scholarly journals Covid-19 Space-time Cluster Detection Using Retrospective Analysis

2022 ◽  
Author(s):  
KALEAB TESFAYE TEGEGNE ◽  
ELENI TESFAYE TEGEGNE ◽  
MEKIBIB KASSA TESSEMA ◽  
GELETA ABERA ◽  
BERHANU BIFATO ◽  
...  
Keyword(s):  
Addis Ababa ◽  
Probability Model ◽  
Health Data ◽  
Space Time ◽  
Scan Statistics ◽  
County Level ◽  
Temporal Cluster ◽  
Preventative Measures ◽  
Spatio Temporal ◽  
Time Scan

Abstract Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

scholarly journals Prospective Time Periodic Geographical Covid-19 Surveillance in Ethiopia Using a Space-time Scan Statistics: Detecting and Evaluating Emerging Clusters

2020 ◽  
Author(s):  
Nuredin Nassir Azmach ◽  
Tesfay Gebremariam Tesfahannes ◽  
Samiya Abrar Abdulsemed ◽  
Temam Abrar Hamza
Keyword(s):  
Addis Ababa ◽  
Space Time ◽  
City Government ◽  
Capital City ◽  
Scan Statistics ◽  
Individual Level ◽  
Level Information ◽  
Public Health Officials ◽  
The Individual ◽  
Time Scan

Abstract Background: On December 31, 2019, multiple pneumonia cases, subsequently identified as coronavirus disease 2019 (COVID-19), was reported for the first time in Wuhan, the capital city of Hubei province in China. At that time, the Wuhan Municipal Health Commission had report 27 cases, of which seven are severely ill, and the remaining cases are stable and controllable. Since, then, the spread of COVID-19 has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. As of September 07, 2020, there had been more than 27 million confirmed cases and 889,000 total deaths, with an average mortality of about 3.3%, globally. In Ethiopia, 58,672 confirmed cases and 918 deaths and this number are likely to increase exponentially. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the pandemics continue to grow.Methods: We have collected the individual-level information on patients with laboratory-confirmed COVID-19 on daily bases from the official reports of the Ethiopian Federal Ministry of Health (FMOH), regional, and city government of Addis Ababa and Dire Dawa health bureaus. Using the daily case data, we conducted a prospective space-time analysis with SaTScan version 9.6. We detect statistically significant space-time clusters of COVID-19 at the woreda and sub-city level in Ethiopia between March 13th-June 6th, 2020, and March 13th-June 24th, 2020.Results: The prospective space-time scan statistic detected “alive” and emerging clusters that are present at the end of our study periods; notably, nine more clusters were detected when adding the updated case data.Conclusions: These results can notify public health officials and decision-makers about where to improve the allocation of resources, testing areas; also, where to implement necessary isolation measures and travel bans. As more confirmed cases become available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. In Ethiopia, our research is the first geographic study that utilizes space-time statistics to monitor COVID-19.

scholarly journals Assessment of the spatio-temporal distribution of Hepatitis-a virus transmission in the state of Pará, Brazil

2020 ◽  
Author(s):  
Philipe Riskalla Leal ◽  
Milton Kampel ◽  
Ricardo José de Paula Souza e Guimarães
Keyword(s):  
Hepatitis A ◽  
Complex Disease ◽  
Hepatitis A Virus ◽  
Disease Incidence ◽  
Space Time ◽  
The State ◽  
Scan Statistics ◽  
Vaccination Program ◽  
Spatio Temporal ◽  
Time Scan

