Journal of Data Mining en génomique et protéomique

Abstrait

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

Kaleab Tesfaye Tegegne, Eleni Tesfaye Tegegne, Mekibib Kassa Tessema, Geleta Abera, Berhanu Bifato, Kebebush Gebremichael, Abiyu Ayalew Assefa, Andualem Zenebe, Wosenyeleh Semeon Bagajjo, Musie Rike, Belayneh Feleke Weldeyes, Alelign Tadele Abebe, Argaw Getachew Alemu

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 WHO. The goal of this study is to uncover the spatiotemporal patterns ofCOVID- 19in Ethiopia, which will aid in the planning and implementation of essential preventative measures.

Methods: We obtained data onCOVID-19cases 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 ofCOVID-19at the county level in Ethiopia, using the discrete Poisson probability model.

Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199COVID-19cases were reported.

TheCOVID-19cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff’s scan statistics. The 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.