Massive Sampling Research Articles

Massive citizen science sampling and integrated taxonomic approach unravel Danish cryptogam-dwelling tardigrade fauna

Tardigrade diversity and distribution are enigmatic in most parts of the globe, and only some European countries can boast of a relatively well-studied water bear fauna. However, even these suffer from the lack of genetic data, which would substantiate faunistic data and make biogeographic comparisons easier. Denmark has never been intensively and systematically researched in this regard, thus a citizen science sampling of cryptogams (mosses, liverworts, and lichens) was launched in spring 2023, aiming at a comprehensive biodiversity survey across this insular country. Nearly 700 samples were selected out of 8.000 sent to NHMD, based on the quality of samples, representativeness of various regions of Denmark, and the type of substrate to allow unravelling of potential ecological associations between tardigrades and cryptogams. Importantly, a large fraction of morphological identifications was backed up by DNA barcode data based on ITS-2 (1001 sequences), and in some cases also on COI (93 sequences) and ITS-1 (22 sequences) molecular markers, which are recognised DNA fragments used in species delimitation. We quadruple the number of known Danish limno-terrestrial tardigrade species (55 spp. reported in this paper vs. 14 spp. reported in literature so far, most of which were contentious due to the insufficient knowledge on tardigrade taxonomy), demonstrating the power of integrative taxonomy. No fewer than nine spp. are new to science. This is the first case where tardigrade fauna of an entire country is examined both from morphological and DNA barcoding data perspective.

Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire

Abstract Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.

Bacterial ecology and evolution converge on seasonal and decadal scales.

Ecology and evolution are often viewed as distinct processes, which interact on contemporary time scales in microbiomes. To observe these processes in a natural system, we collected a two-decade, 471-sample freshwater lake time series, creating the longest metagenome dataset to date. Among 2,855 species-representative genomes, diverse species and strains followed cyclical seasonal patterns, and one in five species experienced decadal shifts in strain composition. The most globally abundant freshwater bacterium had constant species-level abundance, but environmental extremes appeared to trigger a shift in strain composition and positive selection of amino acid and nucleic acid metabolism genes. These genes identify organic nitrogen compounds as potential drivers of freshwater responses to global change. Seasonal and long-term strain dynamics could be regarded as ecological processes or equivalently as evolutionary change. Rather than as distinct processes that interact, we propose a conceptualization where ecology and evolution converge along a continuum to better describe change in diverse microbial communities.

Spin‐Transfer‐Torque Magnetic Tunnel Junction Nonlinear In‐Sensor Computing Synapse for Improving the Performance of the Feedforward Neural Network

In‐sensor computing architecture has a great advantage especially in massive data sampling, transfer, and processing compared with the separated intelligent sensor systems. However, most of the in‐sensor computing device is proposed based on the traditional neural network model, where the synapse performs linear multiplication of input and weight. This approach fails to make the most use of the nonlinearity of in‐sensor computing devices. Therefore, in this article, first a modified feedforward neural network model with the nonlinear in‐sensor computing synapse (NSCS) located at the input layer is presented, and the backpropagation (BP) algorithm is modified to train the network. Then, the nonlinear characteristics of the NSCS composed of the spin‐transfer‐torque magnetic tunnel junction (STT–MTJ) devices and simple complementary metal‐oxide‐semiconductor (CMOS) circuit are analyzed. Based on the nonlinear response of STT–MTJ NSCS, the small‐scale network with NSCS synapse is experimented on the Modified National Institute of Standards and Technology dataset and compared with the traditional network of the same network size. In the simulation result, it is shown that better performance can be achieved with the STT–MTJ NSCS, including a 2–15 times improvement in convergence speed and a 2.5%–5.1% increase in accuracy.

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations.

The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.

AFsample: improving multimer prediction with AlphaFold using massive sampling.

The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated ∼6000 models per target compared with 25 default for AlphaFold-Multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared with AlphaFold-Multimer v2 it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared with all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modeling multimeric structures, alternate conformations, or flexible structures. AFsample is available online at http://wallnerlab.org/AFsample.