Abstract The hepatitis-A virus (HAV) is a worldwide healthcare problem, mainly affecting countries with poor sanitation and socioeconomic conditions. Spatio-temporal analyses have become an important scientific asset for identifying the clustering of disease infection, providing support for planning interventions and control strategies. This study aims to determine the spatio-temporal variability of HAV infection and related population-based demographic factors in a endemic region. The selected area of study was Pará state, Brazil. Brazilian Ministry of Health Notifiable Diseases Information System (SINAN) epidemiological report, MS vaccination coverage and Brazilian National Sanitation System (SNIS) sanitation condition datum have been analyzed. Spatial (Moran and Local Moran index) and space-time scan statistics techniques have been applied over Pará state using SINAN database for the assessment of the hepatitis-A incidence for a period of 10 years (from 2008 up to 2017). A total of 5500 cases has been reported. Gender specific incidence analysis indicated that men have higher risk of contamination than women. Sociodemographic (lack of sanitation), socioeconomic (municipality governments investments in infra-structure) presented relationship with the disease incidence. There have been evidences that extreme events of severe precipitation and severe droughs were also related to increase in hepatitis-A notification cases. Spatial statistics denoted a heterogeneous geographical structure in the disease`s incidence: isolated high and low HAV incidence clusters through the years, implying in a complex disease outbreak system that is partially controlled by public vaccination actions. Space-time scan statistics denoted that hepatitis-A incidence is highly attached to the public HAV vaccination program and to municipality specific social infrastructure. Lower incidence risk were majorly aggregated over the Nordeste Paraense and Metropolitana de Belém meso-regions. Distinct clusters of hepatitis-A incidence have been found over the studied area (Pará state), and these clusters varied over the years centered at northwest and northeast meso-regions, mainly time-located prior to the national vaccination program start (prior to 2014). National public vaccination program has not been capable of erradicating the disease in the state. Further studies are required to better assess the relationship between climate change efffects over weather events and their relation to HAV transmission outbreaks.

scholarly journals Women’s cancers in China: a spatio-temporal epidemiology analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rongxin He ◽  
Bin Zhu ◽  
Jinlin Liu ◽  
Ning Zhang ◽  
Wei-Hong Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Cervical Cancer ◽  
Space Time ◽  
Scan Statistics ◽  
Incidence And Mortality ◽  
Cluster Feature ◽  
Spatio Temporal ◽  
Time Scan ◽  
High Cluster

Abstract Background Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns. Methods Data on the incidence and mortality of women’s cancers between January 2010 and December 2015 were obtained from the National Cancer Registry Annual Report. Linear tests and bar charts were used to visualize and compare the epidemiologic trends. Two complementary spatial statistics (Moran’s I statistics and Kulldorff’s space–time scan statistics) were adopted to identify the spatial–temporal clusters. Results The results showed that the incidence and mortality of breast cancer displayed slow upward trends, while that of cervical cancer increase dramatically, and the mortality of ovarian cancer also showed a fast increasing trend. Significant differences were detected in incidence and mortality of breast, cervical and ovarian cancer across east, central and west China. The average incidence of breast cancer displayed a high-high cluster feature in part of north and east China, and the opposite traits occurred in southwest China. In the meantime, the average incidence and mortality of cervical cancer in central China revealed a high-high cluster feature, and that of ovarian cancer in northern China displayed a high-high cluster feature. Besides, the anomalous clusters were also detected based on the space–time scan statistics. Conclusion Regional differences were detected in the distribution of women’s cancers in China. An effective response requires a package of coordinated actions that vary across localities regarding the spatio-temporal epidemics and local conditions.

Spatio-temporal cluster analyses of landslides in Italy at national and regional scale

2020 ◽  
Author(s):  
Marj Tonini ◽  
Kim Romailler ◽  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Cluster Analysis ◽  
Point Processes ◽  
Statistical Significance ◽  
Regional Scale ◽  
Regional Analysis ◽  
Scan Statistics ◽  
Economic Losses ◽  
Campania Region ◽  
Temporal Cluster ◽  
Spatio Temporal

<p><strong>Keywords:</strong> Landslides, FraneItalia, cluster analysis, spatio-temporal point process</p><p>In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.</p><p>In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan<sup>TM</sup> software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.</p><p>Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.</p><p>In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.</p><p>REFERENCES</p><p>Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. <em>Geoenvironmental Disasters</em>. 5: 13 (2018)</p><p>Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. <em>Landslides</em> 16 (2019)</p>

Spatial and space-time scan statistics to detect low rate clusters of sex ratio

2005 ◽  
Vol 12 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Jean-Francois Viel ◽  
Nathalie Floret ◽  
Frederic Mauny
Keyword(s):  
Sex Ratio ◽  
Space Time ◽  
Scan Statistics ◽  
Time Scan ◽  
Low Rate

scholarly journals Trends and Spatial Patterns of Oral Cancer Mortality in Ecuador, 2001–2016

