Table 1.
Spatial approximations for the COVID-19 and other diseases spread.
| Author | Case Study | Approaches |
|---|---|---|
| Mao and Bian, 2010 a [7] | Buffalo Metropolitan Area and Niagara Falls | An individual spatially explicit model is established to replicate a network of urban contacts and simulate influenza epidemics. The resulting epidemic curves and infection intensity maps are used to analyze transmission dynamics. |
| Liang Mao, 2014 a [8] | Applicable to any city with 1 million inhabitants. | It proposes a spatially explicit agent-based model to simulate a triple diffusion process in a metropolitan area of 1 million people. |
| S. Zhao, 2020 b [9] | Mainland China | The association between Wuhan’s domestic passenger load and the number of confirmed 2019-nCoV cases in different cities in China is examined and explored. |
| Desjardins, M.R., (2020) b [10] | United States | A foresight space-time analysis detecting statistically significant space-time clusters of COVID-19 at a federal level in USA is conducted. |
| Kang, D. (2020) b [11] | Mainland China | This study explored the spatial epidemic dynamics of COVID-19 in mainland China. The Moran I Spatial Statistic with various neighbor definitions was used to perform a test to determine if there was a spatial association of COVID-19 infections. |
| Botá, A. et al., 2020 a,b [12] | Sweden | The Generalized Inverse Infection Method (GIIM) is performed to identify socioeconomic, travel, and environmental factors contributing to the spread of H1N1 in Sweden. |
| Mameulnd, S.E. et al., 2019 b [13] | Review to several cases in many countries, mainly in Europe. | A systematic review and meta-analysis on the link between socioeconomic status and pandemic outcome are carried out. |
| Rader B. et al., 2020 b [2] | China | Spatial variables for cities in China are analyzed alongside case count data to investigate the role of climate, urbanization, and variation in interventions across the country. |
| Copiello and Grillenzoni, 2020 b [6] | China | The relationship between demographic, socio-economic, and environmental conditions and the spread of the novel coronavirus COVID-19 in China is analyzed with spatial regression models |
| Hamidi et al., 2020 b [14] | USA | Using SEM analysis, the relationship between county density and COVID-19 mortality and infection rates is investigated. |
a Simulations or geostatistical tools to estimate the geographic spread of infectious diseases; b assessments of how some socioeconomic factors are related to the presence of infections in a given space.