Hierarchical Bayesian Framework Research Articles

A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems

ABSTRACT Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ( 1 ≤ M g C h a − 1 ≤ 135 ) and the Sentinel-2 ( 1 ≤ M g C h a − 1 ≤ 130 ) -based C stock models than C stock predictions published in related studies. Distance to settlements ( D I S T ) and N D V I are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ( 1 ≤ M g C h a − 1 ≤ 4 ) 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ( 2 ≤ M g C h a − 1 ≤ 4 ) associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.

Intraoperative parathyroid hormone monitoring in parathyroidectomy for hyperparathyroidism: a protocol for a network meta-analysis of diagnostic test accuracy.

Intraoperative parathyroid hormone (iPTH) monitoring is standard-of-care in the surgical management of hyperparathyroidism. It involves real-time determination of circulating PTH levels to guide parathyroid gland excision. There exists several iPTH monitoring criteria, such as the Miami criteria, and a lack of standardization in the timing of post-parathyroid gland excision samples. We present a protocol of a systematic review and network meta-analysis of diagnostic test accuracy to identify the iPTH criteria and post-gland excision timepoint that best predicts surgical cure in hyperparathyroidism. The database search strategy will be developed in conjunction with a librarian specialist. We will perform a search of Medline (Ovid), EMBASE (Ovid), CINAHL, Cochrane Collaboration, and Web of Science from 1990-present. Studies will be eligible if they include adult patients diagnosed with hyperparathyroidism who undergo parathyroidectomy with iPTH monitoring. We will only include studies that report diagnostic test properties for iPTH criteria and/or post-excision sampling timepoints. All screening, full-text review, data extraction, and critical appraisal will be performed in duplicate. Critical appraisal will be performed using QUADAS-2 instrument. A descriptive analysis will present study and critical appraisal characteristics. We will perform evaluation of between-study heterogeneity using I2 and Cochrane Q and where applicable, we will perform sensitivity analysis. Our network meta-analysis will include Bayesian hierarchical framework with random effects using multiple models. Ethics approval is not required. This proposed systematic review will utilize a novel Bayesian network meta-analysis model to help standardize iPTH monitoring in hyperparathyroidism, thereby optimizing patient outcomes and healthcare expenditures.

Relation of prenatal and postnatal PM2.5 exposure with cognitive and motor function among preschool-aged children

The literature informing susceptible periods of exposure on children's neurodevelopment is limited. We evaluated the impacts of pre- and postnatal fine particulate matter (PM2.5) exposure on children's cognitive and motor function among 1303 mother-child pairs in the Spanish INMA (Environment and Childhood) Study. Random forest models with temporal back extrapolation were used to estimate daily residential PM2.5 exposures that we averaged across 1-week lags during the prenatal period and 4-week lags during the postnatal period. The McCarthy Scales of Children's Abilities (MSCA) were administered around 5 years to assess general cognitive index (GCI) and several subscales (verbal, perceptual-performance, memory, fine motor, gross motor). We applied distributed lag nonlinear models within the Bayesian hierarchical framework to explore periods of susceptibility to PM2.5 on each MSCA outcome. Effect estimates were calculated per 5 μg/m3 increase in PM2.5 and aggregated across adjacent statistically significant lags using cumulative β (βcum) and 95% Credible Intervals (95%CrI). We evaluated interactions between PM2.5 with fetal growth and child sex. We did not observe associations of PM2.5 exposure with lower GCI scores. We found a period of susceptibility to PM2.5 on fine motor scores in gestational weeks 1–9 (βcum = −2.55, 95%CrI = −3.53,-1.56) and on gross motor scores in weeks 7–17 (βcum = −2.27,95%CrI = −3.43,-1.11) though the individual lags for the latter were only borderline statistically significant. Exposure in gestational week 17 was weakly associated with verbal scores (βcum = −0.17, 95%CrI = −0.26,-0.09). In the postnatal period (from age 0.5–1.2 years), we observed a window of susceptibility to PM2.5 on lower perceptual-performance (β = −2.42, 95%CrI = −3.37,-1.46). Unexpected protective associations were observed for several outcomes with exposures in the later postnatal period. We observed no evidence of differences in susceptible periods by fetal growth or child sex. Preschool-aged children's motor function may be particularly susceptible to PM2.5 exposures experienced in utero whereas the first year of life was identified as a period of susceptibility to PM2.5 for children's perceptual-performance.

