Assessing logistic regression applied to respondent-driven sampling studies : a simulation study with an application to empirical data

The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option

A hybrid bi-objective optimization approach for joint determination of safety stock and safety time buffers in multi-item single-stage industrial supply chains

In material requirements planning (MRP) systems, safety stock and safety time are two well-known inventory buffering strategies to protect against supply and demand uncertainties. While the role of safety stocks in coping with uncertainty is well studied, safety time has received only scarce attention in the supply chain management literature. Particularly, most previous operations research models have typically considered the use of such inventory buffers in a separate fashion, but not together. Here, we propose a decision support system (DSS) to address the problem of integrating optimal safety stock and safety time decisions at the component level, in multi-supplier multi-item single-stage industrial supply chains under dynamic demands and stochastic lead times. The DSS is based on a hybrid bi-objective optimization approach that simultaneously optimizes upstream inventory holding costs and β-service levels, suggesting multiple non-dominated Pareto-optimal solutions to decision-makers. We further explore a weighted closed-form analytical expression to select a single Pareto-optimal point from a set of non-dominated solutions, thereby enhancing the practical application of the proposed DSS. We describe the implementation of our approach in a major automotive electronics company operating under a myriad of components with dynamic demand, uncertain supply and requirements plans with different degrees of sparsity. We show the potential of our approach to improve β-service levels while minimizing inventory-related costs. The results suggest that, in certain cases, it appears to be more cost-effective to combine safety stock with safety time compared to considering each inventory buffer independently.This work has been supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Pro-gram (COMPETE 2020) [Project No. 39479, Funding reference: POCI-01–0247-FEDER-39479]

Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Amazonia forest plays a major role in providing ecosystem services for human and sanctuaries for wildlife. However, ongoing deforestation and habitat fragmentation in the Brazilian Amazon has threatened both. The ocelot is an ecologically important mesopredator and a potential conservation ambassador species, yet there are no previous studies on its habitat preference and spatial patterns in this biome. From 2010 to 2017, twelve sites were surveyed, totaling 899 camera trap stations, the largest known dataset for this species. Using occupancy modeling incorporating spatial autocorrelation, we assessed habitat use for ocelot populations across the Brazilian Amazon. Our results revealed a positive sigmoidal correlation between remote-sensing derived metrics of forest cover, disjunct core area density, elevation, distance to roads, distance to settlements and habitat use, and that habitat use by ocelots was negatively associated with slope and distance to river/lake. These findings shed light on the regional scale habitat use of ocelots and indicate important species–habitat relationships, thus providing valuable information for conservation management and land-use planning

Spatial and temporal dynamics of pathogenic Leptospira in surface waters from the urban slum environment

Leptospirosis has emerged as an important urban health problem as slum settlements have expanded worldwide. Yet the dynamics of the environmentally transmitted Leptospira pathogen has not been well characterized in these settings. We used a stratified dense sampling scheme to study the dynamics of Leptospira abundance in surface waters from a Brazilian urban slum community. We collected surface water samples during the dry, intermediate and rainy seasons within a seven-month period and quantified pathogenic Leptospira by quantitative PCR (qPCR). We used logistic and linear mixed models to identify factors that explained variation for the presence and concentration of Leptospira DNA. Among 335 sewage and 250 standing water samples, Leptospira DNA were detected in 36% and 34%, respectively. Among the 236 samples with positive results geometric mean Leptospira concentrations were 152 GEq/mL. The probability of finding Leptospira DNA was higher in sewage samples collected during the rainy season when increased leptospirosis incidence occurred, than during the dry season (47.2% vs 12.5%, respectively, p = 0.0002). There was a marked spatial and temporal heterogeneity in Leptospira DNA distribution, for which type of water, elevation, and time of day that samples were collected, in addition to season, were significant predictors. Together, these findings indicate that Leptospira are ubiquitous in the slum environment and that the water-related risk to which inhabitants are exposed is low. Seasonal increases in Leptospira presence may explain the timing of leptospirosis outbreaks. Effective prevention will need to consider the spatial and temporal dynamics of pathogenic Leptospira in surface waters to reduce the burden of the disease