Journal of Data Mining in Genomics & Proteomics

COVID-19 Space-Time Cluster Detection in Ethiopia Using Retrospective Analysis

Kaleab Tesfaye Tegegne^1*, Eleni Tesfaye Tegegne², Mekibib Kassa Tessema³, Geleta Abera¹, Berhanu Bifato¹, Kebebush Gebremichael¹, Abiyu Ayalew Assefa¹, Andualem Zenebe¹, Wosenyeleh Semeon Bagajjo⁴, Musie Rike⁵, Belayneh Feleke Weldeyes⁶, Alelign Tadele Abebe⁷ and Argaw Getachew Alemu^{8 1}Department of Public Health, Hawassa College of Health Science, Hawassa, Ethiopia
²College of Medicine and Health Science, School of Nursing, University of Gondar, Gondar, Ethiopia
³Leishmania Research and Treatment Centre, University of Gondar, Gondar, Ethiopia
⁴Hawassa College of Health Science, Hawassa, Ethiopia
⁵Research and Publication Directorate, Hawassa College of Health Science, Hawassa, Ethiopia
⁶Department of Mid Wifery, Hawassa College of Health Science, Hawassa, Ethiopia
⁷Department of Medical Laboratory Technology, Hawassa College of Health Science, Hawassa, Ethiopia
⁸Tenta Gashena Road Project Co-Ordinator, Hawassa, Ethiopia
^*Correspondence: Kaleab Tesfaye Tegegne, Department of Public Health, Hawassa College of Health Science, Hawassa, Ethiopia, Email:

Received: 20-Jan-2022, Manuscript No. JDMGP-22-15489; Editor assigned: 24-Jan-2022, Pre QC No. JDMGP-22-15489 (PQ); Reviewed: 07-Feb-2022, QC No. JDMGP-22-15489; Revised: 10-Feb-2022, Manuscript No. JDMGP-22-15489 (R); Published: 17-Feb-2022, DOI: 10.4172/2153-0602.22.13.250

Introduction

AAs 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 WHO [1].Geographic Information Systems (GIS) and scan statistics are increasingly being used to evaluate and detect geographical and spatiotemporal changes and clustering of diseases, resulting in more information in terms of disease dispersion [2] COVID-19 has been found to cluster spatially and spatiotemporally in several investigations [3-13]. The Geographic Information System (GIS) is a useful tool for displaying and evaluating geographic features based on epidemiology data [14,15].

By giving scientific knowledge, establishing spatial relationships with other parameters, and identifying transmission patterns and clustering, GIS can be utilized in conjunction with spatial statistics to help limit the epidemic [16-19]. COVID-19 reports from Ethiopia suggest regional differences in trends and case notification rates, while it’s unclear if these variations are caused by the disease’s geographical and spatiotemporal pattern [20]. As a result, regardless of the illness burden in the community, the nationalCOVID-19program executes comparable Interventions have been inconsistent across places, which could be due to a lack of understanding of the disease’s distribution pattern in several circumstances. Furthermore, except for a single paper describing a geographic study of vulnerability to infection, case severity, and death, information concerning the disease’s spatial distribution is scarce [21].

Understanding the disease’s geographical pattern and spatiotemporal fluctuations across larger geographic settings, such as urban-rural areas, could aid policy and decision-making in resource-constrained situations like Ethiopia. As a result, we want to investigate the disease’s spatiotemporal clustering in Ethiopia over the last month.

Methodology

Study area and setting

• Ethiopia’s GPS coordinates are 9.1450° N and 40.4897° E.

• Ethiopia is the 26th largest country in terms of physical size, with a total area of 426,400 square miles.

• The country has a length of 1,018 miles and a width of 980 miles.

• Ethiopia has a land area of 90.5 percent and a sea area of 9.5 percent.

• In other terms, Ethiopia’s entire land area is 386,102 square miles and its total water area is 40,298 square miles [22]. Ethiopia’s population is over 117 million people [23].

• There are eleven regional states and two chartered cities in Ethiopia: Addis Ababa, the capital, and Dire Dawa, which was chartered in 2004 (Figure 1) [24].

Figure 1: Study map.