Sparse Bayesian learning based on spatio-temporal structure-aware for matched field processing.

In the application of the matched field processing (MFP) algorithm for underwater acoustic source localization, the measurements at each time step are conventionally processed independently. This study incorporates the prior information about the continuous spatial changes of the source over time under realistic conditions, a factor anticipated to improve localization performance. In this paper, a sparse Bayesian learning (SBL) algorithm based on the spatio-temporal structure-aware is described. We exploit a structure prior for sparse coefficients to capture the continuous spatial structure between adjacent time steps. Moreover, the sparse coefficient can automatically select the update method, utilizing the statistical information from adjacent neighbors or updating independently. The hidden variables in the hierarchical Bayesian framework are inferred via variational Bayesian inference (VBI). Additionally, we extend the proposed method to the multi-frequency case. This method inherits the advantages of the SBL and further reduces position estimation errors. Compared to other approaches, the construction of an accurate motion model is not required. The efficacy of the proposed algorithm is demonstrated through simulation examples and an analysis of the SWellEx-96 experimental data.

Impact of fishing activities on the population dynamics of European whitefish in four peri-alpine lakes

Inland fisheries play a crucial role in socio-economic activities and food production. However, these fisheries are often less intensively monitored and studied than marine fisheries. In the past two decades, important fluctuations in European whitefish (Coregonus lavaretus) landings have been observed in four peri-alpine lakes in France and Switzerland. To better gauge the impact of fishing removal, a multi-lakes Delay-Difference model was developed and adapted to these fisheries. A hierarchical state-space Bayesian framework was used to integrate stochasticity for each process and incorporate diverse data. The model gave estimates of time series for biomass, recruitment, stage-structured landings, and fishing mortality, as well as estimates of natural mortality rate, Brody growth coefficient and mean recruitment productivity. Differences in the estimates of variables predicted by the model were observed among lakes. Negative impacts of exploitation have been highlighted for Lakes Geneva, Bourget, and Annecy. Low levels of biomass under high fishing mortality rates were observed, while landings tended to increase under lower mortality rates. Lakes Geneva and Bourget outputs suggest stocks were impacted by fishing activities and that fishing effort reduction could ultimately increase yields. Lake Annecy abundance and landings trends increased after 2010, suggesting management strategies that focused on effort reduction were successful. Lake Neuchâtel stock abundance was low after 2017 and probably more fragile due to low biomass levels and recruitment. The observed changes in European whitefish biomass cannot be attributed solely to fishing, suggesting that environmental factors may also influence recruitment success alongside the impacts of fishing pressure.

Volume Density Structure of the Central Molecular Zone NGC 253 through ALCHEMI Excitation Analysis

We present a spatially resolved excitation analysis for the central molecular zone (CMZ) of the starburst galaxy NGC 253 using the data from the Atacama Large Millimeter/submillimeter Array Comprehensive High-resolution Extragalactic Molecular Inventory, whereby we explore parameters distinguishing NGC 253 from the quiescent Milky Way’s Galactic center (GC). Non-LTE analyses employing a hierarchical Bayesian framework are applied to Band 3–7 transitions from nine molecular species to delineate the position–position–velocity distributions of column density ( NH2 ), volume density ( nH2 ), and temperature (T kin) at 27 pc resolution. Two distinct components are detected: a low-density component with (nH2,Tkin)∼(103.3cm−3,85K) and a high-density component with (nH2,Tkin)∼(104.4cm−3,110K) , separated at nH2∼103.8cm−3 . NGC 253 has ∼10 times the high-density gas mass and ∼3 times the dense-gas mass fraction of the GC. These properties are consistent with their HCN/CO ratio but cannot alone explain the factor of ∼30 difference in their star formation efficiencies (SFEs), contradicting the dense-gas mass to star formation rate scaling law. The nH2 histogram toward NGC 253 exhibits a shallow declining slope up to nH2∼106cm−3 , while that of the GC steeply drops in nH2≳104.5cm−3 and vanishes at 105 cm−3. Their dense-gas mass fraction ratio becomes consistent with their SFEs when the threshold nH2 for the dense gas is taken at ∼104.2−4.6 cm−3. The rich abundance of gas above this density range in the NGC 253 CMZ, or its scarcity in the GC, is likely to be the critical difference characterizing the contrasting star formation in the centers of the two galaxies.

