Open Access Open Access  Restricted Access Subscription or Fee Access

Analysis of Spatiotemporal Pattern for COVID‐19 in Algeria Using Space‐Time-Cubes

(*) Corresponding author

Authors' affiliations



This study aims to analyze the spatiotemporal models of the 2019 coronavirus pandemic (SARS CoV-2) in Algeria and to identify the distribution and spatiotemporal evolution characteristics of the pandemic. Thus, space-time cubes were used to analyze the local outliers, spatiotemporal clustering models and the trends of cold spots and hotspots of the SARS CoV-2 cases. This study has concluded that the pandemic, at first, has spread slowly in northern Algeria, and then has accelerated quickly to affect the whole country, before slowing down and stabilizing. In addition, the results of the outliers analysis has suggested that the pandemic had a high clustering pattern during the studied period, and the Low-Low outlier has been the main spatiotemporal clustering pattern, which indicates that the pandemic situation has remained at a relatively low level in general. In addition, the analysis of emerging hotspots has helped to detect different types of hotspots in the northern areas of Algeria and different types of cold spots in the far south and in the center of the country. Finally, the use of spatiotemporal analysis based on space time cubes can determine the different spatiotemporal models of SARS CoV-2 data.
Copyright © 2022 Praise Worthy Prize - All rights reserved.


SARS CoV-2; Covid-19; Space-Time-Cubes; Spatiotemporal Hotspots; Spatiotemporal Outliers

Full Text:



C. Huang, Y. Wang, L. Xingwang, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223): 497-506, January 2020.

C. Hopkins, and N. Kumar, Loss of sense of smell as marker of COVID-19 infection. The Royal College of Surgeons of England: British Rhinological Society, 2020.

T.P.Velavan, and C.G. Meyer, The COVID‐19 epidemic. Tropical medicine & international health, 25(3): 278, 2020.

World Health Organization.

Algerian Ministry of Health, Population and Hospital Reform.

S. Sarwar, R. Waheed, et al., COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions? Science of the Total Environment, 730: p. 139089, 2020.

C. Zhou, S. Fenzhen, et al., COVID-19: Challenges to GIS with big data. Geography and Sustainability, 1(1):77_87 , 2020.

L. Sharma, and R. Verma, Latent Blowout of COVID-19 Globally: An Effort to Healthcare Alertness via Medical GIS Approach. medRxiv, 2020.

H. Ren, et al., Early forecasting of the potential risk zones of COVID-19 in China's megacities. Science of the Total Environment. 729: p. 138995, 2020

A.R. Akhmetzhanov, et al., Estimation of the actual incidence of coronavirus disease (COVID-19) in emergent hotspots: The example of Hokkaido, Japan during February-March 2020. medRxiv, 2020.

H. Ankarali, et al., A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis. Asia-Pacific Journal of Public Health, 2020.

M. Sayadi, et al., A Linear Study of the Spread of COVID19 in China and Iran. Frontiers in Health Informatics, 9(1): p. 32, 2020.

A. Maroko, D. Nash, and B. Pavilonis, Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots. Journal of Urban Health, 97(4): 461-470, 2020.

P.A. da Cruz, and L.C.C. Cruz, Mathematical Modeling and Epidemic Prediction of COVID-19 of the State of São Paulo, Brazil. International Journal of Advanced Engineering Research and Science, 7(5), 2020.

J. Wang, et al., Global dynamics of a SUIR model with predicting COVID-19. arXiv preprint arXiv:2004.12433, 2020.

G. Kobayashi, et al., Predicting Infection of COVID-19 in Japan: State Space Modeling Approach. arXiv preprint arXiv:2004.13483, 2020.

D. Calvetti, et al., Metapopulation network models for understanding, predicting and managing the coronavirus disease COVID-19. arXiv preprint arXiv:2005.06137, 2020.

M.G. Plessen, Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining. arXiv preprint arXiv:2005.00592, 2020.

C. Yuanyuan, Linear regression analysis of COVID-19 outbreak and control in Henan province caused by the output population from Wuhan. medRxiv, 2020.

A.M. Almeshal, et al., Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models. Applied Sciences,. 10(10): p. 3402, 2020.

S. Patrikar, et al., Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India. Medical Journal Armed Forces India, 2020.

C.J. Huang, et al., Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019. medRxiv, 2020.

N. Zheng, et al., Predicting covid-19 in china using hybrid AI model. IEEE Transactions on Cybernetics, 2020.

J.H. Fowler, et al., The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States. medRxiv, 2020.

D. Below, and F. Mairanowski, Prediction of the coronavirus epidemic prevalence in quarantine conditions based on an approximate calculation model. medRxiv, 2020.