Data collection and analysis

From November 23 through December 29, 2021, data was collected. The data came from an Ethiopian health data website that is free to the general public [20]. After importing the data into Microsoft Excel and analyzing it with SaTscan software version 10, we used ArcGIS 10.3 to illustrate the relative risk ofCOVID-19in high-risk cluster sites.

Statistical analysis

To uncover COVID-19’s temporal, geographical, and space-time clusters, as well as determine whether the geographic grouping was due to chance or not, we used Kulldorff’s space-time scan statistical analysis [25]. The scan statistics do a cluster analysis and determine cluster size and locations using Monte Carlo Simulation, as well as compute the Relative Risk (RR) and generate a P-value.

Spatial analysis

With the assumption that the number of cases at each site was Poisson distributed with a known population at risk, the discrete Poisson model, as well as the number of cases (COVID-19 instances), population, and coordinates, were used as input files. Using scan circles of various sizes, including the default value in scan statistics, the most likely spatial clusters of COVID-19 were found. The maximum spatial cluster size was calculated using an upper limit of 50% of the population at risk. The likelihood ratio was utilized to compute relative risk [26], and the most likely and secondary clusters were determined and reported when the P-value was less than 0.05. The results of the investigation were displayed in tables and on maps to illustrate where the sickness had occurred at particularly high rates.

Space-time analysis

The space-time scan statistic approach uses a cylindrical window with a circular geographic foundation that is related to space and height according to time for potential clusters [26]. COVID- 19’s RR was supposed to be the same inside the glass as it was outside. The number of occurrences in areas is poisson-distributed based on a given risk population in the poisson probability model [26]. To establish the test of significance, the likelihood ratio test was compared to a null distribution created by a Monte Carlo Simulation. The permutation count was set to 999, while the statistical significance was set at P0.05. We ran a space-time investigation for the period 2021/11/23-2021/12/29 to see if there were any recent space-time clusters of the disease.

Results

A total of 22,199 COVID-19 cases were reported from November 23 through December 29, 2021. In Ethiopia A total of eight statistically significant areas, comprising a total of 11 regions, were identified by spatial clustering analysis. Addis Ababa has the highest proportion of high-risk locations, with a relative risk of more than three. Addis Ababa. The region, 9.005401 N, 38.763611 E (LLR=56388.10451, P 0.001) was found to be the centre of the most likely cluster area (Figure 2). With a radius of 0 kilometres, this circular area exclusively covered Addis Ababa. The total number of COVID- 19cases was 19772, and the risk of COVID-19was 198.72 times (RR=198.72) higher in this location than it was elsewhere (Table 1). The temporal cluster analysis with time aggregation length of 7 days and time precision of day showed thatCOVID-19cases were low from 2021/11/23-2021/12/1 (Figure 3). The low aggregated period forCOVID-19was observed in all regions from 2021/11/23- 2021/12/1 (LLR=31382.578351, P=0.001). During this period from 2021/11/23-2021/12/1, a total of 22, 199COVID-19cases were reported, and the risk of COVID-19 was (RR=infinity) very low (Table 2).

Figure 2: Spatial clustering of COVID 19 in Ethiopia.

Figure 3: Temporal graph.

Table 1: Spatial clustering of COVID-19 in Ethiopia from 2021/11/23-2021/12/29.

Table 2: Temporal clustering of COVID 19 cases in Ethiopia from 2021/11/23-2021/12/29.

Addis Ababa was the most likely spatiotemporal cluster location, and the high-risk period was from 2021/11/1-2021/11/30 (LLR=70369.783209, P 0.001). The area’s centre was Addis Ababa, which was located at 9.005401 N and 38.763611 E, with a radius of 0 km (Figure 4). During the period 2021/11/1-2021/11/30, a total of 19772COVID-19cases were recorded in this location, with an RR of 412.48 (Table 3).

Figure 4: Spatiotemporal clusters of COVID 19 in Ethiopia.

Table 3: Spatiotemporal clusters of COVID-19 in Ethiopia.

Discussion

Kulldorff’s scan statistical analysis was used to analyze the spatial, temporal, and spatiotemporal clusters ofCOVID-19in Ethiopia from 2021/11/23 to 2021/12/29. To our knowledge, no other study of this nature has been conducted in Ethiopia. Our research found that the distribution of COVID-19 cases in Addis Ababa was highly space-time clustered.