Approximating the zero-norm penalized sparse signal recovery using a hierarchical Bayesian framework

Sparse Bayesian learning (SBL) with its self-regulation and uncertainty estimation features, has become a popular topic in the field of sparse signal recovery. However, it is a challenging problem to employ zero-norm penalty on signals in the hierarchical framework of SBL. In this paper, an SBL approach that approximates the zero-norm penalized spare signal recovery is proposed based on the idea that signals can be modeled using generalized Gaussian distribution (GGDs). It should be noted that the mean and variance of GGDs are related to its shape parameters, making the direct use of GGDs in Bayesian inference impractical. To solve this problem, we derives a proposition and further build a new hierarchical Bayesian framework (HrBayFw). The proposed HrBayFw is equivalent to assign ℓp-norm penalty to signals, and the parameter p can be designed to approximate 0. Thus, the zero-norm penalized spare signal recovery can be realized using SBL, and the use of the proposition enables tractable marginalization over all parameters. The main advantage of the proposed approach is that it achieves a lower reconstruction error than current Bayesian methods. Numerical simulations evidence that the proposed SBL approach achieves better accuracy performance in terms of normalized-mean-square-error comparing with other contemporary algorithms.

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

ObjectiveTo investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. MethodsDescription of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR).The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. ResultsThe proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. ConclusionsNumerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. SignificanceThe proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Bayesian inference for prediction of survival probability inprime-boost vaccination regimes.

We focus on Bayesian inference for survival probabilities in a prime-boost vaccination regime in the development of an Ebola vaccine. We are interested in the heterologous prime-boost regimen (unmatched vaccine deliverys using the same antigen) due to its demonstrated durable immunity, well-tolerated safety profile, and suitability as a population vaccination strategy. Our research is motivated by the need to estimate the survival probability given the administered dosage. To do so, we establish two key relationships. Firstly, we model the connection between the designed dose concentration and the induced antibody count using a Bayesian response surface model. Secondly, we model the association between the antibody count and the probability of survival when experimental subjects are exposed to the Ebola virus in a controlled setting using a Bayesian probability of survival model. Finally, we employ a combination of the two models with dose concentration as the predictor of the survival probability for a future vaccinated population. We implement our two-level Bayesian model in Stan, and illustrate its use with simulated and real-world data. Performance of this model is evaluated via simulation. Our work offers a new application of drug synergy models to examine prime-boost vaccine efficacy, and does so using a hierarchical Bayesian framework that allows us to use dose concentration to predict survival probability.

Error modeling and hierarchical Bayesian fusion for spaceborne and ground radar rainfall data

The optimal fusion of multisource rainfall data can improve the accuracy and resolution of a posteriori estimates, which is beneficial for hydrological and meteorological applications. In this study, the errors of ground-based dual-polarization radar (GR) and dual-frequency precipitation radar (DPR) on global precipitation measurement (GPM) satellite rainfall estimation were modeled, and a hierarchical Bayesian (HB) framework was developed for GR and DPR rainfall data fusion. According to the matched optimal estimated GR rainfall and rain gauge data, the GR rainfall error was analyzed, and the error variance varying with rain type and intensity was described and modeled. The rain types were classified based on disdrometer and dual-polarization radar data. By comparing the DPR rainfall product with GR rainfall, the DPR rainfall error was divided into systematic and random errors. The systematic error was linearly fitted, and the probability distributions for the random error and the variable error parameters were analyzed and modeled. Because the parameters for GR and DPR rainfall errors varied with rain type and intensity, we developed a HB fusion approach considering the varying parameters in rainfall error modeling. The performance of the fusion algorithm was evaluated using matched GR and DPR cases, which showed that the HB fusion can effectively improve the consistency between the original DPR rainfall product and rain gauge data, reducing the relative bias by an average of 30% and improving the correlation by an average of 20%. Compared with the conventional Bayesian (CB) fusion algorithm, which is based on overall error modeling, the HB fusion results can improve the root mean square error by an average of 24% and relative bias by 14%.

