| [7] |
Los Angeles, US |
Investigating the relationships among socio-economic features of people and human mobility during COVID-19. |
| [45] |
13 countries of the world |
Evaluating the effectiveness of lockdown measures on the COVID-19 pandemic. |
| [46] |
New York and Seattle, US |
Investigating the impacts of post-COVID-19 reopening strategies on travel patterns and mode choice of people. |
| [47] |
Cracow, Poland |
Investigating changes in pedestrian activities in public places (e.g., tourist spots, residential areas, and places with mixed land uses) before and during COVID-19. |
| [48] |
50 states of the US |
Assessing the impacts of policy instruments (e.g., closing and reopening of retail stores, workplaces, businesses, places of entertainment and worship, and restriction on mobility) on the COVID-19 pandemic. |
| [49] |
US, Italy, Spain, Germany, France, and South Korea |
Understand the impacts of social distancing measures (i.e., mobility) on the transmission of COVID-19. |
| [50] |
401 counties in Germany |
Exploring the spatial (e.g., population density) and aspatial (e.g., socio-economic) factors of coronavirus diffusion. |
| [51] |
US and Australia |
Forecasting the effects of the COVID-19 pandemic on tourist arrivals. |
| [52] |
50 states of the US and District of Columbia |
Investigating the factors that affect human mobility and travel during the COVID-19 pandemic. |
| [53] |
26 countries of the world |
Examining the role of social distancing measures on the COVID-19 transmission rate. |
| [54] |
Iran |
Predicting coronavirus cases and identifying the associated factors that influence new daily cases. |
| [55] |
Boston, US |
Estimating the changes in people’s mobility due to COVID-19 situations and related local policy measures. |
| [56] |
Hayatabad, Pakistan |
Detecting the violation of social distancing measures. |
| [57] |
US |
Modeling COVID-19 transmission at the county level. |
| [58] |
Detroit, US |
Investigating the impacts of COVID-19 and social distancing measures on traffic volume and safety. |
| [59] |
Dane and Milwaukee County, City of Madison, US |
Modeling of COVID-19 spread and investigating the associations between COVID-19 transmission and mobility, business foot-traffic, and socioeconomic features. |
| [60] |
Wuhan, China |
Predicting the number of COVID-19 infection cases related to patient recovery and death. |
| [61] |
3219 counties in the US |
Developing a COVID-19 case prediction model with ML techniques based on county-level data. |
| [62] |
US |
Developing an interactive platform to analyze COVID-19 impact. |
| [63] |
China |
Predicting COVID-19 cases on the next day. |
| [64] |
Iran |
Investigating the impacts of air and inter-city travel on COVID-19 confirmed cases. |
