Spatio-temporal analysis of the COVID-19 pandemic in Türkiye: results of the controlled normalization

This study investigates the spatio-temporal structure of the pandemic in Türkiye during the normalization process. An analysis has been conducted based on spatial and space–time scan statistics of the province-based numbers of confirmed COVID-19 cases during the normalization process from February 27 to May 7, 2021. The clusters affected by regional application differences has determined. The increase in cases has been observed, and the risk classes that supported the spatial relationship have been determined. Positive spatial relationships have been observed. Moran I measurements have also directly overlapped with the developments in the timeline of the COVID-19 pandemic in Türkiye. Local Moran I analysis has shown the transition of clusters from different regions to others over time. According to the results, controlled normalization has not happened as expected and the increase in the number of cases eventually led to the start of a total lockdown. Spatial and spatio-temporal analysis may show how to respond to a potential new pandemic. Regulations that vary from region to region can be meaningless depending on the spatial interaction. Decision makers may benefit in the future from these analyses, which reveal the results of experience to control current worsening scenarios. © 2022, The Author(s), under exclusive licence to Korean Spatial Information Society.

Space-time cluster detection techniques for infectious diseases: A systematic review.

BACKGROUND: Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS: We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS: Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION: This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks

The majority of the widely used scan statistics are based on distributional assumptions. Contrary to the existing methods, with a new perspective in clustering, the Mann-Whitney Scan Statistic was introduced to detect clusters in continuous data indexed by time or space, without any distributional assumptions or parameters to set up. We propose an extension of the Mann-Whitney Scan Statistic that can be applied to spatiotemporal data based on spatiotemporal distance measure. This novel scan statistic is distribution-free and seems to be powerful against parametric spatiotemporal scan statistics. The results are applicable in a wide variety of spatiotemporal domains, including epidemiology, socioeconomic analysis and climate sciences, irrespective of continuous or discrete data.

SPATIAL ANALYSIS OF CORONA VIRUS (COVID-19) DISEASE

Coronavirus disease (COVID-19) has been quickly spreading all over the world. As of 27th September 2020, a total of 382835 confirmed cases and 19755 deaths have been reported in Uttar Pradesh. The first case in India was registered on 30th January 2020. The data of Coronavirus cases in India and state-wise is available on the Ministry of Health and Family Welfare, Govt. of India. This paper aims to identify the Hot-spots (high rate cluster) of Coronavirus disease in Uttar Pradesh through the Scan Statistics methodology of clustering using the datasets till 27th September, 2020. The clusters (group of states) are reported through scan statistic using SaTscan software. We have identified the statistically significant clusters. The scanning of corona cases is done using simulation to detect the hotspot. The Poisson distribution is assumed for the corona cases. The expected and observed number of cases are compared through the likelihood ratio test. The highest value of the likelihood ratio among all is the hot-spot (most likely cluster). The results could be pretty helpful to the Government for taking strict actions for control, spread and effective management of medical resources in the country on a priority basis since the resources are very limited. © 2022 DAV College. All rights reserved.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh.

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

Applying Spatio-temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?

With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or "alive" clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.