Table 1.
Selected studies that used spatio-temporal analysis in COVID-19
| Study | Date | Spatio-temporal analysis | Subject |
|---|---|---|---|
| Luo et al. [30] | 2/17 | Regression | Correlation between the number of COVID-19 incidents and absolute humidity |
| Allam and Jones [31] | 2/27 | AI | Using universal data sharing standards and Artificial Intelligence (AI) to monitor and manage urban health |
| Bogoch et al. [32] | 3/13 | Data mining | Potential for the global spread of COVID-19 |
| Sangiorgio and Parisi [33] | 3/18 | GIS-MCDA | A multicriteria-based approach for analyzing the spread of COVID-19 in urban district lockdown |
| Zhou et al. [34] | 3/20 | Data mining | Reflections on the use of GIS with big data and spatiotemporal analysis of COVID-19 |
| Kang et al. [35] | 3/26 | Moran’s I spatial statistic | Investigating spatial dynamics of the COVID-19 in China |
| Chan et al. [36] | 3/29 | Web mapping/data mining | Analysis with mobility data from Google users |
| Tosepu et al. (2020) [37] | 4/3 | Regression | Correlation between climate and COVID-19 in Jakarta |
| Gupta et al. [38] | 4/19 | Data mining | Correlation between climatic characteristics and the spread of the virus in the USA, and extrapolation of the method to India |
| Allcott et al. [39] | 4/7 | Regression | Correlation between the ruling party in each county, social behavior, and confirmed COVID-19 cases |
| Velásquez and Mejía Lara [40] | 5/20 | Regression | Evaluating the spread of COVID-19 in the USA with Gaussian process regression |
| Cuevas [20] | 5/25 | Agent-based modeling | Using an agent-based model to assess the COVID-19 spread in facilities |
| Franch-Pardo et al. [41] | 6/4 | Data mining | A review of spatial analysis and GIS in studying COVID-19 |
| Jin et al. [42] | 6/9 | Interpolation | Examining the time, place, and population of COVID-19 in China between Jan 20 and Feb 10, 2020 |
| Pourghasemi et al. [43] | 6/17 | Regression/Random Forest | Spatial analysis and modeling of COVID-19 in Iran between Feb 19 and Jun 14, 2020 |
| Huang et al. [16] | 6/17 | Logistic regression model | Spatio-temporal analysis of COVID-19 and its relationship with epidemiological characteristics, control of measures taken, and their effects |
| Cordes and Castro [18] | 6/18 | Cluster analysis | Spatial analysis of COVID-19 spread in New York City |
| Chatterjee et al. [44] | 6/20 | Timeline Series Analysis | An innovative COVID-19 Risk Assessment Tool |
| Karaye and Horney [45] | 6/26 | Geographically weighted regression | Analyzing the association between the number of COVID-19 cases and social vulnerability in the U.S |
| Kulkarni and Anantharama (2020) [46] | 6/30 | Multi-objective approaches | Examining the impact of COVID-19 pandemic on municipal solid waste management |
| Gao et al. [29] | 8/9 | Statistical model | Assessing the connection between human mobility changes and COVID-19 incidence in the U.S |
| Sannigrahi et al. [47] | 10/1 | Geographically weighted regression | Assessing the relationship between socio-demographic conditions and COVID-19 deaths in the European region |
| Briz-Redón and Serrano-Aroca [21] | 10/8 | Statistical model | Examining the influence of temperature on COVID-19 early evolution in Spain |