Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

SPATIAL ANALYSIS OF PROSTATE CANCER INCIDENCE IN IRAQ DURING 2000-2015

Prostate cancer incidence rates have evidenced a substantial increase in Iraq over the past sixteen years. Geographic variation of prostate cancer in Iraq has not been explored. We examine the geographic incidence patterns of prostate cancer in Iraq using the global index of spatial autocorrelation, Getis-Ord Gi* and Anselin Local Moran’s to detect hotspots, coldspots, and spatial outliers of prostate cancer rates. We calculated the age-adjusted incidence rates (AAIRs) according to district level for three periods (2000-2004, 2005-2009, and 2010-2015). Disease maps were produced to explore whether prostate cancer incidence clusters by district, and where hotspots and coldspots occur. Results highlight several districts of Iraq where the burden of prostate cancer incidence is especially high. In 2005-2009, the spatial autocorrelation analysis revealed a prostate cancer incidence hotspot in Al-Rissafa, Al-Manathera, Al-Kufa, Al-Hilla, Al-Hindiya, and Kerbela district. In 2010-2015, hotspots were seen in Al-Mussyab, Al-Hilla, Al-Hindiya, Al-Rissafa, Al-Adhamiya, Al-Sadir, and Daquq district. Examining spatial pattern of prostate cancer AAIRs is critical to government efforts to focus on those regions, and to understanding and targeting prostate cancer

SPATIAL ANALYSIS OF BRAIN AND OTHER CNS CANCERS INCIDENCE IN IRAQ DURING 2000-2015

Brain and other CNS cancers have evidenced increase in Iraq over the study period (2000-2015). Spatial variation of brain and CNS cancers in Iraq at the district level has not been explored. This study aimed to explore the spatial patterns of the Age-Standardized Incidence Rates (ASIRs) of brain and CNS cancers throughout Iraq (except Kurdish region) during 2000-2015 using spatial autocorrelation analyses. Data were obtained from the Iraqi Cancer Registry. The ASIRs were calculated according to geographical region (provinces and districts) for each period (2000-2004, 2005- 2009, and 2010-2015). spatial statistical tools were employed to evaluate hotspots, cold spots, spatial clustering and outliers for each period. Results showed a spatial correlation with hotspots, cold spots, and detecting spatial outliers. This study identified 7 districts as high-risk areas for brain and CNS cancers during 2010-2015, including Al-Sadir, Al-Kadhimiyah, Adhamia, Al-Karkh, Al-Rissafa, and Al-Madain districts in Baghdad province) and southern region (Abu-Al-Khaseeb district in Al-Basrah provinces, and we have evidenced an increase of brain and CNS cancers incidence rates during 2010-2015. The government efforts should focus on those regions, and the factors related to the spatial pattern of the brain and CNS cancers incidence in Iraq should be investigated.

ROBUST SPATIAL AUTOREGRESSIVE UNTUK PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TIMUR

Spatial regression analysis is regression method used for type of data has a spatial effect. Spatial regression showing the presence of spatial effects on the response variable (Y) is a Spatial Autoregressive (SAR). Outlier often found in research spatial data. The outlier is called the spatial outliers. The analysis can be used to handle outliers in general is Robust Regression. There are several estimator that can be used in which the estimator Robust Regression S, M, MM and LTS. Meanwhile, Robust Regression were used to handle spatial outlier is a combination of SAR and Regression Robust method to form a new method that is Robust Spatial Autoregressive (Robust SAR). Type estimator used in this study is the S-Estimator. This study was conducted to determine the best model on a case study Life Expectancy of East Java Province. The best model is analyzed by comparing the methods of SAR and SAR Robust method. Based on the analysis results obtained MSE and Adjusted R2 values for the SAR method are 1.7521 and 55.54% while for the Robust SAR method are 0.7456 and 62.30%. The Robust SAR model has a lower MSE value and a higher Adjusted R2 when compared to the SAR model. Thus the best model for modeling the life expectancy in East Java is Robust SAR models.Keywords:Spatial Autoregressive (SAR), Robust SAR, Life expectancy

Anomaly Region Detection Based on DMST

Anomaly region detection aims at finding spatial outliers or spatial anomalous clusters. Generally, detection approaches cover spatial neighbor's discovery with spatial attributes and anomaly measurement of spatial regions according to non-spatial attributes. In this article, an anomaly region detection method using Delaunay minimal spanning tree (DMST for short) is proposed. First, a Delaunay minimal spanning tree is constructed. Then, the current longest edge of the tree is iteratively cut and anomaly regions are concurrently detected. Finally, the shortest edge of the related bipartite graph is taken as the anomaly measurement. The proposed method could avoid the disturbance of bad reference neighbors and generate anomaly regions keeping atomicity.