Addis Ababa was the epicenter of the COVID-19 outbreak. Multiple testing problems are taken into consideration in Kulldorff’s retrospective scan statistics, which is known as the most powerful method for evaluating geographical and temporal distribution utilizing routinely obtained data [27]. This approach has been used to detect disease clusters all over the world [28-31]. The results of our temporal scanning revealed that COVID-19 had a low-risk phase between November 23, 2021, and December 29, 2021. The spatiotemporal model utilized in this work examined both time and space distributions at the same time. The time-space scanning model, as opposed to the distinct spatial and temporal scanning models, produces a conclusion that is closer to the real-world situation. We discovered that COVID-19 instances were concentrated in Addis Ababa from 2021/11/23-2021/12/29 when we used this model to determine the Spatio-temporal distribution of COVID-19in Ethiopia. COVID-19 was more prevalent in Addis Ababa during this time than in other Ethiopian districts. The causes for the great magnitude of COVID-19in Addis Ababa are as follows: Addis Ababa is Ethiopia’s capital city, and it has a higher level of testing and quarantine coverage than the rest of the country. Many people, including Ethiopian long-distance vehicle drivers, traders, and others, have been traveling from Djibouti to Ethiopia via the route that connects the Amhara area, Addis Ababa, and Oromia region since the commencement of the COVID-19 pandemic, mostly owing to geographical proximity [32] SARS-CoV 2 infection is very dangerous for certain populations. Sentinel monitoring attempts to detect the early introduction and spread of COVID-19 are especially well-suited for such populations, especially in areas with low vaccination coverage or where layered preventative techniques are not used. Due to their high risk of exposure or severe illness, the CDC deems the ability to monitor COVID-19 incidence in the following populations to be particularly useful Health care workers, residents and staff members of long-term care facilities, incarcerated people, homeless people, and workers in high-density work sites, students and staff members of kindergarten-grade 12 schools and institutions of higher education, incarcerated people, homeless people, and workers in high-density work sites [33-38]. Rising case detection rates can act as an early warning indicator that prevention methods in the facility and the larger community need to be reinforced or introduced. Furthermore, strategic serial testing can aid in the prevention of SARS-CoV-2 transmission by quickly detecting asymptomatic cases, which are thought to account for at least 50% of SARS-CoV-2 transmission [39,40]. Further prevention and particular COVID-19 control methods should be addressed regarding the vaccine, testing, and prevention practices in other Ethiopian regions, according to our findings. Our research also confirmed the use of spatial and temporal clustering analysis with ArcGIS and SaTScan in identifying significant COVID-19 space-time clusters in Ethiopia. This could be utilized to develop COVID-19 preventive initiatives at the county level. However, the study’s analysis was limited. First and foremost, the data were studied at the county level, which is not the smallest administrative regionalization unit.As a result, we can rule out several important elements.Second, meteorological and socio-economic aspects were not taken into account in this study.

Conclusion

Using Kulldorff’s retrospective scan statistic approaches, we examined the geographical, temporal, and space-time clusters ofCOVID-19at the county level in Ethiopia from 201/11/23 to 2021/12/29.The results of the space-time scanning revealed that Addis Ababa is at high risk for COVID-19. These findings indicate that the Addis Ababa health office, Ethiopian Minister of Health, and Ethiopia Public Health Institute must implement preventive and control programs to reduceCOVID-19in Addis Ababa as soon as possible.

Declaration

Ethics approval and consent to participate

This research is based on secondary data from Ethiopia’s COVID-19, which is publically available at https://ethiopianhealthdata.org › COVID-19 Ethiopia Case Tracker Dashboard.

Consent for Publication

Not applicable

Availability of Data and Materials

The paper includes all data.

Competing Interest

There are no competing interests stated by the authors.

Funding

There was no financing available for this project.

Authors Contributions

KTT was responsible for the original drafting of the manuscript’s conceptualization, analysis, supervision, and development.

Methodology, Discussion, and Data Analysis were all done by KTT, ETT, AGA, and MKT.

KTT, ETT, MKT, GA, BB, KG, AT, AAA, AZ, WSB, MR, BFW, and AGA assisted with data analysis, critically revised the work, and agreed to be held accountable.

Acknowledgment

We’d like to thank Hawassa College of Health Science for assisting us with this study project.

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