Conference
Song Gao: RAPID: Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data
| Τίτλος: | Song Gao: RAPID: Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data |
|---|---|
| Συγγραφείς: | Gao, Song |
| Έτος έκδοσης: | 2021 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | COVID-19 (Disease), Diseases, Viruses, Geospatial data--Computer processing |
| Περιγραφή: | This presentation was made by Song Gao, University of Wisconsin-Madison. The presentation’s title is: “RAPID: Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data.” Funded by NSF Social, Behavioral and Economic Sciences / Division of Behavioral and Cognitive Sciences. -- Every month, the COVID Information Commons Team (along with the Northeast Big Data Innovation Hub) brings together a group of researchers studying wide-ranging aspects of the current pandemic, to share their research and answer questions from our community. The events showcase scientists' ongoing efforts in the fight against COVID-19, including opportunities for collaboration. |
| Τύπος εγγράφου: | conference object |
| Γλώσσα: | English |
| DOI: | 10.7916/bjey-j022 |
| Διαθεσιμότητα: | https://doi.org/10.7916/bjey-j022 |
| Αριθμός Καταχώρησης: | edsbas.CAFB255D |
| Βάση Δεδομένων: | BASE |
καταχωρήστε σχόλιο πρώτοι!