Spatial Clustering Patterns Research Articles

The impact of health resource enhancement and its spatiotemporal relationship with population health.

This study investigated the impact of health resource enhancement on health and spatiotemporal variation characteristics from 2000 to 2010 at the county level. Multiscale Geographically Weighted Regression and curve fitting were used to explore the characteristics of spatiotemporal impact and divergence mechanism of health resource enhancement on population health. From 2000 to 2010, China's population health continued to rise steadily, and health resource allocation improved. Population health demonstrated the significant spatial autocorrelation, and its spatial clustering patterns were relatively fixed. Health resource allocation was relatively equal. Health technicians per 1,000 persons had a significant positive effect on population health in 2000 and 2010. Meanwhile, its impact tends to be consistent across regions, and the impact scale has been continuously expanding. A quantitative relationship exists between population health and health resource inputs. When life expectancy ranged from 73.68 to 84.08 years, the death rate ranged from 6.27 to 9.00%, and the infant mortality rate ranged from 0.00 to 6.33%, investments in health resources, especially related to health technicians, were beneficial for population health. The government should improve the science and rationality of health resource planning. Planning meets regional realities by combining the impacts of economy and geography. The influence of health resources on population health depends on the overall allocation of health technicians. The number of health technicians needs to be further increased to improve the health resources' effective allocation between regions.

도시쇠퇴 유형별 재난⋅재해 위험의 공간분포 패턴 분석

This study aims to compare the difference in the spatial pattern of the Risk Diagnosis Index(RDI) and the Comprehensive Diagnosis Index(CDI) by type of urban decline. First, CDI decreases the risk of disasters as it comes close to the urbanized area in all case areas. Various strategies are needed to increase the reduction capacity, focusing on areas with a high risk of disasters. Second, there is a difference in the spatial pattern of declined and non-declined areas in the city, and this trend is different for the urban type. Seosan has relatively little influence in the reduction capacity sector, while Daegu has a high influence in the reduction capacity sector, which affects the spatial pattern of CDI. It implies CDI by urban size and regional attributes should be differentiated. Third, both RDI and CDI tend to be spatially clustered. The spatial clustering pattern of disaster risk in the declined areas is higher than that of other areas. Although the pattern of disaster risk is high in declined areas, this varies depending on urban size and declined type.

Spatial homogeneity pursuit of regression coefficients for hand, foot and mouth disease in Xinjiang Uygur Autonomous Region in 2018

To explore the complex spatial pattern between the incidence of hand, foot, and mouth disease (HFMD) and meteorological factors [average temperature (AT), average relative humidity (ARH), average air pressure (AP), average wind speed (AW)], this paper constructed a Spatial Clustering coefficient (SCC) regression model to detect spatial clustering patterns of each regression coefficients in different seasons. The results revealed that compared with geographically weighted regression (GWR), the coefficients estimated by SCC method were more smooth with clearly identified spatial and improved edge effects. Therefore, interesting spatial patterns were easy to identify in the SCC estimated coefficients. And then, the SCC method had better estimation accuracy in estimating the relationship between potential meteorological factors and HFMD cases. Meteorological factors had different significance in their effect on HFMD incidence depending on the season. Specifically, the influence of AT on HFMD was negatively correlated in summer and winter, especially in the Altay region, Bayingoleng Mongolian Autonomous Prefecture, Turpan region and Hami region. Second, AW had positive effects with HFMD in summer, but the AW played a negative role in the whole Xinjiang in winter. In Tianshan district, Shayibake district, Shuimogou district, etc. in summer, ARH showed a strong negative correlation, but in Alar city it had a high positive correlation, however, in winter ARH showed a high negative correlation in Altay regions, Aksu region and other places had negative effects, and it showed a strong positive correlation in Shayibak district. Finally, AP had a strong positive correlation with HFMD in summer in Shaybak district, but in winter, AP showed a strong negative correlation in Altay district and Buxel Mongolia Autonomous county. In summary, Xinjiang should adapt measures to local conditions, and formulate appropriate HFMD prevention strategies according to the characteristics of different regions, time, and meteorological factors.

