1001Spatio-temporal epidemiology of mumps and related region factors in Korea

Background Mumps is in Korea's national immunization program, though there are still epidemics, especially in young age. The study's objectives are to establish the epidemiological characteristics of mumps and suggest the predicting factors. Methods We extracted cases from national health insurance data, between 2013 and 2017. Age-specific incidence rate and geographical distribution were evaluated. We tested for spatial autocorrelation by Moran’s I statistics with Delaunary triangular links. Simultaneous autoregressive model for cumulative incidence of mumps using triangular links was used to predict cumulative incidence with region specific factors. Results A total of 219,149 (85.12 per 100,000) were diagnosed and 23,805 (9.25 per 100,000) were hospitalized. Weekly cumulative incidence showed two epidemics every year, between weeks 20-25 and 40-45. Cumulative incidence of ages 10-19 was the highest, 332.21 per 100,000 people, followed by 300.75 per 100,000 people in ages 0-9. Geographical distribution showed clusters of epidemics, and Moran’s I statistics was 0.304 with a p-value <0.01. The Simultaneous autoregressive model estimated the mean age and hospital resources of each region as prediction factors for geographical distribution of mumps. Conclusions Mumps is common in children and peaks in summer and winter. Additionally, there are geographical clusters in epidemics, and the effect of region factors such as mean age and hospital resources are suspected. Key messages Two peaks in age and season appear in mumps in Korea. Clusters of geographical distribution indicate that region factors may affect the incidence.

Using Spatial Regression Models in Identifying the Drivers of Forest Structure in the Hyrcanian Forests of Iran

Background: Understanding the relationships between forest structure, in particular attainable height, and the environment is important for sustainable forest management. Similarly, modeling structural attributes improve our understanding of forest growth dynamics and may identify key drivers of long-term changes in the forest ecosystem. Due to the inherent complexity of these relationships, quantification of some drivers of forest growth is often not available, resulting in spatially auto-correlated errors of the regression model. Methods: To explore the tree height-environment relationships of oriental beech we compared the performance of a standard regression model (multiple linear regression, MLR) to those accommodating a spatial correlation structure, specifically a Generalized Least Squares model with exponential correlation structure (GLS) and three variations of the Simultaneous Autoregressive Model (SAR): the spatial lag model (SLM), the spatial Durbin model (SDM) and the spatial error model (SEM). Across 127 0.1 ha circular sample plots in the primeval World Heritage Hyrcanian Forests of Iran, we collected data on tree height and edaphic and topographic. Within each plot, the height of all trees with DBH ≥ 7 cm was measured. Results: The results showed that SAR and GLS models reduced spatial autocorrelation of model residuals and improved model fit, with both SDM and SEM slightly superior to the SLM in removing spatial autocorrelation in the model residuals. SDM performs better than SEM in terms of RMSE and adjusted R2. Conclusions: Although SAR-based models performed marginally better than GLS, we still recommend GLS for spatial analyses due to their easier implementation and ease-of-use compared to SAR models. However, when the computation time is a concern, SAR-based models can be more useful because of faster execution. Keywords: spatial autocorrelation; Hyrcanian forests; multiple linear regression model; simultaneous autoregressive model; generalized least squares

Peer Effects in Education

In the social sciences, the term “peer effects” has been widely used to describe the various ways in which individual behaviors and attitudes can be influenced by friends, acquaintances, and the wider social environment. Due to the crucial role of social interactions within the school context, the role of peers in shaping academic outcomes has been under scrutiny for decades. Following seminal work by Manski, we distinguish between three different components of peer influence: endogenous (where the behavior of an individual varies in accordance with the behavior of the peer group), exogenous (where the behavior of an individual varies with the characteristics of the members of the peer group), and correlated (where the behavior of individuals is shaped by shared environmental or institutional factors). By estimating a simultaneous autoregressive model, we assess the relative strength of these three forms of peer influence in relation to secondary school exam results in a large sample of Italian school-leavers. One limitation is that we are only able to observe peer influence within the classroom, while another is that the study is confined to a specific moment in time, which comes quite late in young people’s educational trajectories. The results confirm that peer processes play an important role in the reproduction of social inequalities, against the backdrop of institutional criteria for the selection of students into schools and classes. These factors therefore demand the sustained attention of educational administrators and policymakers.

On the Statistical Distribution of the Nonzero Spatial Autocorrelation Parameter in a Simultaneous Autoregressive Model

This paper focuses on the spatial autocorrelation parameter ρ of the simultaneous autoregressive model, and furnishes its sampling distribution for nonzero values, for two regular square (rook and queen) tessellations as well as a hexagonal case with rook connectivity, using Monte Carlo simulation experiments with a large sample size. The regular square lattice directly relates to increasingly used, remotely sensed images, whereas the regular hexagonal configuration is frequently used in sampling and aggregation situations. Results suggest an asymptotic normal distribution for estimated ρ. More specifically, this paper posits functions between ρ and its variance for three adjacency structures, which makes hypothesis testing implementable and furnishes an easily-computed version of the asymptotic variance for ρ at zero for each configuration. In addition, it also presents three examples, where the first employed a simulated dataset for a zero spatial autocorrelation case, and the other two used two empirical datasets—of these, one is a census block dataset for Wuhan (with a Moran coefficient of 0.53, allowing a null hypothesis of, e.g., ρ=0.7) to illustrate a moderate spatial autocorrelation case, and the other is a remotely sensed image of the Yellow Mountain region, China (with a Moran coefficient of 0.91, allowing a null hypothesis of, e.g., ρ=0.95) to illustrate a high spatial autocorrelation case.

PEAK-LOAD PRICING AND AIRLINE REACTIONS AT EUROPEAN AIRPORTS

Conventional wisdom in the economics of pricing holds that peak-load pricing can enhance welfare in cases where demand peaks are clearly identifiable and highly predictable. However, this pricing tool has not found acceptance among airlines in the past. In the very few cases in which peak-load pricing has been introduced, regulators have faced strong opposition from airlines. Recent research has focused on whether airlines could pass the additional costs associated with peak-load pricing on to passengers. Expanding on this work, this paper assesses how peak-load pricing would impact airline costs and forecasts howairlines would react to the implementation of a peak-load pricing regime. We use a simultaneous autoregressive model to predict airline pricing reactions. Our findings indicate that for certain routes, airlines would subsidize revenue decreases in off-peak times with price increases during peak times. This finding corroborates the perception held by airlines that a peak-load pricing regime would encourage new competitors to enter the market at off-peak times.