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In survey sampling, it might happen that information on the population mean of the auxiliary variable is not available, but it can be obtained if the researcher opts for it. The sampling design to be used in such a case is the two-phase sampling design. This design has been studied extensively in SRSWOR, but it has not been comprehensively studied when the population under study is rare or clumped. It is known that when the population under study is rare or clumped, adaptive cluster sampling (ACS) design is more efficient, and therefore in this paper we have studied the two-phase adaptive cluster sampling under transformed population approach and further proposed ratio and product estimator and a generalized robust ratio type estimator in this design. The bias and MSE of the proposed estimators have been derived and presented up to the first order of approximation. Further, the performance of the proposed estimators has been analyzed using simulation studies.

In survey sampling, it might happen that information on the population mean of the auxiliary variable is not available, but it can be obtained if the researcher opts for it. The sampling design to be used in such a case is the Two-Phase sampling design. This design has been studied extensively in SRSWOR, but it has not been studied when the population under study is rare or clumped. It is known that when the population under study is rare or clumped, adaptive cluster sampling (ACS) design is more efficient, and therefore in this paper we have proposed the Two-Phase Adaptive Cluster Sampling Under Transformed Population Approach and further proposed ratio and product estimator and a generalized robust ratio type estimator in this design. The bias and MSE of the proposed estimators have been derived and presented up to the first order of approximation. Further, the performance of the proposed estimators has been analyzed using simulation studies.

In recent years, new sampling techniques such as adaptive cluster sampling (ACS) have been developed. Since the theory of sample surveys plays an important role in the development of statistical science, different versions of ACS have been researched with different approaches. In this article, we also deal with the problem of estimating the mean of a hidden and rare clustered population utilizing auxiliary attribute information. Qualitative auxiliary data are significantly more accessible than quantitative auxiliary data in such populations. In this regard, we present two proposed classes of generalized regression-cum-exponential ratio type estimators under ACS with the transformed population approach. By the large sample approximation, we obtain mean square error of these two proposed classes of estimators using Taylor expansion. In addition to the calculations of theoretical comparisons and simulation studies, to investigate the optimal state of these two proposed classes in comparison with other existing estimators, we use experimental research among 179 countries of the world related to the estimation of the spread of the COVID-19 disease in its early stages, which severity is clustered and hidden. The results indicate the very high desirability of these two proposed classes of estimators, especially for samples with small sizes based on the modified Horvitz–Thompson estimator. Such an accurate estimate of the number of new patients allows the health system to take appropriate countermeasures.

Situations when field researchers are tempted to deviate from preselected sampling plan and to include nearby or related units in sample, then adaptive cluster sampling (ACS) offers a nearly completion solution. For rare and clustered populations, Thompson introduced ACS as an effective sampling method when data is not contaminated with outliers. However, traditional approaches produce distorted results when data includes outliers. Taking the same issue into consideration, the present study focuses on defining adaptive ratio-type regression estimators using OLS, Huber M, Mallows GM, Schweppe GM, SIS GM and Uk’s redescending M-estimation functions within ACS framework. Subsequently, we propose regression type estimators utilizing these functions within ACS framework. In this study, we have also derived mean square error properties of both adapted and proposed estimators in order to evaluate performance of these estimators, by using both real-life data and simulated data sets generated from a Poisson clustered process.

In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes—the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts.

Background Sickle Cell Disorder is Africa’s most prevalent genetic disease. Yet, it remains a neglected condition, with high mortality under-five, and a lack of population-based studies in the region. This is the first of its kind in São Tomé e Príncipe, aiming to estimate the prevalence of sickle cell trait and other haemoglobin variants in women of reproductive age and its associated factors. Methods: We conducted a cluster survey in 35 neighbourhoods. Haemoglobin was assessed through point-of-care capillary electrophoresis or high-performance liquid chromatography, and sociodemographic data through questionnaires. The weighted prevalence of sickle cell trait (HbAS) and HbC carriers was estimated with a 95% confidence interval (95% CI). We calculated weighted prevalence ratios (95% CI) through robust Poisson regression for its association with age and individual and collective genetic heritage. Findings: The prevalence of sickle cell trait in women of reproductive age in São Tomé e Príncipe (n = 376) was 13.45% (95% CI: 9.05-19.00). The prevalence of HbC carriers was 8.00% (95% CI: 4.71-12.00). Older age and speaking Forro or Angolar were positively associated with having sickle cell trait. Interpretation: The prevalence of sickle cell trait in São Tomé e Príncipe ranks high in the West African region. The country should follow international guidelines, implementing newborn screening and comprehensive healthcare management.

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy estimation in conservation monitoring. More generally, this work represents a contribution to the topic of cluster sampling for situations in which there is a need for specific modeling (e.g., reflecting dependence) for the distribution of the variable(s) of interest among subunits.

Biological nitrogen fixation (BNF) is the largest natural source of exogenous nitrogen (N) to unmanaged ecosystems and also the primary baseline against which anthropogenic changes to the N cycle are measured. Rates of BNF in tropical rainforest are thought to be among the highest on Earth, but they are notoriously difficult to quantify and are based on little empirical data. We adapted a sampling strategy from community ecology to generate spatial estimates of symbiotic and free-living BNF in secondary and primary forest sites that span a typical range of tropical forest legume abundance. Although total BNF was higher in secondary than primary forest, overall rates were roughly five times lower than previous estimates for the tropical forest biome. We found strong correlations between symbiotic BNF and legume abundance, but we also show that spatially free-living BNF often exceeds symbiotic inputs. Our results suggest that BNF in tropical forest has been overestimated, and our data are consistent with a recent top-down estimate of global BNF that implied but did not measure low tropical BNF rates. Finally, comparing tropical BNF within the historical area of tropical rainforest with current anthropogenic N inputs indicates that humans have already at least doubled reactive N inputs to the tropical forest biome, a far greater change than previously thought. Because N inputs are increasing faster in the tropics than anywhere on Earth, both the proportion and the effects of human N enrichment are likely to grow in the future.

This study aimed to explore which offensive variables best discriminate the ball possession duration according to players specific role (defenders, midfielders, attackers) during a Gk+3vs3+Gk football small-sided games. Fifteen under-15 players (age 13.2±1.0 years, playing experience 4.2±1.0 years) were grouped according to their positions (team of defenders, n = 5; team of midfielders, n = 7; team of attackers, n = 3). On each testing day (n = 3), each team performed one bout of 5-min against each team in a random order, accounting for a total of nine bouts in the following scenarios: i) defenders vs midfielders; ii) defenders vs attackers; iii) midfielders vs attackers. Based on video, a notational analysis process allowed to capture individual and collective actions. According to each playing position group, discriminant analysis was used to identify relevant variables that discriminate different ball possession sequences (short, medium, and long). The analysis revealed the existence of three clusters according to ball possession duration, classified as short sequence (~4 seconds), medium sequence (~10 seconds) and long sequence (~18 seconds). The number of touches per possession was the variable that discriminates the ball possession duration from all playing positions while passing actions were related to midfielders and attackers. In addition, different ball possessions sequences in the attackers were also discriminated by the number of players involved per possession. Accordingly, to increase the duration of the offensive phase during small-sided games, coaches should foster the players’ ability to stay on the ball, as it may amplify their opportunities to maintain the ball possession. In addition, coaches may also include reward rules to encourage midfielders and attackers’ passing actions and the number of attackers involved during the attack to promote longer ball possessions durations.