Analysing spatiotemporal patterns of Covid-19 confirmed deaths at the NUTS-2 regional level

During the ongoing Covid-19 pandemic, understanding the spatiotemporal patterns of the virus is crucial for policymakers to intervene promptly. The relevance of spatial proximity in the spread of the pandemic necessitates adequate tools, and noisy data must be properly treated. This study proposes obtaining clusters of European regions using smoothed curves of daily deaths from March 2020-March 2022. A functional representation of the curves w<s implemented to extract the features used in a clustering algorithm that allows spatial proximity. In a spatial regression model, the authors also investigated the role of clusters and pre-existing conditions on cumulative deaths, and observed that air pollution, health conditions, and population age structure are significantly associated with Covid-19 confirmed deaths.

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

Visualizing COVID-19 Vaccination Rate and Vaccination Centre in Malaysia using DBSCAN Clustering model

In February 2021, the Malaysian government launched a vaccination campaign against coronavirus disease 2019 (COVID-19). However, there is a problem in identifying suitable location for vaccination centre should be allocated. At the same time, there are population that living in the rural area and has difficulty to travel to the nearest vaccination centres. Therefore, based on the data of vaccination rate collected by Ministry of Health, the proposed project aims to classify and visualise the data based on number of COVID-19 vaccination rate and centre in Malaysia for the adult and adolescent populations. This project uses machine learning technique called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The system is developed in Python language platform for back-end development, and PyCharm is utilised for front-end development in web-based platform. This project follows four phases in Waterfall model: requirement analysis, design, implementation, and testing. The system is evaluated for functionality and usability based on user satisfaction and the accuracy of the model. The results of the testing shows that all the functionality of the system have been implemented successfully in the system. The system also rated good according to SUS Questionnaire in usability testing with score of 88.5%. The model of machine learning also achieved a good accuracy score which is greater than 0.3. In conclusion, the data visualization web-based application helps the Malaysian government to identify location for additional vaccination centres in strategic locations and it helps Malaysian people to locate nearby vaccination centres in their area. © 2022 IEEE.

Post-lockdown spatiotemporal pattern of COVID clustering in North 24 Parganas, West Bengal, India

Many scholars and researchers have studied the CoVID-19 epidemic's spread using GIS technologies since it first appeared. The CoVID-19 pandemic is thought to be rife with unknowns, and many of them have a spatial component that makes the phenomenon understood as being spatially and possibly mappable. The majority of these efforts, though, have been made at the national, state, or district, levels. Very few studies primarily concentrate on the display of the CoVID-19 cluster at a local or neighborhood scale. From the perspective of micro-planning, analyzing the clustering, geographical direction, and heterogeneity of the CoVID-19 hotspots' spatial pattern is crucial specially when mass has returned to new normal living style. Using a case study on the North 24 Parganas of West Bengal, India, the most vulnerable district in West Bengal, we attempt to analyze the CoVID-19 diffusion at the block level in post-lockdown period. We assess the spatiotemporal distribution of CoVID-19 and map its hotspots based on the containment zones. This study demonstrates the patterns of geographical dispersion and the CoVID-19 pandemic spread in North 24 Parganas which is highly concentrated along the western boundaries of the state. We observed that the containment clusters of 2020 once more noted a higher density of CoVID cases in 2022 and validates the findings of the current study. It promises to corroborate the study into the geographic relation and spread of CoVID-19. By examining such spatial distribution patterns, the government might be able to track and predict the transmission of the infection in neighborhoods of blocks. © 2022, The Author(s), under exclusive licence to Korean Spatial Information Society.

Spatio-Temporal Variations of the COVID-19 Epidemic in Mexico

Mexico is one of the countries worst affected by the Coronavirus Disease 2019 (COVID-19). Analyzing the spatiotemporal spread processes of the COVID-19 epidemic in Mexico is of great significance in terms of preventing its further transmission. This study obtained COVID-19 cases and deaths at the municipality level in Mexico from February 28, 2020, to February 27, 2022, and adopted Hoover index, spatial autocorrelation analysis, and epidemic center calculation to reveal the spatio-temporal pattern of the pandemic nationwide. The results showed that the COVID-19 outbreak in Mexico experienced an initial low-level transmission and four concentrated outbreaks. In terms of spatial transmission pattern, COVID-19 cases showed clear spatial clustering characteristics (Moran's I: 0.48), and large cities with more social interactions (such as Mexico City, Guadalajara, etc.) were most affected. In terms of the directional characteristics of the COVID-19 impact, the epidemiological center constantly shifted in the northeast-southwest direction due to the changing severity of the epidemic in the northwestern coast and the central part of Mexico during the initial outbreak phase. Accordingly, the centers of the three subsequent outbreaks moved to the southeast, northwest, and southeast. The COVID-19 epidemic spread very rapidly in Mexico, especially in the second phase. In the four concentrated outbreaks, the time for the distribution of cases to form a relatively stable spatial pattern was 99 days, 15 days, 95 days, and 42 days, respectively. But the difference of transmission rate at the state level is significant. The state with earlier outbreaks, such as Mexico City, spreads faster. This study revealed the characteristics and laws of the spread of infectious diseases at the national scale, and provided a reference for the prevention and control of the COVID-19 epidemic and future emerging infectious diseases. © 2022 IEEE.