Modelling orthographic similarity effects in recognition memory reveals support for open bigram representations of letter coding

A variety of letter string representations has been proposed in the reading literature to account for empirically established orthographic similarity effects from masked priming studies. However, these similarity effects have not been explored in episodic memory paradigms and very few memory models have employed orthographic representation of words. In the current work, through two recognition memory experiments employing word and pseudoword stimuli respectively, we empirically established a set of key orthographic similarity effects for the first time in recognition memory – namely the substitution effect, transposition effect and reverse effect in recognition memory of words and pseudowords, and a start-letter importance in recognition memory of words. Subsequently, we compared orthographic representations from the reading literature including slot coding, closed-bigram, open-bigram and the overlap model. Each of these representations was situated in a global matching model and fitted to recognition performance via Luce’s choice rule in a hierarchical Bayesian framework. Model selection results showed support for the open-bigram representation in both experiments.

A hierarchical Bayesian modeling framework for identification of Non-Gaussian processes

Non-Gaussian processes are frequently encountered in engineering problems, posing a challenge when it comes to identification. The main challenge in the identification arises from the fact that a non-Gaussian process can be treated as a collection of infinite dimensional non-Gaussian variables. The application of the hierarchical Bayesian modeling (HBM) framework is constrained due to the inherent complexity of dimensionality and non-Gaussian characteristics associated with these variables. To tackle the issue of dimensionality, the improved orthogonal series expansion (iOSE) representing a non-Gaussian process by time functions with non-Gaussian coefficients, which are readily obtained from discretizing the process at some specific time points, is introduced within the HBM framework. In particular, the iOSE is embedded to convert the identification of a non-Gaussian process into a finite number of non-Gaussian coefficients. Regarding their non-Gaussian characteristics, polynomial chaos expansion (PCE) is used to quantify the uncertainty of the non-Gaussian coefficients with parameters in PCE treated as hyper parameters to be estimated by the HBM framework. The proposed framework is applicable to the identification of both stationary and nonstationary non-Gaussian processes. The effectiveness of non-Gaussian process quantification by the proposed framework is demonstrated using simulated data of a non-stationary extreme value process. Theapplicability of this approach for non-Gaussian process identification is validated by accurately identifying a stochastic load in a structural dynamic problem. Furthermore, it is successfully applied to the reconstruction of random mode shapes of a building arising from different environmental conditions.

The intrinsic X-ray luminosity distribution of an optically selected SDSS quasar population

ABSTRACT In active galactic nuclei, the relationship between UV and X-ray luminosity is well studied (often characterised by αox) but often with heterogeneous samples. We have parametrized the intrinsic distribution of X-ray luminosity, LX, for the optically selected sample of SDSS quasars in the Stripe 82 and XXL fields across redshifts 0.5–3.5. We make use of the available XMM observations and a custom pipeline to produce Bayesian sensitivity curves that are used to derive the intrinsic X-ray distribution in a hierarchical Bayesian framework. We find that the X-ray luminosity distribution is well described by a Gaussian function in log10 LX space with a mean that is dependent on the monochromatic 2500 Å UV luminosity, L2500. We also observe some redshift dependence of the distribution. The mean of the LX distribution increases with redshift while the width decreases. This weak but significant redshift dependence leads to L2500–LX and L2500–αox relations that evolve with redshift, and we produce a redshift- and L2500-dependent αox equation. Neither black hole mass nor Eddington ratio appear to be potential drivers of the redshift evolution.

How effective were Australian Quarantine Stations in mitigating transmission aboard ships during the influenza pandemic of 1918-19?

The influenza pandemic of 1918-19 was the most devastating pandemic of the 20th century. It killed an estimated 50-100 million people worldwide. In late 1918, when the severity of the disease was apparent, the Australian Quarantine Service was established. Vessels returning from overseas and inter-state were intercepted, and people were examined for signs of illness and quarantined. Some of these vessels carried the infection throughout their voyage and cases were prevalent by the time the ship arrived at a Quarantine Station. We study four outbreaks that took place on board the Medic, Boonah, Devon, and Manuka in late 1918. These ships had returned from overseas and some of them were carrying troops that served in the First World War. By analysing these outbreaks under a stochastic Bayesian hierarchical modeling framework, we estimate the transmission rates among crew and passengers aboard these ships. Furthermore, we ask whether the removal of infectious, convalescent, and healthy individuals after arriving at a Quarantine Station in Australia was an effective public health response.

