Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation.

Wastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in wastewater catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of <40 %; ≤0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment. Furthermore, WBE can offer additional insights into infection rates in the catchment, such as the estimated 48 % decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.

Combined effects of landscape fragmentation and sampling frequency of movement data on the assessment of landscape connectivity.

BACKGROUND: Network theory is largely applied in real-world systems to assess landscape connectivity using empirical or theoretical networks. Empirical networks are usually built from discontinuous individual movement trajectories without knowing the effect of relocation frequency on the assessment of landscape connectivity while theoretical networks generally rely on simple movement rules. We investigated the combined effects of relocation sampling frequency and landscape fragmentation on the assessment of landscape connectivity using simulated trajectories and empirical high-resolution (1 Hz) trajectories of Alpine ibex (Capra ibex). We also quantified the capacity of commonly used theoretical networks to accurately predict landscape connectivity from multiple movement processes. METHODS: We simulated forager trajectories from continuous correlated biased random walks in simulated landscapes with three levels of landscape fragmentation. High-resolution ibex trajectories were reconstructed using GPS-enabled multi-sensor biologging data and the dead-reckoning technique. For both simulated and empirical trajectories, we generated spatial networks from regularly resampled trajectories and assessed changes in their topology and information loss depending on the resampling frequency and landscape fragmentation. We finally built commonly used theoretical networks in the same landscapes and compared their predictions to actual connectivity. RESULTS: We demonstrated that an accurate assessment of landscape connectivity can be severely hampered (e.g., up to 66% of undetected visited patches and 29% of spurious links) when the relocation frequency is too coarse compared to the temporal dynamics of animal movement. However, the level of landscape fragmentation and underlying movement processes can both mitigate the effect of relocation sampling frequency. We also showed that network topologies emerging from different movement behaviours and a wide range of landscape fragmentation were complex, and that commonly used theoretical networks accurately predicted only 30-50% of landscape connectivity in such environments. CONCLUSIONS: Very high-resolution trajectories were generally necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long periods should thus grow in the movement ecology sphere. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.

The effect of sampling frequency and spatial and temporal variation in the density of fecal indicator bacteria on the assessment of coastal bathing water quality.

The time and location of sampling as well as the number of samples per season can influence a reliable assessment of bathing water quality. In this study, we investigated the spatio-temporal variation of fecal indicator bacteria (FIB) density and the effects of FIB variability and sampling frequency on the assessment of a single sample and the annual and final assessment of coastal bathing water quality. Increasing the number of samples from 10 to 20 per bathing season had a significant impact on bathing water quality assessment at sites where water quality fluctuations had previously been observed, resulting in a change in water quality to a lower category in 36 % of annual and 54 % of final assessments, suggesting that the minimum number of samples per season should be increased at such sites. Increasing the number of samples at sites assessed as excellent over a longer period had no impact on water quality assessment. Spatial and temporal variations in FIB density were significant at a considerable number of sites both in the single sample assessment and in the annual and final assessment. Bathing water quality was spatially unrepresentative at a quarter of the sites analyzed and temporally unrepresentative at a fifth, as there was at least one additional point with a lower bathing water quality than the official sampling point or the bathing water quality was lower in the afternoon than in the morning. When revising the current recreational water quality regulations, the impact of sampling frequency of and of spatio-temporal variation of FIB density on the relevance of bathing water quality assessment at sites subjected to pollution needs to be seriously considered.

Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis.

BACKGROUND: Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods. OBJECTIVE: This study systematically analyzed the steps required to automate the video analysis workflow by investigating the applicability of a threshold-based event detection algorithm developed for 3D marker trajectories to 2D video data at four sampling rates; the agreement of 2D keypoints estimated by an off-the-shelf pose estimation model compared with gold-standard 3D marker trajectories projected to camera's field of view; and the influence of an offset in event detection on contact time and the sagittal knee joint angle at the key critical events of touch down and foot flat. METHODS: Repeated measures limits of agreement were used to compare parameters determined by markerless and marker-based motion capture. RESULTS: Results highlighted that a minimum video sampling rate of 100 Hz is required to detect key events, and the limited applicability of 3D marker trajectory-based event detection algorithms when using 2D video. Although detected keypoints showed good agreement with the gold-standard, misidentification of key events-such as touch down by 20 ms resulted in knee compression angle differences of up to 20°. CONCLUSION: These findings emphasize the need for de novo accurate key event detection algorithms to automate 2D video analysis pipelines.