Leaf morphology and anatomy of Schlechtendalia luzulifolia, a basal member of subfamily Barnadesioideae (Asteraceae)

Abstract Based on molecular phylogenetic studies, Barnadesioideae have been proposed to be the basal subfamily of Asteraceae. This is a complex of 10 genera and 87 species distributed primarily along the Andean mountains, Patagonia, and into southern Brazil and Uruguay. Phylogenetic analyses have recovered all genera as monophyletic groups and have provided insights to their inter-relationships. Four generic clades have been substantiated: (1) Chuquiraga, Doniophyton, and Duseniella; (2) Dasyphyllum; (3) Barnadesia and Huarpea; and (4) Archidasyphyllum, Arnaldoa, and Fulcaldea. The remaining genus, the monospecific Schlechtendalia, has been an outlier in the subfamily, with some previous analyses recovering it as basal for the entire subfamily, and others showing it as sister to Barnadesia and Huarpea (with weak support) as well as to other genera. Recent massive sampling of loci has confirmed Schlechtendalia as the sister genus for the subfamily. Schlechtendalia luzulifolia has morphology atypical for Asteraceae. The capitula are loose aggregations of florets, and the leaves are long and strap shaped, more reminiscent of monocots. Morphological and anatomical investigations of the leaves reveal long, laminar blades with parallelodromous vascularization. The vesture is often with ‘barnadesioid trichomes’, especially towards the base of the plant, plus additional uniseriate trichomes consisting of 3 to many cells, newly reported for the subfamily. Some glandular trichomes with 2-4 short cells also occur. The transverse anatomy of the leaves reveals a single epidermal layer on both surfaces, which also contain the stomata (the leaf being amphistomatic). The mesophyll is undifferentiated; the vascular traces are surrounded by sclerenchyma that not only encircles the traces but also extends towards the epidermis and connects with it. The morphology and anatomy of the leaves of Schlechtendalia are divergent in comparison with other genera of the subfamily. Chuquiraga, Doniophyton, and Huarpea have leaf adaptations for survival in xeric habitats, such as dense pubescence, grey surfaces, and revolute margins. Schlechtendalia, in contrast, is adapted to a more mesic environment, especially near the Atlantic Ocean and along the Uruguay and La Plata rivers. The leaves are oriented upright, which correlates with undifferentiated mesophyll and stomata on both epidermal layers. The stem is an underground rhizome, an adaptation that permits survival during seasonal drought in the austral summer in Uruguay and adjacent regions. It is hypothesized that Schlechtendalia may have become adapted to more mesic environments in the Miocene prior to the rise of the Andes and development of the modern arid environments, into which many of the other genera of the subfamily subsequently radiated.

Improved multimer prediction using massive sampling with AlphaFold in CASP15.

AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: http://wallnerlab.org/AFsample/.

Barcoding coffee grounds - Exploring pteropod gastropod biodiversity with dregs in collection jars

Despite their cosmopolitan occurrence and massive plankton sampling during expeditions, the genetic diversity within Pteropoda Cuvier, 1804 is still largely unexplored. In this study we present a next-generation environmental barcoding approach to zooplankton bulk samples, which were collected during the circumglobal 2010 Malaspina expedition to evaluate pteropod diversity. We introduce a technique that avoids destructive procedures and leaves material intact for further morphological investigations. We extracted DNA out of the dregs (organic material such as mucus or body parts) of 27 sample containers for molecular barcoding (average 100-260 bp of COI). We were able to identify 7128 operational taxonomic units corresponding to the species composition contained in the examined samples. Among them were three species of thecosome pteropods, Creseis acicula, Creseis virgula and Cavolinia inflexa, which are discussed with respect to their taxonomy and their geographic distribution. Unidentified gymnosomes were also present in our samples from warmer regions in oceanic waters of the southern Indian Ocean. To facilitate identification of species, it is beneficial to create a better database of pteropod COI barcodes. Furthermore, gathering environmental barcoding data on a broad global scale will help to better understand species abundance and distribution of pteropods in the world’s oceans, and potentially those of other planktonic organisms.

Dependent state space Student‐t processes for imputation and data augmentation in plasma diagnostics

AbstractMultivariate time series measurements in plasma diagnostics present several challenges when training machine learning models: the availability of only a few labeled data increases the risk of overfitting, and missing data points or outliers due to sensor failures pose additional difficulties. To overcome these issues, we introduce a fast and robust regression model that enables imputation of missing points and data augmentation by massive sampling while exploiting the inherent correlation between input signals. The underlying Student‐t process allows for a noise distribution with heavy tails and thus produces robust results in the case of outliers. We consider the state space form of the Student‐t process, which reduces the computational complexity and makes the model suitable for high‐resolution time series. We evaluate the performance of the proposed method using two test cases, one of which was inspired by measurements of flux loop signals.