Mortality due to noninfectious lower respiratory diseases: A spatiotemporal, cross-sectional study.

Background and Aims: Geography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs). Methods: Data related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD-10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics. Results: The frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age- and sex-adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD. Conclusions: Our findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air-polluted days and to control pollutants, especially in the industrial areas of the north of this province.

Clustering and mapping the first COVID-19 outbreak in France.

BACKGROUND: With more than 160 000 confirmed COVID-19 cases and about 30 000 deceased people at the end of June 2020, France was one of the countries most affected by the coronavirus crisis worldwide. We aim to assess the efficiency of global lockdown policy in limiting spatial contamination through an in-depth reanalysis of spatial statistics in France during the first lockdown and immediate post-lockdown phases. METHODS: To reach that goal, we use an integrated approach at the crossroads of geography, spatial epidemiology, and public health science. To eliminate any ambiguity relevant to the scope of the study, attention focused at first on data quality assessment. The data used originate from official databases (Santé Publique France) and the analysis is performed at a departmental level. We then developed spatial autocorrelation analysis, thematic mapping, hot spot analysis, and multivariate clustering. RESULTS: We observe the extreme heterogeneity of local situations and demonstrate that clustering and intensity are decorrelated indicators. Thematic mapping allows us to identify five "ghost" clusters, whereas hot spot analysis detects two positive and two negative clusters. Our re-evaluation also highlights that spatial dissemination follows a twofold logic, zonal contiguity and linear development, thus determining a "metastatic" propagation pattern. CONCLUSIONS: One of the most problematic issues about COVID-19 management by the authorities is the limited capacity to identify hot spots. Clustering of epidemic events is often biased because of inappropriate data quality assessment and algorithms eliminating statistical-spatial outliers. Enhanced detection techniques allow for a better identification of hot and cold spots, which may lead to more effective political decisions during epidemic outbreaks.

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022.

The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z-score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High-High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low-Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354-2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = -1.591, 95% CI: -2.461 to -0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High-High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects.

Spatial epidemiology of COVID-19 infection through the first outbreak in the city of Mashhad, Iran

The COVID-19 epidemic is currently the most important public health challenge worldwide. The current study aimed to survey the spatial epidemiology of the COVID-19 outbreak in Mashhad, Iran, across the first outbreak. The data was including the hospitalized lab-confirmed COVID-19 cases from Feb 4 until Apr 13, 2020. For comparison between the groups, classical statistics analyses were used. A logistic regression model was built to detect the factors affecting mortality. After calculating the empirical Bayesian rate (EBR), the Local Moran's I statistic was applied to quantify the spatial autocorrelation of disease. The total cumulative incidence and case fatality rates were respectively 4.6 per 10,000 (95% CI: 4.3-4.8) and 23.1% (95% CI: 23.2-25.4). Of 1535 cases, 62% were males and were more likely to die than females (adjusted Odds Ratio (aOR): 1.58, 95% CI: 1.23-2.04). The odds of death for patients over 60 years was more than three times (aOR: 3.66, 95% CI: 2.79-4.81). Although the distribution of COVID-19 patients was nearly random in Mashhad, the downtown area had the most significant high-high clusters throughout most of the biweekly periods. The most likely factors influencing the development of hotspots around the downtown include the congested population (due to the holy shrine), low socioeconomic and deprived neighborhoods, poor access to health facilities, indoor crowding, and further use of public transportation. Constantly raising public awareness, emphasizing social distancing, and increasing the whole community immunization, particularly in the high-priority areas detected by spatial analysis, can lead people to a brighter picture of their lives.