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Solange Núñez-González ◽  
J. Andrés Delgado-Ron ◽  
Christopher Gault ◽  
Daniel Simancas-Racines
Keyword(s):  
Ecological Study ◽  
Temporal Trend ◽  
Model Space ◽  
Space Time ◽  
High Variability ◽  
Scan Statistics ◽  
Joinpoint Regression ◽  
Crude Mortality ◽  
Standardized Mortality Rate ◽  
Time Scan

The aims of this study were to describe the temporal trend of OC from 2001 to 2016 and to analyze the space and space-time clusters of high mortality due to OC in Ecuador from 2011 to 2016. Methods. The present study is a mixed ecological study; the time trends were obtained using a Joinpoint regression model, space-time scan statistics was used to identify high-risk clusters, and Global Moran I index was calculated. Results. In Ecuador, between 2001 and 2016, OC caused a total of 1,025 deaths. Crude mortality rates significantly increased, with an APC (annual percentage change) of 2.7% (p=0.009). The age-standardized mortality rate did not significantly increase (APC: 1.73%; p=0.08). The most likely cluster was detected in 2015, included 20 cantons. The second cluster included 38 cantons, in the years 2014 to 2016. The Global Moran I index for the study period showed a negative spatial autocorrelation (−0.067; p=0.37). Conclusion. Mortality due to OC in Ecuador significantly increased over the 16-year study period, the young groups being the most affected. Ecuadorian provinces present high variability in types of OC and cancer rates.

scholarly journals Space-time clusters of dengue, chikungunya, and Zika cases in the city of Rio de Janeiro

2019 ◽  
Author(s):  
Laís Picinini Freitas ◽  
Oswaldo Gonçalves Cruz ◽  
Rachel Lowe ◽  
Marilia Sá Carvalho
Keyword(s):  
Vector Control ◽  
Rio De Janeiro ◽  
Space Time ◽  
Scan Statistics ◽  
Scan Statistic ◽  
Timely Manner ◽  
The North ◽  
Spatio Temporal ◽  
The City ◽  
Janeiro City

AbstractBrazil is a dengue-endemic country where all four dengue virus serotypes circulate and cause seasonal epidemics. Recently, chikungunya and Zika viruses were also introduced. In Rio de Janeiro city, the three diseases co-circulated for the first time in 2015-2016, resulting in what is known as the ‘triple epidemic’. In this study, we identify space-time clusters of dengue, chikungunya, and Zika, to understand the dynamics and interaction between these simultaneously circulating arboviruses in a densely populated and heterogeneous city.We conducted a spatio-temporal analysis of weekly notified cases of the three diseases in Rio de Janeiro city (July 2015 – January 2017), georeferenced by 160 neighbourhoods, using Kulldorff’s scan statistic with discrete Poisson probability models.There were 26549, 13662, and 35905 notified cases of dengue, chikungunya, and Zika, respectively. The 17 dengue clusters and 15 Zika clusters were spread all over the city, while the 14 chikungunya clusters were more concentrated in the North and Downtown areas. Zika clusters persisted over a longer period of time. The multivariate scan statistic – used to analyse the three diseases simultaneously – detected 17 clusters, nine of which included all three diseases.This is the first study exploring space-time clustering of dengue, chikungunya, and Zika in an intraurban area. In general, the clusters did not coincide in time and space. This is probably the result of the competition between viruses for host resources, and of vector-control attitudes promoted by previous arbovirus outbreaks. The main affected area – the North region – is characterised by a combination of high population density and low human development index, highlighting the importance of targeting interventions in this area. Spatio-temporal scan statistics have the potential to direct interventions to high-risk locations in a timely manner and should be considered as part of the municipal surveillance routine as a tool to optimize prevention strategies.Author summaryDengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades, but vector-control strategies have not led to a significant reduction in the disease burden and were not sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015-2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly due to competition between viruses for host resources, and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Neighbourhoods with high population density and social vulnerability should be considered as important targets for interventions. Particularly in the North region, where clusters of the three diseases exist and the first chikungunya cluster occurred. The use of space-time cluster detection can direct intensive interventions to high-risk locations in a timely manner.

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