Alpha particle confinement metrics based on orbit classification in stellarators

We present orbit classification schemes for use as fast metrics for fusion alpha particle losses implemented in the symplectic guiding-centre code SIMPLE. Two variants respectively based on conservation of the parallel adiabatic invariant, and topology of footprints in Poincaré sections are introduced. Like an existing approach based on the Minkowski fractal dimension, those methods estimate whether a guiding-centre orbit is regular and therefore expected to be confined for infinite time in the collisionless case, or chaotic, which might lead to its loss. Compared with the existing approach, the required orbit tracing time for the novel classifiers is shorter by at least an order of magnitude. This enables massive sampling of orbits across the whole phase space to identify regular and chaotic regions for the purpose stellarator optimization. Based on conservation of the perpendicular invariant, we demonstrate how extended regular regions may act as radial barriers for orbits from the chaotic regions on the radially inboard side. We propose to use a quantified version of this property as a new metric for collisionless fusion alpha losses. As pitch-angle scattering becomes only relevant after alphas have already deposited a significant fraction of their energy, such a metric remains useful also for the case with collisions. This is illustrated by comparison with collisional loss computations. Results are presented for applications to two quasi-isodynamic configurations, a quasi-helical configuration and two quasi-axisymmetric configurations. In addition, the Hamiltonian action-angle formalism is used in quasi-axisymmetric configurations to investigate the overlap of drift-orbit resonances leading to chaos. The respective analysis is performed with the NEO-RT code originally developed for investigation of neoclassical toroidal viscous torque in tokamak plasmas.

Research on SAR Imaging Simulation Based on Time-Domain Shooting and Bouncing Ray Algorithm

The signal source of synthetic aperture radar (SAR) usually adopts the linear frequency modulation (LFM) signal, which exhibits the characteristics of a wide pulse. Hence, in the case of usage of the LFM signal by time-domain shooting and bouncing ray (TDSBR) to simulate the SAR echo signal, numerous time sampling points are generated, resulting in huge computational efforts; thereby, it is hard to exploit the TDSBR algorithm for simulating the SAR echo. In order to unlock this dilemma, the hybrid approach, the transfer function in conjunction with the range frequency-domain pulse coherence, is developed, in which the transfer function is stated by the radar cross-section. The proposed methodology is capable of enhancing the computational efficiency through avoiding the massive time sampling, so that the suggested TDSBR approach could be more conveniently applied to the SAR echo simulations. Furthermore, because of the efficiency advantage of the TDSBR in the calculation of wideband scattering field, the proposed methodology exhibits higher computational efficiency than the frequency-domain shooting and bouncing ray in the SAR image simulation. Finally, the key equation of the TDSBR for the transient scattering field of dielectric targets based on a closed-form integration formula is analytically derived, which is the basis of SAR imaging simulation for the composite scene of ships above the sea surface.

Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell

Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process.This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy.To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework.

Model Predictive Control of Quadruped Robot Based on Reinforcement Learning

For the locomotion control of a legged robot, both model predictive control (MPC) and reinforcement learning (RL) demonstrate powerful capabilities. MPC transfers the high-level task to the lower-level joint control based on the understanding of the robot and environment, model-free RL learns how to work through trial and error, and has the ability to evolve based on historical data. In this work, we proposed a novel framework to integrate the advantages of MPC and RL, we learned a policy for automatically choosing parameters for MPC. Unlike the end-to-end RL applications for control, our method does not need massive sampling data for training. Compared with the fixed parameters MPC, the learned MPC exhibits better locomotion performance and stability. The presented framework provides a new choice for improving the performance of traditional control.

Contributions of adaptation and purifying selection to SARS-CoV-2 evolution.

Continued evolution and adaptation of SARS-CoV-2 has led to more transmissible and immune-evasive variants with profound impacts on the course of the pandemic. Here I analyze the evolution of the virus over 2.5 years since its emergence and estimate the rates of evolution for synonymous and non-synonymous changes separately for evolution within clades-well-defined monophyletic groups with gradual evolution-and for the pandemic overall. The rate of synonymous mutation is found to be around 6 changes per year. Synonymous rates within variants vary little from variant to variant and are compatible with the overall rate of 7 changes per year (or [Formula: see text] per year and codon). In contrast, the rate at which variants accumulate amino acid changes (non-synonymous mutations) was initially around 12-16 changes per year, but in 2021 and 2022 it dropped to 6-9 changes per year. The overall rate of non-synonymous evolution, that is across variants, is estimated to be about 26 amino acid changes per year (or [Formula: see text] per year and codon). This strong acceleration of the overall rate compared to within clade evolution indicates that the evolutionary process that gave rise to the different variants is qualitatively different from that in typical transmission chains and likely dominated by adaptive evolution. I further quantify the spectrum of mutations and purifying selection in different SARS-CoV-2 proteins and show that the massive global sampling of SARS-CoV-2 is sufficient to estimate site-specific fitness costs across the entire genome. Many accessory proteins evolve under limited evolutionary constraints with little short-term purifying selection. About half of the mutations in other proteins are strongly deleterious.