GIS-Based Study of Dental Accessibility and Caries in 3-Year-Old Japanese Children

This study aimed to reveal the geographic accessibility of dental clinics for most municipalities in Japan in 2015 and to explore the association between dental accessibility and dental caries status in 3-year-old children. We computed the accessibility index and accessibility index rate for the population outside a 1-km radius of dental clinics using a geographic information system. We also used spatial autocorrelation analysis (Moran's I statistic) to examine the spatial clustering patterns of dental accessibility in Japanese municipalities. In addition, we adjusted the prevalence of dental caries for most municipalities using empirical Bayesian estimation. Finally, we applied multiple linear regression to scrutinise the associations between dental caries status, including the prevalence of dental caries and decayed and filled teeth (dft), and dental accessibility, with adjustments made for other sociodemographic variables. The distribution of dental accessibility in Japanese municipalities is relatively unequal. Dental accessibility is decent in the 3 metropolitan areas around Tokyo, Osaka, and Nagoya but poor in the Tohoku and Kyushu regions. In addition, dental accessibility is significantly related to the prevalence of dental caries and dft after adjusting for other sociodemographic variables (P < .005). This study suggests that dental accessibility is considerably connected to the dental caries status of 3-year-old children after excluding financial burden. Preschool children in areas with poor dental accessibility are likely to have poor dental caries status. We also verified the inequality of dental accessibility amongst Japanese municipalities. For the future development of primary oral health care, more attention should be paid to people with a disadvantage in terms of dental accessibility.

Spatial Effect of Ecological Environmental Factors on Mumps in China during 2014–2018

(1) Background: although mumps vaccines have been introduced in most countries around the world in recent years, mumps outbreaks have occurred in countries with high vaccination rates. At present, China remains the focus of the global fight against mumps. This study aims to observe the epidemic characteristics and spatial clustering patterns of mumps and to investigate the potential factors affecting the disease incidence, which could provide novel ideas and avenues for future research as well as the prevention and control of mumps. (2) Methods: we used ArcGIS software to visualize the spatial distribution and variation of mumps. Spatial autocorrelation analysis was applied to detect the spatial dependence and clustering patterns of the incidence. We applied the Spatial Durbin Panel Model (SDPM) to explore the spatial associations of ecological environmental factors with mumps. (3) Results: overall, the incidence rate showed a significant upward trend from 2014 to 2018, with the highest number of cases in the 10-15-year age group and from May to June. Geographically, the high incidence clusters were concentrated in southern regions, including Hunan, Hubei, Chongqing, Guizhou, Guangdong, and Guangxi. This study also found that mumps has a positive spatial spillover effect in the study area. The average temperature and GDP of the local and adjacent areas have a significant impact on mumps. The increase in PM2.5 contributes to the rise in the incidence of mumps in this region. (4) Conclusions: these results can offer some novel ideas for policymakers and researchers. Local meteorological conditions and economic levels can extend to surrounding areas to affect the occurrence of mumps, so regional cooperation becomes particularly important. We recommend investment of public health funds in areas with a high incidence of mumps and developing economies to reduce and control the incidence of mumps.

Socio-Economic and Demographic Factors Associated with the Spatial Distribution of COVID-19 in Africa.

Corona is a disease that affects the whole world. Countries with weak economies are specifically more vulnerable. A proper understanding of COVID-19 spreading, identifying the high-risk areas, and discovering factors influencing the spread of the disease are crucial to improving disease control. This study evaluates the geo-statistical distribution of COVID-19 to identify critical areas of Africa using spatial clustering pattern analysis. In addition, the spatial correlation between infected cases and variables such as the unemployment rate, gross domestic product (GDP), population, and vaccination rate is calculated using Geographically Weighted Regression (GWR) analysis. The hot-spot map showed a statistically significant cluster of high values in southern and northern Africa. Moreover, the outcome of the GWR analysis revealed the GDP and population had the most significant correlation with the spreading of COVID-19, with Local R2 values of (0.01-0.99) and (0-0.89), respectively.

Spatial Patterns and Determinants of Diabetes Mellitus in Indian AdultPopulation: a Secondary Data Analysis from Nationally Representative Surveys.