Concomitant determination of pesticides in soil and drainage water over a potato cropping season reveal dissipations largely in accordance with respective models.

Pesticides are widely used in agriculture where they do not only reach their targets but also distribute to other environmental compartments and negatively affect non-target organisms. To prospectively assess their environmental risk, several tools and models using pesticide persistence (DT50) and leaching potential (groundwater ubiquity score (GUS), EXPOSIT) have been developed. Here, we simultaneously quantified 18 pesticides in soil and drainage water during a conventionally grown potato culture at field scale with high temporal resolution and compared our findings with predictions of the above models. Overall dissipations of all freshly applied compounds in soil were in line with published DT50 field values and their occurrences in drainage water were generally consistent with GUS and EXPOSIT models, respectively. In contrast, soil concentrations of the legacy pesticide atrazine and one of its transformation products (atrazine-2-hydroxy) were constant during the entire sampling campaign. Moreover, during peak discharge atrazine concentrations in drainage water were diluted whereas those of freshly applied pesticides were maximal. This difference demonstrates that the applied risk assessment tools were capable of predicting environmental concentrations and dissipation of pesticides at the short and medium time scale of a few half-lives after application, but fell short of capturing long-term trace residues.

Detail-preserving image warping by enforcing smooth image sampling.

Multi-phase dynamic contrast-enhanced magnetic resonance imaging image registration makes a substantial contribution to medical image analysis. However, existing methods (e.g., VoxelMorph, CycleMorph) often encounter the problem of image information misalignment in deformable registration tasks, posing challenges to the practical application. To address this issue, we propose a novel smooth image sampling method to align full organic information to realize detail-preserving image warping. In this paper, we clarify that the phenomenon about image information mismatch is attributed to imbalanced sampling. Then, a sampling frequency map constructed by sampling frequency estimators is utilized to instruct smooth sampling by reducing the spatial gradient and discrepancy between all-ones matrix and sampling frequency map. In addition, our estimator determines the sampling frequency of a grid voxel in the moving image by aggregating the sum of interpolation weights from warped non-grid sampling points in its vicinity and vectorially constructs sampling frequency map through projection and scatteration. We evaluate the effectiveness of our approach through experiments on two in-house datasets. The results showcase that our method preserves nearly complete details with ideal registration accuracy compared with several state-of-the-art registration methods. Additionally, our method exhibits a statistically significant difference in the regularity of the registration field compared to other methods, at a significance level of p < 0.05. Our code will be released at https://github.com/QingRui-Sha/SFM.

Optimal sampling interval for characterisation of the circadian rhythm of body temperature in homeothermic animals using periodogram and cosinor analysis.

Core body temperature (T c) is a critical aspect of homeostasis in birds and mammals and is increasingly used as a biomarker of the fitness of an animal to its environment. Periodogram and cosinor analysis can be used to estimate the characteristics of the circadian rhythm of T c from data obtained on loggers that have limited memory capacity and battery life. The sampling interval can be manipulated to maximise the recording period, but the impact of sampling interval on the output of periodogram or cosinor analysis is unknown. Some basic guidelines are available from signal analysis theory, but those guidelines have never been tested on T c data. We obtained data at 1-, 5- or 10-min intervals from nine avian or mammalian species, and re-sampled those data to simulate logging at up to 240-min intervals. The period of the rhythm was first analysed using the Lomb-Scargle periodogram, and the mesor, amplitude, acrophase and adjusted coefficient of determination (R 2) from the original and the re-sampled data were obtained using cosinor analysis. Sampling intervals longer than 60 min did not affect the average mesor, amplitude, acrophase or adjusted R 2, but did impact the estimation of the period of the rhythm. In most species, the period was not detectable when intervals longer than 120 min were used. In all individual profiles, a 30-min sampling interval modified the values of the mesor and amplitude by less than 0.1°C, and the adjusted R 2 by less than 0.1. At a 30-min interval, the acrophase was accurate to within 15 min for all species except mice. The adjusted R 2 increased as sampling frequency decreased. In most cases, a 30-min sampling interval provides a reliable estimate of the circadian T c rhythm using periodogram and cosinor analysis. Our findings will help biologists to select sampling intervals to fit their research goals.

Increasing accuracy of pulse arrival time estimation in low frequency recordings.