The Accuracy of Semi-Empirical Quantum Chemistry Methods on Soot Formation Simulation

Soot molecules are hazardous compounds threatening human health. Computational chemistry provides efficient tools for studying them. However, accurate quantum chemistry calculation is costly for the simulation of large-size soot molecules and high-throughput calculations. Semi-empirical (SE) quantum chemistry methods are optional choices for balancing computational costs. In this work, we validated the performances of several widely used SE methods in the description of soot formation. Our benchmark study focuses on, but is not limited to, the validation of the performances of SE methods on reactive and non-reactive MD trajectory calculations. We also examined the accuracy of SE methods of predicting soot precursor structures and energy profiles along intrinsic reaction coordinate(s) (IRC). Finally, we discussed the spin density predicted by SE methods. The SE methods validated include AM1, PM6, PM7, GFN2-xTB, DFTB2, with or without spin-polarization, and DFTB3. We found that the shape of MD trajectory profiles, the relative energy, and molecular structures predicted by SE methods are qualitatively correct. We suggest that SE methods can be used in massive reaction soot formation event sampling and primary reaction mechanism generation. Yet, they cannot be used to provide quantitatively accurate data, such as thermodynamic and reaction kinetics ones.

Whole genome sequencing and the application of a SNP panel reveal primary evolutionary lineages and genomic variation in the lion (Panthera leo)

BackgroundPrevious phylogeographic studies of the lion (Panthera leo) have improved our insight into the distribution of genetic variation, as well as a revised taxonomy which now recognizes a northern (Panthera leo leo) and a southern (Panthera leo melanochaita) subspecies. However, existing whole range phylogeographic studies on lions either consist of very limited numbers of samples, or are focused on mitochondrial DNA and/or a limited set of microsatellites. The geographic extent of genetic lineages and their phylogenetic relationships remain uncertain, clouded by massive sampling gaps, sex-biased dispersal and incomplete lineage sorting.ResultsIn this study we present results of low depth whole genome sequencing and subsequent variant calling in ten lions sampled throughout the geographic range, resulting in the discovery of >150,000 Single Nucleotide Polymorphisms (SNPs). Phylogenetic analyses revealed the same basal split between northern and southern populations, as well as four population clusters on a more local scale. Further, we designed a SNP panel, including 125 autosomal and 14 mitochondrial SNPs, which was tested on >200 lions from across their range. Results allow us to assign individuals to one of these four major clades (West & Central Africa, India, East Africa, or Southern Africa) and delineate these clades in more detail.ConclusionsThe results presented here, particularly the validated SNP panel, have important applications, not only for studying populations on a local geographic scale, but also for tracing samples of unknown origin for forensic purposes, and for guiding conservation management of ex situ populations. Thus, these genomic resources not only contribute to our understanding of the evolutionary history of the lion, but may also play a crucial role in conservation efforts aimed at protecting the species in its full diversity.

Efficient application of stochastic Discrete Well Affinity (DiWA) proxy model with adjoint gradients for production forecast

In this paper, we describe adjoint gradient formulation for the Operator-Based Linearization modeling approach. Adjoint gradients are implemented in Delft Advanced Research Terra Simulator (DARTS) framework and applied for history matching using a proxy methodology. Due to the application of adjoint gradients, the computational efficiency of the discrete well affinity (DiWA) proxy model for production forecast is significantly improved. That allows us to derive several important extensions. The proxy methodology is further extended and validated for 3D three-phase black-oil problems. The results show that the gradient-based regression can provide good history matching and reconstruct a true petrophysical characterization when the initial guess is generated based on highly reliable geological information. For cases with a limited or not sufficient geological characterization, an efficient stochastic application of DiWA proxy model is proposed. This approach consists of massive sampling procedures for collecting different realizations based on high-fidelity statistics with filtering. These realizations are generated stochastically because they are not conditioned to any production information but the basic geological statistics of the reservoir. The trained DiWA proxy model demonstrates a small deviation between the model response and the observation data. When applying the refined DiWA model for the training, the error between the model response and observation data can be further reduced. The forecast based on the trained model has slightly larger variability but the deviation is still reasonable. The enhanced DiWA methodology presents an efficient and robust technique for creating an ensemble of stochastic proxy models that can be used in production forecast, flow diagnostic, and optimization. • A new adjoint framework using an Operator-Based Linearization approach is proposed. • The framework is extended for the application of a three-phase 3D proxy model. • The characterization of the petrophysical information of reservoir is discussed. • An efficient sampling approach is applied to stochastic Discrete Well Affinity model.

REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of “on the fly” biomarker validation, i.e., during the screening.