Diabetes mellitus (DM) is a major public health challenge around the world. It is crucial to understand the geographic distribution of the disease in order to pinpoint high-priority locations and focus intervention on the target populations. Hence, this study was carried out to determine the spatial pattern and determinants of type-2 DM in an Indian population using National Family Health Survey-4 (NFHS-4) and Longitudinal Aging Survey in India (LASI). We have adopted an ecological approach, wherein geospatial analysis was performed using aggregated district-level data from NFHS-4 (613 districts) and LASI survey datasets (632 districts). Moran's I statistic was determined and Local Indicators of Spatial Association (LISA) maps were created to understand the spatial clustering pattern of DM. Spatial regression models were run to determine the spatial factors associated with DM. Prevalence of self-reported DM among males (15-50years) and females (15-49years) was 2.1% [95% confidence interval (CI) 2.0-2.3%] and 1.7% (95% CI 1.6-1.8%), respectively. Prevalence of self-reported DM among males and females aged 45years and above was 12.5% (95% CI 11.5-13.5%) and 10.9% (95% CI 9.8-12%). Positive spatial autocorrelation with significant Moran's I was found for both males and females in both NFHS-4 and LASI data. High-prevalence clustering (hotspots) was maximum among the districts belonging to southern states such as Kerala, Tamil Nadu, Karnataka, and Andhra Pradesh. Northern and central states like Madhya Pradesh, Chhattisgarh, and Haryana mostly had clustering of cold spots (i.e., lower prevalence clustered in the neighboring regions). DM burden in India is spatially clustered. Southern states had the highest level of spatial clustering. Targeted interventions with intersectoral coordination are necessary across the geographically clustered hotspots of DM.

Spatial clustering in vaccination hesitancy: The role of social influence and social selection.

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection, in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence, in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models of both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.

Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China

The rising concentration of carbon in the atmosphere leads to increasing climate change, and it has become a worldwide consensus to reduce emissions. Considering the degree of economic development and industrial structure of different regions and the vast differences in the spatial distribution of clean energy reserves, it is essential to develop localized emission reduction programs. This study investigates the changes in city GDP after implementing carbon pricing policies. The results show that the carbon pricing policy could effectively reduce inequalities between “rich” and “poor” regions. The Moran index before and after the implementation of the policy decreases from 0.416 to 0.401. We also found spatial clustering patterns of carbon emissions, with the main drivers of carbon emissions differing significantly between developed and developing cities, resource-based and industrial cities, and southern and northern cities in China. The most crucial driver of carbon emissions is still the demand for economic development, which can explain more than 30% of carbon emissions. This study focuses on the impact of carbon market & carbon pricing on poverty alleviation and carbon reduction, makes up for the lack of “spatial justice” in the existing studies and provides a feasible carbon reduction plan for different cities.

Is ecological vulnerability assessment based on the VSD model and AHP-Entropy method useful for loessial forest landscape protection and adaptative management? A case study of Ziwuling Mountain Region, China

• Ecological vulnerability was assessed and mapped via the AHP-entropy method and VSD model. • Spatiotemporal changes existed in the exposure, sensitivity, and adaptive capacity. • Apparent spatial cluster patterns of ecological vulnerability existed. • An integrated method was developed for adaptive management to dryland sustainability. Ecological vulnerability assessment is an effective tool to aid decision-makers in understanding the impact of natural and anthropogenic variables on ecosystems. Yet, we know little about incorporating ecological vulnerability into accurate decision-making for climate change adaptation and human activities governance. This study proposes a comprehensive framework to link the Vulnerability Scoping Diagram (VSD) model, Analytic Hierarchy Process-Entropy method, hot spot and cluster analysis into ecological vulnerability assessment and management in the Ziwuling Mountain Region (ZMR), a typical loessial forest landscape in arid China. There were significant spatial and temporal changes in the exposure, sensitivity, and adaptive capacity index in the ZMR from 1990 to 2017. The exposure and adaptive capacity index increased from 1990 to 2017. The subareas with a high exposure value were distributed in the north and south ZMR, and the subareas with a high adaptive capacity value were distributed in the east ZMR. The ecological vulnerability declined during the study period, with the smaller value subareas allocated in the middle and south ZMR and higher value in the north and southwest. Significant spatial clustering patterns on the hot and cold spots of ecological vulnerability existed in the ZMR. The clustering subareas of hot spots on ecological vulnerability were mainly characterized by dry steppe, less precipitation, and less vegetation coverage. The subareas of cold spots of ecological vulnerability were dominated by forestry and forestry-agricultural landscapes. Some suggestions and countermeasures were put forward for adaptive management and sustainability enhancement. Our results provide integrative methods linking the VSD model, AHP-entropy method, hot spot and cluster analysis to ecological vulnerability reduction and adaptive management for dryland sustainability, especially for the forest social-ecological systems in the Chinese Loess Plateau.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China.