Objective.Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz.Approach.The method applies template matching to leverage the random nature of sampling time and expected change in the PAT.Main results.The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively.Significance.This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.

Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country.

Water quality monitoring networks (WQMNs) that capture both the temporal and spatial dimensions are essential to provide reliable data for assessing water quality trends in surface waters, as well as for supporting initiatives to control anthropogenic activities. Meeting these monitoring goals as efficiently as possible is crucial, especially in developing countries where the financial resources are limited and the water quality degradation is accelerating. Here, we asked if sampling frequency could be reduced while maintaining the same degree of information as with bimonthly sampling in the São Paulo State (Brazil) WQMN. For this purpose, we considered data from 2004 to 2018 for 56 monitoring sites distributed into four out of 22 of the state's water resources management units (UGRHIs, "Unidades de Gerenciamento de Recursos Hídricos"). We ran statistical tests for identifying data redundancy among two-month periods in the dry and wet seasons, followed by objective criteria to develop a sampling frequency recommendation. Our results showed that the reduction would be feasible in three UGRHIs, with the number of annual samplings ranging from two to four (instead of the original six). In both seasons, dissolved oxygen and Escherichia coli required more frequent sampling than the other analyzed parameters to adequately capture variability. The recommendation was compatible with flexible monitoring strategies observed in well-structured WQMNs worldwide, since the suggested sampling frequencies were not the same for all UGRHIs. Our approach can contribute to establishing a methodology to reevaluate WQMNs, potentially resulting in less costly and more adaptive strategies in São Paulo State and other developing areas with similar challenges.

Estimation of human spine orientation with inertial measurement units (IMU) at low sampling rate: How low can we go?

Studying people in their daily life is important for understanding conditions with multi-faceted aetiology such as chronic low back pain. Inertial measurement units can be used to reconstruct the posture and motion of the body outside of laboratories to enable this research. The battery life of these sensors strongly affects the usability of the system, since recharging them frequently is inconvenient and can lead to additional errors. A major determinant of the battery life for these sensors is sampling rate, but the relationship between sampling rate and accuracy in motion reconstruction is not well documented. We measured the spine of 12 participants using inertial measurement units across a variety of tasks such as sitting, standing, walking, and jogging. The orientation of the spine was reconstructed using several filters, including a novel filter developed specifically for high performance at low sampling frequencies. Benchmarking against optical motion capture, we developed a model showing that the error of all tested filters depends exponentially on the sampling frequency, with the optimal filter gains showing a similar exponential relationship. Using this model of error, we developed a criterion for recommending minimum sampling frequencies for accurate motion estimates for each task, finding frequencies ranging from about 13 to 35 Hz sufficient depending on the task. Although we only studied the spine, these models should provide insight into optimizing sampling rate and filter parameters for inertial measurements in general use.

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA.

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<103 km2) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990-2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as "true loads". Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. However, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

A Methodological Checklist for Studies of Pleasure and Enjoyment Responses to High-Intensity Interval Training: Part II. Intensity, Timing of Assessments, Data Modeling, and Interpretation.

Recent studies have concluded that high-intensity interval training should be seen as a "viable alternative" to, and may be more enjoyable than, moderate-intensity continuous exercise. If true, these claims have the potential to revolutionize the science and practice of exercise, establishing high-intensity interval training as not only a physiologically effective exercise modality but also a potentially sustainable one. However, these claims stand in contrast to voluminous evidence according to which high levels of exercise intensity are typically experienced as less pleasant than moderate levels. To help researchers, peer reviewers, editors, and critical readers appreciate possible reasons for the apparently conflicting results, we present a checklist that identifies crucial methodological elements in studies investigating the effects of high-intensity interval training on affect and enjoyment. This second installment covers how "high-intensity" and "moderate-intensity" experimental conditions are defined, the timing of assessments of affect, the modeling of affective responses, and data interpretation.

Impact of ActiGraph sampling rate on free-living physical activity measurement in youth.