Susceptible windows of prenatal and postnatal fine particulate matter exposures and attention-deficit hyperactivity disorder symptoms in early childhood

Few prior studies have explored windows of susceptibility to fine particulate matter (PM2.5) in both the prenatal and postnatal periods and children's attention-deficit/hyperactivity disorder (ADHD) symptoms.We analyzed data from 1416 mother-child pairs from the Spanish INMA (INfancia y Medio Ambiente) Study (2003–2008). Around 5 years of age, teachers reported the number of ADHD symptoms (i.e., inattention, hyperactivity/impulsivity) using the ADHD Diagnostic and Statistical Manual of Mental Disorders. Around 7 years of age, parents completed the Conners' Parent Rating Scales, from which we evaluated the ADHD index, cognitive problems/inattention, hyperactivity, and oppositional subscales, reported as age- and sex-standardized T-scores. Daily residential PM2.5 exposures were estimated using a two-stage random forest model with temporal back-extrapolation and averaged over 1-week periods in the prenatal period and 4-week periods in the postnatal period. We applied distributed lag non-linear models within the Bayesian hierarchical model framework to identify susceptible windows of prenatal or postnatal exposure to PM2.5 (per 5-μg/m3) for ADHD symptoms. Models were adjusted for relevant covariates, and cumulative effects were reported by aggregating risk ratios (RRcum) or effect estimates (βcum) across adjacent susceptible windows.A similar susceptible period of exposure to PM2.5 (1.2–2.9 and 0.9–2.7 years of age, respectively) was identified for hyperactivity/impulsivity symptoms assessed ~5 years (RRcum = 2.72, 95% credible interval [CrI] = 1.98, 3.74) and increased hyperactivity subscale ~7 years (βcum = 3.70, 95% CrI = 2.36, 5.03). We observed a susceptibility period to PM2.5 on risk of hyperactivity/impulsivity symptoms ~5 years in gestational weeks 16–22 (RRcum = 1.36, 95% CrI = 1.22, 1.52). No associations between PM2.5 exposure and other ADHD symptoms were observed.We report consistent evidence of toddlerhood as a susceptible window of PM2.5 exposure for hyperactivity in young children. Although mid-pregnancy was identified as a susceptible period of exposure on hyperactivity symptoms in preschool-aged children, this association was not observed at the time children were school-aged.

EEG based emotion recognition by hierarchical bayesian spectral regression framework

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

A Bayesian Semi-parametric Modelling Approach for Area Level Small Area Studies

We present a new semiparametric extension of the Fay-Herriot model, termed the agnostic Fay-Herriot model (AGFH), in which the sampling-level model is expressed in terms of an unknown general function [Formula: see text]. Thus, the AGFH model can express any distribution in the sampling model since the choice of [Formula: see text] is extremely broad. We propose a Bayesian modelling scheme for AGFH where the unknown function [Formula: see text] is assigned a Gaussian Process prior. Using a Metropolis within Gibbs sampling Markov Chain Monte Carlo scheme, we study the performance of the AGFH model, along with that of a hierarchical Bayesian extension of the Fay-Herriot model. Our analysis shows that the AGFH is an excellent modelling alternative when the sampling distribution is non-Normal, especially in the case where the sampling distribution is bounded. It is also the best choice when the sampling variance is high. However, the hierarchical Bayesian framework and the traditional empirical Bayesian framework can be good modelling alternatives when the signal-to-noise ratio is high, and there are computational constraints. AMS subject classification: 62D05; 62F15

Evidence That the Occurrence Rate of Hot Jupiters around Sun-like Stars Decreases with Stellar Age

