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

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Kwan Hong ◽  
Hari Hwang ◽  
Byung Chul Chun
Keyword(s):  
Geographical Distribution ◽  
Cumulative Incidence ◽  
Autoregressive Model ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Hospital Resources ◽  
Simultaneous Autoregressive Model ◽  
Epidemiological Characteristics ◽  
Simultaneous Autoregressive

Abstract 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.

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

2018 ◽  
Vol 7 (12) ◽  
pp. 476 ◽  
Author(s):  
Qing Luo ◽  
Daniel A. Griffith ◽  
Huayi Wu
Keyword(s):  
Spatial Autocorrelation ◽  
Autoregressive Model ◽  
Null Hypothesis ◽  
Asymptotic Variance ◽  
Remotely Sensed ◽  
The Other ◽  
Square Lattice ◽  
Large Sample Size ◽  
Simultaneous Autoregressive Model ◽  
Simultaneous Autoregressive

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.

scholarly journals PEAK-LOAD PRICING AND AIRLINE REACTIONS AT EUROPEAN AIRPORTS

2012 ◽  
Vol 3 (1) ◽  
pp. 1-23
Author(s):  
Christos Evangelinos ◽  
Jacqueline Stangl ◽  
Andy Obermeyer
Keyword(s):  
Autoregressive Model ◽  
Peak Load ◽  
Conventional Wisdom ◽  
Airline Pricing ◽  
The Past ◽  
Price Increases ◽  
Additional Costs ◽  
Simultaneous Autoregressive Model ◽  
Simultaneous Autoregressive

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.

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