An Approach to Identify Covid Infected People Due to Contact by using Machine Learning

Since the year 2020, the world has faced an unprecedented situation resulting from the spread of a deadly and highly contagious Coronavirus disease. Ever since the inception of the Coronavirus disease 2019 (Covid- 19), scientists all over the world have started preparing methods to fight the disease to save as many human lives as possible. One preliminary solution was hidden in the spreading mechanism of the virus, which was physical contact. So, the solution was to follow social distancing as much as possible. Another solution, which was the second stage solution, was to find the affected patients and isolate them to prevent the disease from spreading. Thus, contact tracing mechanisms were developed to find potential patients in as little time as possible. The Density-based spatial clustering of applications with noise (DBSCAN) algorithm proves to be an efficient solution to implement the proposed solution by making use of the physical locations of patients and geotagging them. This paper focuses on the development of a standalone web application that performs the task of contact tracing by making use of machine learning clustering algorithms. This reduces the time taken by competent authorities and helps them to find probable covid -positive patients in a much lesser period. © 2022 Bharati Vidyapeeth, New Delhi.

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.

Finding Spatial-Textual Clusters in COVID Tweets

Finding Semantic similarity in text is a vital concept in the fields of information mining, text-based profiling. There have been many approaches to improve information retrieval by mining the semantics of the text. With the pandemic situation prevailing all over the world, we come across many useful posts about the COVID infection that is being tweeted by medical practitioners and people in the health care sector. While we come across such tweets, we also have tweets related to the vaccines, medical facilities, change in economic conditions due to pandemic, etc. But there is no methodology to efficiently study the tweet data and retrieve useful information out of them. Also, we need to utilize the geographical information that comes with each tweet. Though there have been many studies conducted on sentiment analysis, statistical analysis related to twitter data, there has not been much research on finding out the geographical distribution of COVID related tweets combined with query-based textual similarity of COVID related tweets. In this paper, we try to study the semantics of geo-Tagged twitter data related to COVID and segregate the tweets based on their geographical location and according to the content of tweets. We use an improved version of Density-Based Spatial Clustering for clustering the tweets according to geo-spatial information. Then, we apply cosine similarity techniques to do the textural clustering and evaluate the performance of proposed model. The proposed model is able to cluster tweets using the spatial coordinates and classify the tweets based on the textual similarity measure. © 2022 IEEE.

Assessing Response Readiness to Health Emergencies: A Spatial Evaluation of Health and Socio-Economic Justice in Pakistan.

COVID19 pandemic has put the global health emergency response to the test. Providing health and socio-economic justice across communities/regions helps in resilient response. In this study, a Geographic Information Systems-based framework is proposed and demonstrated in the context of public health-related hazards and pandemic response, such as in the face of COVID19. Indicators relevant to health system (HS) and socio-economic conditions (SC) are utilized to compute a response readiness index (RRI). The frequency histograms and the Analysis of Variance approaches are applied to analyze the distribution of response readiness. We further integrate spatial distributional models to explore the geographically-varying patterns of response readiness pinpointing the priority intervention areas in the context of cross-regional health and socio-economic justice. The framework's application is demonstrated using Pakistan's most developed and populous province, namely Punjab (districts scale, n = 36), as a case study. The results show that ~ 45% indicators achieve below-average scores (value < 0.61) including four from HS and five from SC. The findings ascertain maximum districts lack health facilities, hospital beds, and health insurance from HS and more than 50% lack communication means and literacy-rates, which are essential in times of emergencies. Our cross-regional assessment shows a north-south spatial heterogeneity with southern Punjab being the most vulnerable to COVID-like situations. Dera Ghazi Khan and Muzaffargarh are identified as the statistically significant hotspots of response incompetency (95% confidence), which is critical. This study has policy implications in the context of decision-making, resource allocation, and strategy formulation on health emergency response (i.e., COVID19) to improve community health resilience.

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

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* statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters i and spatial outliers of COVID-19. The Getis-Ord’s G* statistic was then used to detect hotspots of COVID-19. The method has i 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 North-Eastern 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. © 2021, Russian Geographical Society. All rights reserved.