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu’s ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The “H-H” agglomeration is mainly concentrated in Lianyungang and Suqian, while the “L-L” agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, −0.127%, −0.116%, 0.192%, −0.110%, −0.114%, and −0.123% in Jiangsu’s ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

Exploring the microgeography and typology of U.S. high-tech clusters

Despite the principal role of high-tech clusters in local planning practice and research, their location and sectoral typology at the granular level have been rarely studied. This study explores the location of U.S. high-tech clusters at a micro-scale by employing firm-level data sets and spatial statistics and examines their sectoral typology using market concentration indices in 52 large U.S regions. The majority (80 %) of the 627 tech clusters we identify have multiple dominant tech industries or are specialized in professional services. Furthermore, while clusters form the major regional hubs for the high-tech economy, they are home to a very small share (7 %, on average) of regional population. U.S. regions also have widely diverse spatial patterns of high-tech clusters; although some regions have scattered clusters, the New York and Northern California high-tech booming regions have clusters concentrated in central business districts (CBDs). Last, U.S. high-tech clusters and the overall high-tech economy are strongly shaped by the location and performance of professional services, i.e., consulting, legal, computer, engineering, and architectural services.

A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics

BackgroundIn public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection.MethodsWe conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data.ResultsThe results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights.ConclusionsThrough the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.

Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico

The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.

Analysis of China’s Regional Economic Competitiveness, Regionalization, and Spatial Aggregation Characteristics Based on Density Clustering Algorithm

Regional development disparities, especially in developing countries, have traditionally been one of the central issues of empirical research in regional economics. However, this rapid change is accompanied by profound changes in the spatial distribution of economic activities in China, the formation of regional economic “blocks,” the widening of regional disparities, and the geographical concentration of economic growth efficiency are important issues highlighted in this change. Therefore, it is important to explore the spatial clustering characteristics and patterns of regional economic growth to provide a scientific basis for relevant government departments to formulate reasonable regional development strategies and promote the balanced and stable development of economic growth. Clustering analysis is an important research topic in the field of data mining, which is used to discover unknown object classes in large-scale data sets. This paper proposes a density-clustering algorithm based on the regional economic competitiveness of China and analyzes its spatial aggregation characteristics. From the perspective of spatial structure theory, economic development is a dynamic process, and to optimize the spatial pattern of China’s regional economic development and improve the efficiency of economic interaction between regions, it is necessary to fully exploit the diffusion and trickle-down effects of important growth poles in the region to the surrounding areas. The experimental results show that the error rate of KSNN is very small, and the error rate of K-means and PSO has increased to a certain extent. Therefore, it can be obtained that the density-clustering algorithm based on the regional economic competitiveness zoning method in China can find out the correct clustering results without the given clustering individual cases. Thus, it is important to grasp the current situation of regional economic agglomeration and reveal the driving factors of agglomeration formation to promote the coordinated development of regional economy and guide the spatial layout of economic development.

Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China.