ActiGraph sampling frequencies of more than 30 Hz may result in overestimation of activity counts in both children and adults, but research on free-living individuals has not included the range of sampling frequencies used by researchers.Objective.We compared count- and raw-acceleration-based metrics from free-living children and adolescents across a range of sampling frequencies.Approach.Participants (n = 445; 10-15 years of age) wore an ActiGraph accelerometer for at least one 10 h day. Vector magnitude counts, mean amplitude deviation, monitor-independent movement summary units, and activity intensity classified using six methods (four cut-points, two-regression model, and artificial neural network) were compared between 30 Hz and 60, 80, 90, and 100 Hz sampling frequencies using mean absolute differences, correlations, and equivalence testing.Main results.All outcomes were statistically equivalent, and correlation coefficients were ≥0.970. Absolute differences were largest for the 30 versus 80 and 30 versus 100 Hz count comparisons. For comparisons of 30 with 60, 80, 90, or 100 Hz, mean (and maximum) absolute differences in minutes of moderate-to-vigorous physical activity per day ranged from 0.1 to 0.3 (0.4 to 1.5), 0.3 to 1.3 (1.6 to 8.6), 0.1 to 0.3 (1.1 to 2.5), and 0.3 to 2.5 (1.6 to 14.3) across the six classification methods.Significance.Acceleration-based outcomes are comparable across the full range of sampling rates and therefore recommended for future research. If using counts, we recommend a multiple of 30 Hz because using a 100 Hz sampling rate resulted in large maximum individual differences and epoch-level differences, and increasing differences with activity level.

Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis.

Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test-retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93-0.96]. The concordance correlation coefficients for the two systems' agreement were [0.81-0.91] in the time domain and [0.43-0.99] in the frequency domain. The absolute biases estimated by the Bland-Altman method were [0.0005-0.008] in the time domain and [0-0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.

Incipient chatter fast and reliable detection method in high-speed milling process based on cumulative strategy.

This paper proposes an incipient chatter detection method to meet high dynamic applications' time and reliability constraints, such as high-speed milling involving heavy noise. The herein introduced method relies on a multiple sampling per revolution (MSPR) technique, coupled with two data preprocessing techniques, a modified adaptive cumulative chatter indicator, and a two-risk levels-based threshold. The MSPR technique enables collecting information-rich enough data to characterize the chatter dynamics thanks to a significant amount of data collected in each revolution. Therefore, the MSPR technique allows for acquiring the data using a short-time window, thus reducing the detection delay. Two data preprocessing techniques, i.e., Z-score normalization and mean-centered, are implemented for data integration and chatter information consolidation. The modified adaptive cumulative chatter indicator has three advantages: (a) it accumulates the information on the chatter feature and highlights the appearance of an incipient chatter; (b) it adapts to the variation of the environmental disturbance noises, resulting in enhanced detection reliability; (c) it is faster than the adaptive cumulative log-likelihood ratio (ACLLR) for decision-making statistically. The two-risk levels-based threshold overcomes the limitations of a unique threshold, and allows simultaneously assessing the two risk levels, thus improving detection reliability. We successfully applied the proposed method to detect incipient chatter in a digital high-speed milling process and assessed its effectiveness by comparing it with several existing chatter detection methods.

Evaluation of sampling frequency impact on the accuracy of water quality status as determined considering different water quality monitoring objectives.

Water quality sampling is a key element in tracking water quality monitoring objectives. However, frequencies adapted by different agencies might not be sufficient to provide an accurate indication of water quality status. In this study, data from low- and high-resolution water quality datasets were analyzed to determine the extent to which monitoring objectives could be achieved with different sampling frequencies, with a view to providing recommendations and best practices for water quality monitoring frequency in places with limited resources with which to implement a high-frequency monitoring plan. Water quality data from two watersheds (Maumee River and Raisin River) located in the Western Lake Erie Basin (WLEB) were used since these watersheds have consistent records over substantial periods of time, and the water quality data available have a high resolution (at least daily). The water quality constituents analyzed included suspended solids (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), and nitrate + nitrite (NO2+3). Sources of pollutants for watersheds located in the WLEB include contributions from point sources like discharges from sewage treatment plants and non-point sources such as agricultural and urban storm runoff. Weekly, bi-weekly, monthly, and seasonal datasets were created from the original datasets, following different sampling rules based on the day of the week, week of the month, and month of the year. The resulting datasets were then compared to the original dataset to determine how the sampling frequency would affect the results obtained in a water quality assessment when different monitoring objectives are considered. Results indicated that constituents easily transported by water (such as sediments and nutrients) require more than 50 samples/year to provide a small error (< 10%) with a confidence interval of 95%. Monthly and seasonal sampling were found appropriate to report a stream's prevailing water quality status and statistical properties. However, these resolutions might not be sufficient to capture long-term trends, in which case bi-weekly samples would be preferable. Limitations of low-resolution sampling frequency could be overcome by including rainfall events and random sampling during specific time windows as part of the monitoring plan.

