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.

PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.

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

AVALIAÇÃO DE DIFERENTES TÉCNICAS ESPACIAIS PARA ANÁLISE DA OCORRÊNCIA DE DENGUE EM RIO CLARO-SP

In this paper we have developed a study of dengue fever in Rio Claro, São Paulo - Brazil. The Municipal Health Foundation of Rio Claro provided data about reported cases of dengue fever in 2011. The main objective was to analyze both the spatial distribution of the disease in the city, by Census tracts, and the relationship of the disease with socioeconomic factors. Two types of spatial models were applied to the data: the SAR model ``- Simultaneous Autoregressive Models and SEM - Simultaneous Error Models. We also fitted the classic linear model, just for comparison to the two spatial models. The results showed that dengue is related to socioeconomic factors and, through the models it was possible to identify which one was statistically significant. Thematic maps have identified the areas that have the highest concentration of the disease.

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

Global efforts to end the tuberculosis (TB) epidemic by 2030 (SDG3.3) through improved TB case detection and treatment have not been effective to significantly reduce the global burden of the TB epidemic. This study presents an analytical framework to evaluate the use of TB case notification rates (CNR) to monitor and to evaluate TB under-detection and under-diagnoses in Bangladesh. Local indicators of spatial autocorrelation (LISA) were calculated to assess the presence and scale of spatial clusters of TB CNR across 489 upazilas in Bangladesh. Simultaneous autoregressive models were fit to the data to identify associations between TB CNR and poverty, TB testing rates and retreatment rates. CNRs were found to be significantly spatially clustered, negatively correlated to poverty rates and positively associated to TB testing and retreatment rates. Comparing the observed pattern of CNR with model-standardized rates made it possible to identify areas where TB under-detection is likely to occur. These results suggest that TB CNR is an unreliable proxy for TB incidence. Spatial variations in TB case notifications and subnational variations in TB case detection should be considered when monitoring national TB trends. These results provide useful information to target and prioritize context specific interventions.