We investigate how the occurrence rate of giant planets (minimum mass > 0.3 M Jup) around Sun-like stars depends on the age, mass, and metallicity of their host stars. We develop a hierarchical Bayesian framework to infer the number of planets per star (NPPS) as a function of both planetary and stellar parameters. The framework fully takes into account the uncertainties in the latter by utilizing the posterior samples for the stellar parameters obtained by fitting stellar isochrone models to the spectroscopic parameters, Gaia DR3 parallaxes, and 2MASS K s-band magnitudes adopting a certain bookkeeping prior. We apply the framework to 46 Doppler giants found around a sample of 382 Sun-like stars from the California Legacy Survey catalog that publishes spectroscopic parameters and search completeness for all the surveyed stars. We find evidence that the NPPS of hot Jupiters (orbital period P = 1–10 days) decreases roughly in the latter half of the main sequence over the timescale of , while that of cold Jupiters (P = 1–10 yr) does not. Assuming that this decrease is real and caused by tidal orbital decay, the modified stellar tidal quality factor is implied to be for a Sun-like main-sequence star orbited by a Jupiter-mass planet with P ≈ 3 days.

Environmental surveillance for COVID-19 using SARS-CoV-2 RNA concentration in wastewater – a study in District East, Karachi, Pakistan

Wastewater-based surveillance is used to track the temporal patterns of the SARS-CoV-2 virus in communities. Viral RNA particle detection in wastewater samples can indicate an outbreak within a catchment area. We describe the feasibility of using a sewage network to monitor SARS-CoV-2 trend and use of genomic sequencing to describe the viral variant abundance in an urban district in Karachi, Pakistan. This was among the first studies from Pakistan to demonstrate the surveillance for SARS-CoV-2 from a semi-formal sewage system. Four sites draining into the Lyari River in District East, Karachi, were identified and included in the current study. Raw sewage samples were collected early morning twice weekly from each site between June 10, 2021 and January 17, 2022, using Bag Mediated Filtration System (BMFS). Secondary concentration of filtered samples was achieved by ultracentrifugation and skim milk flocculation. SARS-CoV-2 RNA concentrations in the samples were estimated using PCR (Qiagen ProMega kits for N1 & N2 genes). A distributed-lag negative binomial regression model within a hierarchical Bayesian framework was used to describe the relationship between wastewater RNA concentration and COVID-19 cases from the catchment area. Genomic sequencing was performed using Illumina iSeq100. Among the 151 raw sewage samples included in the study, 123 samples (81.5%) tested positive for N1 or N2 genes. The average SARS-CoV-2 RNA concentrations in the sewage samples at each lag (1-14 days prior) were associated with the cases reported for the respective days, with a peak association observed on lag day 10 (RR: 1.15; 95% Credible Interval: 1.10-1.21). Genomic sequencing showed that the delta variant dominated till September 2022, while the omicron variant was identified in November2022. Wastewater-based surveillance, together with genomic sequencing provides valuable information for monitoring the community temporal trend of SARS-CoV-2. PATH, Bill & Melinda Gates Foundation, and Global Innovation Fund.

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study.

Leptospirosis, a global zoonotic disease, is prevalent in tropical and subtropical regions, including Fiji where it's endemic with year-round cases and sporadic outbreaks coinciding with heavy rainfall. However, the relationship between climate and leptospirosis has not yet been well characterised in the South Pacific. In this study, we quantify the effects of different climatic indicators on leptospirosis incidence in Fiji, using a time series of weekly case data between 2006 and 2017. We used a Bayesian hierarchical mixed-model framework to explore the impact of different precipitation, temperature, and El Niño Southern Oscillation (ENSO) indicators on leptospirosis cases over a 12-year period. We found that total precipitation from the previous six weeks (lagged by one week) was the best precipitation indicator, with increased total precipitation leading to increased leptospirosis incidence (0.24 [95% CrI 0.15-0.33]). Negative values of the Niño 3.4 index (indicative of La Niña conditions) lagged by four weeks were associated with increased leptospirosis risk (-0.2 [95% CrI -0.29 --0.11]). Finally, minimum temperature (lagged by one week) when included with the other variables was positively associated with leptospirosis risk (0.15 [95% CrI 0.01-0.30]). We found that the final model was better able to capture the outbreak peaks compared with the baseline model (which included seasonal and inter-annual random effects), particularly in the Western and Northern division, with climate indicators improving predictions 58.1% of the time. This study identified key climatic factors influencing leptospirosis risk in Fiji. Combining these results with demographic and spatial factors can support a precision public health framework allowing for more effective public health preparedness and response which targets interventions to the right population, place, and time. This study further highlights the need for enhanced surveillance data and is a necessary first step towards the development of a climate-based early warning system.