A Study on Geospatially Assessing the Impact of COVID-19 in Maharashtra, India

The emergence of 2019 novel corona virus disease (COVID-19) raised global health concerns throughout the world. It has become a major challenge for healthcare personnel and researchers throughout the world to efficiently track and prevent the transmission of this virus. In this paper, the role of geographic information system (GIS) based spatial models for tracking the spread of COVID-19 and discovery of testing centres in Maharashtra, India was studied. The datasets collected from diverse sources were geocoded to make it geospatially compatible. A three-tiered framework was proposed to practically realize the impact of COVID-19 in a cartographic fashion. Initially, choropleth maps labeled with testing centres, number of confirmed cases and casualties were visualized in a district-wise manner. Heatmaps for visualizing the spatial density of confirmed cases and casualties were presented. The visualization of spatial K-means clustering for optimal value of “k” estimated using the heuristic-based Elbow method was provided along with zonal analysis of the districts. Map showing spatial autocorrelation was also presented to identify spatial hotspots and coldspots. The districts of Pune and Thane reported respective z-scores of 3.424 and 3.347 along with p-values of 0.006 and 0.001 respectively. It was inferred from the generated results that Pune and Thane districts in Maharashtra were identified as COVID-19 hotspots. Based upon this analysis, certain effective mitigation strategies can be devised in order to check the uncontrolled spread of COVID-19 in the identified hotspot areas.

Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative units in Romania.

Spatial analyses related to Covid-19 have been so far conducted at county, regional or national level, without a thorough assessment at the continuous local level of administrative-territorial units like cities, towns, or communes. To address this gap, we employ daily data on the infection rate provided for Romanian administrative units from March to May 2021. Using the global and local Moran I spatial autocorrelation coefficients, we identify significant clustering processes in the Covid-19 infection rate. Additional analysis based on spatially smoothed rate maps and spatial regressions prove that this clustering pattern is influenced by the development level of localities, proxied by unemployment rate and Local Human Development Index. Results show the features of the 3rd wave in Romania, characterized by a quadratic trend.

Spatiotemporal analysis and seasonality of tuberculosis in Algeria.

Background: This study aimed to describe the spatiotemporal distribution, to build a forecasting model, and to determine the seasonal pattern of tuberculosis (TB) in Algeria. Methods: The Box-Jenkins methodology was used to develop predictive models and GeoDa software was used to perform spatial autocorrelation. Results: Between 1982 and 2019, the notification rate per 100,000 population of smear-positive pulmonary TB (SPPTB) has dropped 62.2%, while that of extrapulmonary TB (EPTB) has risen 91.3%. For the last decade, the mean detection rate of PTB was 82.6%. At around, 2% of PTB cases were yearly reported in children under 15 years old, a peak in notification rate was observed in the elderly aged 65 and over, and the sex ratio was in favor of men. Between 52% and 59% of EPTB cases were lymphadenitis TB and between 15% and 23% were pleural TB. About two-third of EPTB cases were females and around 10% were children under the age of 15. The time series analysis showed that (1,1, 2) × (1, 1, 0)4 (respectively (0, 1, 2) × (1, 1, 0)4, (3, 1, 0) × (1, 1, 0)4) offered the best forecasting model to quarterly TB (respectively EPTB, SPPTB) surveillance data. The most hit part was the Tell followed by high plateaus which accounted for 96.6% of notifications in 2017. Significant hot spots were identified in the central part for EPTB notification rate and in the northwestern part for SPPTB. Conclusions: There is a need to reframe the set objectives in the state strategy to combat TB taking into account seasonality and spatial clustering to ensure improved TB management through targeted and effective interventions.

Socioeconomically Disadvantaged Neighborhoods Face Increased Persistence of SARS-CoV-2 Clusters.

Objective: To investigate the association between socioeconomic deprivation and the persistence of SARS-CoV-2 clusters. Methods: We analyzed 3,355 SARS-CoV-2 positive test results in the state of Geneva (Switzerland) from February 26 to April 30, 2020. We used a spatiotemporal cluster detection algorithm to monitor SARS-CoV-2 transmission dynamics and defined spatial cluster persistence as the time in days from emergence to disappearance. Using spatial cluster persistence measured outcome and a deprivation index based on neighborhood-level census socioeconomic data, stratified survival functions were estimated using the Kaplan-Meier estimator. Population density adjusted Cox proportional hazards (PH) regression models were then used to examine the association between neighborhood socioeconomic deprivation and persistence of SARS-CoV-2 clusters. Results: SARS-CoV-2 clusters persisted significantly longer in socioeconomically disadvantaged neighborhoods. In the Cox PH model, the standardized deprivation index was associated with an increased spatial cluster persistence (hazard ratio [HR], 1.43 [95% CI, 1.28-1.59]). The adjusted tercile-specific deprivation index HR was 1.82 [95% CI, 1.56-2.17]. Conclusions: The increased risk of infection of disadvantaged individuals may also be due to the persistence of community transmission. These findings further highlight the need for interventions mitigating inequalities in the risk of SARS-CoV-2 infection and thus, of serious illness and mortality.

Spatial robust fuzzy clustering of COVID 19 time series based on B-splines.

The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.