Food security is the foundation of development. We comprehensively characterized the spatiotemporal patterns of non-grain production (NGP) areas in China and elucidated the underlying factors driving NGP. Our objectives were to map NGP on cultivated land (NGPCL) in China, and to quantify its spatiotemporal patterns, to investigate the factors underlying NGP spatial differentiation, and to provide a scientific basis for developing NGP management policies and reference points for protecting cultivated land in other countries. We mapped NGPCL in China from 2000 to 2018 using remote sensing and geographic information system data. The spatiotemporal evolution of the NGP rate (NGPR) was also investigated. The dominant factors driving NGP progression and associated interactions were identified using geographic detectors. From 2000 to 2018, the NGPR gradually decreased from 63.02% to 52.82%. NGPR was high in the west and low in the east, and its spatial differentiation and clustering patterns were statistically significant. Precipitation, temperature, altitude, and soil carbon content were the dominant factors affecting the spatial differentiation in NGPR. The interaction between these factors enhanced the spatial differentiation.

Towards healthy school neighbourhoods: A baseline analysis in Greater London.

Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400m (5-10min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO2 and PM2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5ha greenspace, 29.0µg/m3 of NO2, 19.3µg/m3 of PM2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8ha greenspace, 15.6µg/m3 of NO2, 15.1µg/m3 of PM2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.

Variations in summertime compound heat extremes and their connections to urbanization in China during 1980–2020

Summertime heat extremes are exceptionally harmful and destructive to human health and socio-economic systems. Based on 2419 meteorological stations, this study investigates the temporal and spatial characteristics of summertime extreme high temperatures across China and their response to urbanization over the period 1980–2020. The results show that (a) both the maximum temperature (TXx) and minimum temperature (TNn) in the summer have shown an upward trend in most parts of China during the past 40 years; significant upward trends are found in eastern China for independent hot days (IHDs), independent hot nights (IHNs) and compound hot events (CHE). (b) Extreme heat shows a positive spatial clustering pattern across China. The hot spots of the three compound heat events (including IHD, IHN and CHE) are mainly distributed in the north; while the cold spots are roughly distributed in the south. The spatial pattern of hot and cold spots of TXx exhibits an east-west orientation while that of TNn shows a southeast-northwest orientation. (c) The correlation between the heat extremes and urbanization level varies among regions, with most exhibiting linear and exponential relationships. Significant urbanization effects on TXx and TNn are detected in all sub-regions, with contributions ranging from 11% to 41% and 14% to 29%, respectively, while urbanization contributions to the three compound heat events vary significantly among the sub-regions. Our findings present the spatio–temporal patterns of summertime heat extremes, especially the spatial clustering pattern of the compound extremes, and thus enrich the understanding of these variations and their regional response to urbanization, which may have important implications for policy-making among distinct sub-regions.

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia.

IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

Geospatial Clustering of Mobile Phone Use and Tuberculosis Health Outcomes Among African Health Systems.

BackgroundWhile multiple studies have documented the impacts of mobile phone use on TB health outcomes for varied settings, it is not immediately clear what the spatial patterns of TB treatment completion rates among African countries are. This paper used Exploratory Spatial Data Analysis (ESDA) techniques to explore the clustering spatial patterns of TB treatment completion rates in 53 African countries and also their relationships with mobile phone use. Using an ESDA approach to identify countries with low TB treatment completion rates and reduced mobile phone use is the first step toward addressing issues related to poor TB outcomes.MethodsTB notifications and treatment data from 2000 through 2015 that were obtained from the World Bank database were used to illustrate a descriptive epidemiology of TB treatment completion rates among African health systems. Spatial clustering patterns of TB treatment completion rates were assessed using differential local Moran's I techniques, and local spatial analytics was performed using local Moran's I tests. Relationships between TB treatment completion rates and mobile phone use were evaluated using ESDA approach.ResultSpatial autocorrelation patterns generated were consistent with Low-Low and High-Low cluster patterns, and they were significant at different p-values. Algeria and Senegal had significant clusters across the study periods, while Democratic Republic of Congo, Niger, South Africa, and Cameroon had significant clusters in at least two time-periods. ESDA identified statistically significant associations between TB treatment completion rates and mobile phone use. Countries with higher rates of mobile phone use showed higher TB treatment completion rates overall, indicating enhanced program uptake (p < 0.05).ConclusionStudy findings provide systematic evidence to inform policy regarding investments in the use of mHealth to optimize TB health outcomes. African governments should identify turnaround strategies to strengthen mHealth technologies and improve outcomes.