Characterizing solute budgets of a tropical Andean páramo ecosystem.

Monitoring solute fluxes in water quality studies is essential to reveal potential ecosystem disturbances, and is particularly important in Andean headwater catchments as they are the main sources of water for downstream populations. However, such studies have mainly focused on organic matter and nutrients, disregarding other solutes that can threaten water quality (e.g. arsenic, lead, calcium or magnesium). Additionally, routine low-resolution (weekly or monthly) sampling schemes may overlook important solute dynamics. Therefore, we collected water samples every four hours for the analysis of twenty-four solutes in a pristine tropical Andean páramo catchment. Solute fluxes were calculated using five different methods. The 4-hourly data set was filtered to test for an optimum sampling frequency without compromising export rates. Based on the available 4-hourly data, the results showed that the interpolation export method was best suited, due to a weak correlation with discharges. Of the twenty-four solutes analyzed, Dissolved Organic Carbon (DOC), Total Nitrogen bound (TNb), Si, Ca, Mg, K, and Na presented the highest input rates (with DOC = 4.167E+08 mEq km-2 yr-1 and Si = 1.729E+07 mEq km-2 yr-1) and export rates (with DOC = 2.686E+08 mEq km-2 yr-1 and Si = 2.953E+08 mEq km-2 yr-1). Moreover, DOC, TNb, NH4-N, NO2-N, NO3-N, PO4, Al, B, Cu, Fe, Zn, As, Cd, Cr, Pb, and V presented more input than export, while Ca, K, Mg, Na, Rb, Si, Sr, and Ba presented more export than input (geogenic sources). Filtered sampling frequencies demonstrated that a minimum of daily grab samples would be required to obtain reliable export rates with differences consistently below 10%, when compared to the 4-hourly solute export. These findings can be particularly useful for the implementation of long-term monitoring programs at low cost, and they provide high-quality information, for the first time, on biogeochemical budgets in a pristine páramo catchment.

A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater.

Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.

The effects of assessment intensity on participant burden, compliance, within-person variance, and within-person relationships in ambulatory assessment.

Considering the very large number of studies that have applied ambulatory assessment (AA) in the last decade across diverse fields of research, knowledge about the effects that these design choices have on participants' perceived burden, data quantity (i.e., compliance with the AA protocol), and data quality (e.g., within-person relationships between time-varying variables) is surprisingly restricted. The aim of the current research was to experimentally manipulate aspects of an AA study's assessment intensity-sampling frequency (Study 1) and questionnaire length (Study 2)-and to investigate their impact on perceived burden, compliance, within-person variability, and within-person relationships between time-varying variables. In Study 1, students (n = 313) received either 3 or 9 questionnaires per day for the first 7 days of the study. In Study 2, students (n = 282) received either a 33- or 82-item questionnaire three times a day for 14 days. Within-person variability and within-person relationships were investigated with respect to momentary pleasant-unpleasant mood and state extraversion. The results of Study 1 showed that a higher sampling frequency increased perceived burden but did not affect the other aspects we investigated. In Study 2, longer questionnaire length did not affect perceived burden or compliance but yielded a smaller degree of within-person variability in momentary mood (but not in state extraversion) and a smaller within-person relationship between state extraversion and mood. Differences between Studies 1 and 2 with respect to the type of manipulation of assessment intensity are discussed.

The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population.

Currently, little is known about the association between assessment intensity, burden, data quantity, and data quality in experience sampling method (ESM) studies. Researchers therefore have insufficient information to make informed decisions about the design of their ESM study. Our aim was to investigate the effects of different sampling frequencies and questionnaire lengths on burden, compliance, and careless responding. Students (n = 163) received either a 30- or 60-item questionnaire three, six, or nine times per day for 14 days. Preregistered multilevel regression analyses and analyses of variance were used to analyze the effect of design condition on momentary outcomes, changes in those outcomes over time, and retrospective outcomes. Our findings offer support for increased burden and compromised data quantity and quality with longer questionnaires, but not with increased sampling frequency. We therefore advise against the use of long ESM questionnaires, while high-sampling frequencies do not seem to be associated with negative consequences.