E. Gayawan, et al., The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa. medRxiv, 2020.

Z. Zhao, et al., Prediction of the COVID-19 spread in African countries and implications for prevention and controls: a case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya. Science of the Total Environment, p. 138959, 2020.

M. Hamidouche, COVID-19 Epidemic in Algeria: Assessment of the implemented preventive strategy. medRxiv, 2020.

M. Boukhatem, Novel Coronavirus Disease 2019 (COVID-19) Outbreak in Algeria: A New Challenge for Prevention. J Community Med Health Care, 5(1): p. 1035. 2020.

M.S. Boudrioua, and A. Boudrioua, Predicting the COVID-19 epidemic in Algeria using the SIR model. medRxiv, 2020.

S. Bentout, A. Chekroun, and T. Kuniya, Parameter estimation and prediction for coronavirus disease outbreak 2019 (COVID-19) in Algeria. AIMS Public Health, 7(2): p. 306. 2020.

A. Moussaoui, and P. Auger, prediction of confinement effects on the number of covid-19 outbreak in algeria. 2020.

M. Hamidouche, COVID-19 outbreak in Algeria: A mathematical model to predict the incidence. medRxiv, 2020.

S.C. Gamoura, Real-time Data Analytics and prediction of the COVID-19 pandemic (Period to March 22th, 2020). Published on March 22, 2020.

M. Desjardins, A. Hohl, and E. Delmelle, Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Applied Geography, p. 102202, 2020.

D. Kang, et al., Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 2020.

W. Yang, et al., Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China. International Journal of Environmental Research and Public Health. 17 (7): p. 2563 , 2020

L.A. Andrade, et al., Surveillance of the first cases of COVID-19 in Sergipe using a prospective spatiotemporal analysis: the spatial dispersion and its public health implications. Revista da Sociedade Brasileira de Medicina Tropical, 53, 2020.

K. Al-Ahmadi, S. Alahmadi, and A. Al-Zahrani, Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012-2019. International Journal of Environmental Research and Public Health, 16(14): p. 2520, 2019.

A. Lojić Kapetanović, and D. Poljak, Modeling the Epidemic Outbreak and Dynamics of COVID-19 in Croatia. arXiv,: p. arXiv: 2005.01434, 2020.

A. Rovetta A. S. Bhagavathula, and L. Castaldo, Modelling the epidemiological trend and behavior of COVID-19 in Italy. medRxiv, 2020.

J. Munshi, I. Roy, and G. Balasubramanian, Spatiotemporal dynamics in demography-sensitive disease transmission: COVID-19 spread in NY as a case study. arXiv preprint arXiv:2005.01001, 2020.

R. Huang, M. Liu, and Y. Ding, Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. The Journal of Infection in Developing Countries, 14(03): p. 246-253, 2020.

B. Gross, et al., Spatio-temporal propagation of COVID-19 pandemics. medRxiv, 2020.

L. Lorch, et al., A spatiotemporal epidemic model to quantify the effects of contact tracing, testing, and containment. arXiv preprint arXiv:2004.07641, 2020.

C. Yang, et al., Taking the pulse of COVID-19: A spatiotemporal perspective. arXiv preprint arXiv:2005.04224, 2020.

T. Hagerstrand, What about People in Regional Science? Papers and Proceedingsof the Regional Science Association, 1970.

S. Zhang, D.D. Zhang, and J. Li, Climate Change and the Pattern of the Hot Spots of War in Ancient China. Atmosphere, 11(4): p. 378, 2020.

M. Feng, et al., Relative space-based GIS data model to analyze the group dynamics of moving objects. ISPRS Journal of Photogrammetry and Remote Sensing, 153: p. 74-95, 2019.

Fang, T.B. and Lu, Y. (2011), Constructing a Near Real-time Space-time Cube to Depict Urban Ambient Air Pollution Scenario. Transactions in GIS, 15: 635-649.

Midoun, M., Belbachir, H., New Approach for Spatiotemporal Data Mining: Combining Spatiotemporal and Visual Data Mining for Traffic Analysis, (2019) International Review of Aerospace Engineering (IREASE), 12 (5), pp. 205-215.

S. Abdrakhmanov, et al., Spatio-temporal analysis and visualisation of the anthrax epidemic situation in livestock in Kazakhstan over the period 1933-2016. Geospatial health, 12 (2): p. 589, 2017.

C. Mo, et al., An analysis of spatiotemporal pattern for COIVD‐19 in China based on space‐time cube. Journal of Medical Virology, 2020.

L. Anselin, Local indicators of spatial association-LISA. Geographical analysis,. 27(2): p. 93-115, 1995.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize