentence SummaryThere can be no universal rule for collar-tag masses as a percentage of carrier mass since tag forces depend on lifestyle.

Although randomized experiments are lauded for their high internal validity, they have been criticized for the limited external validity of their results. This chapter describes research strategies for investigating how much nonrepresentative site selection may limit external validity and bias impact findings. The magnitude of external validity bias is potentially much larger than what is thought of as an acceptable level of internal validity bias. The chapter argues that external validity bias should always be investigated by the best available means and addressed directly when presenting evaluation results. These observations flag the importance of making external validity a priority in evaluation planning.

Program evaluators generally prefer to use the strongest design available to answer relevant impact questions, reserving analytic strategies for use only as necessary. Although the “simple” treatment versus control experimental design is well understood and widespread in its use, it is our contention that creativity in evaluation design can help answer more nuanced questions regarding what about a program is responsible for its impacts. In response, this chapter discusses several experimental evaluation designs that randomize individuals—including multiarmed, multistage, factorial, and blended designs—to permit estimating impacts for specific policy design features or program elements. We hope that recasting what are some long-standing but underused designs in a new light will motivate their increased use, where appropriate.

Using exogenous characteristics to identify endogenous subgroups, the approach discussed in this method note creates symmetric subsets within treatment and control groups, allowing the analysis to take advantage of an experimental design. In order to maintain treatment–control symmetry, however, prior work has posited that it is necessary to use a prediction subsample, separate from the subsample used for impact estimation in order to prevent overfitting from affecting impact estimates. Doing so diminishes sample size—both for prediction and analysis—and so has costs. This article delves into this topic to consider the conditions under which overfitting occurs and to characterize the effects of overfitting in terms of bias and variance. It suggests a strategy for preserving the full sample size in all phases of the analysis. The research uses Monte Carlo simulation to directly measure overfitting, identify the circumstances that should concern us, and to explore possible recommended practices and future research implications.

To answer “what works?” questions about policy interventions based on an experimental design, Peck (2003) proposes to use baseline characteristics to symmetrically divide treatment and control group members into subgroups defined by endogenously determined postrandom assignment events. Symmetric prediction of these subgroups in both experimental arms ensures the internal validity of the subgroup impacts estimates but leaves the external validity of the findings in doubt. A final step of the procedure solves for impacts on actual subgroups using a system of equations that is underidentified without further assumptions. We address these assumptions by first extending the methodology to encompass three rather than two endogenous subgroups and then proposing plausible assumptions for deriving impacts for actual endogenous subgroups. We also consider how the first-stage prediction process can be structured to better support the accuracy of the assumptions.

Government employment and training programs typically do not have sufficient resources to serve all those who apply for assistance. Those to be served are usually selected by program staff based on management guidelines that allow considerable policy discretion at the local level. A longstanding issue in employment and training policy is whether allowing this flexibility leads to selection of applicants (1) most likely to benefit from the program or (2) who are likely to experience the highest absolute outcomes in the absence of program services, sometimes called “creaming”. The distinction is crucial to the success of many programs, both as redistributional tools and as economic investments. Selection of those most likely to benefit from the program—i.e., those for whom the program's impact on subsequent labor market success will be greatest—will maximize the social return on the investment in training. In contrast, “creaming” may lead to little or no social benefit or to a substantial gain, depending on whether those selected for training—the group most likely to succeed without the treatment—in fact benefit most from it. The redistributional effects of a program will also depend on who is served: among the applicant group, a more equal distribution of economic well-being, ex post, will be achieved only if the program favors applicants likely to do worst without the intervention. This paper explores the role of creaming in the operation of seven welfare-to-work training programs, the type of programs that have been the focus of increased expenditures over the last 10 years as more and more welfare recipients have been pushed to become self-sufficient. It considers whether the program intake practices adopted in the studied programs furthered the social goals pursued and, if not, what consequences they had on the twin concerns of distributional equity and economic efficiency. The analysis begins by reviewing the history of the creaming issue and its importance in the literature. A unique data set is then examined to discover the factors that influenced admission decisions in seven state-run employment and training programs for welfare recipients and how those decisions played out in terms of the in-training performance and later labor market outcomes of program participants. The principal conclusions are that these programs “creamed” the most able applicants on both observable and unobservable characteristics, but that this targeting did not systematically affect the size of program impacts or the return on investment.

Evaluations of the impact of social programs are often carried out in multiple “sites,” such as school districts, housing authorities, local TANF offices, or One-Stop Career Centers. Most evaluations select sites purposively following a process that is nonrandom. Unfortunately, purposive site selection can produce a sample of sites that is not representative of the population of interest for the program. In this paper, we propose a conceptual model of purposive site selection. We begin with the proposition that a purposive sample of sites can usefully be conceptualized as a random sample of sites from some well-defined population, for which the sampling probabilities are unknown and vary across sites. This proposition allows us to derive a formal, yet intuitive, mathematical expression for the bias in the pooled impact estimate when sites are selected purposively. This formula helps us to better understand the consequences of selecting sites purposively, and the factors that contribute to the bias. Additional research is needed to obtain evidence on how large the bias tends to be in actual studies that select sites purposively, and to develop methods to increase the external validity of these studies.

When measuring the causal impacts of policy interventions, evaluators often contrast samples with and without the intervention tracked longitudinally. This article considers what happens when the without-intervention portion of the sample begins to be treated with the intervention part way through the follow-up period. The authors focus on randomized control trials, whose sponsors increasingly guarantee all participants in the study the intervention—the control members with a lag—while noting that the problem and their proposed solution apply to non-experimental impact evaluations as well. Once the control group (or other counterfactual group) receives the intervention, the treatment-control comparison becomes a measure of the difference in impacts between interventions of different durations—longer in the treatment group than the control group—rather than a measure of the total impact of the longer duration compared to no intervention. To measure this total impact—which tends to matter more to policy than differential or short-term impacts—the authors propose to sum the available experimental impact estimates from consecutive time periods, thus combining an experimental measure of the impact of the short duration intervention with an incremental measure of the added impact of the longer duration. The statistical properties of this estimator are explored, as are the conditions under which it provides an unbiased estimate of the total long-term effects of treatment.

The authors begin with a thorough assessment of the many nonexperimental employment and training program evaluation techniques based on non-random comparison groups. These techniques typically use econometric methods to estimate the effects of employment and training programs by using comparison groups from non-program "external" sources. Then, recognizing the inherent drawbacks in these methods, Bell, Orr, Blomquist and Cain respond by reintroducing an evaluation method first implemented in the 1960s, the use of "internal" comparison groups consisting of nonparticipating program applicants.

Spectroscopic analysis of the 1,1,3-trioxy dienes (1), (2) shows them to possess ( Z )-stereochemistry, contrary to several literature assignments; characterisation of derived cycloadducts and crystallographic analysis of one of them (20) establishes endo cycloaddition.