| Title |
Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods
|
|---|---|
| Published in |
PLoS Computational Biology, May 2021
|
| DOI | 10.1371/journal.pcbi.1008959 |
| Pubmed ID | |
| Authors |
Jue Tao Lim, Kenwin Maung, Sok Teng Tan, Suan Ee Ong, Jane Mingjie Lim, Joel Ruihan Koo, Haoyang Sun, Minah Park, Ken Wei Tan, Joanne Yoong, Alex R. Cook, Borame Sue Lee Dickens |
Twitter Demographics
The data shown below were collected from the profiles of 192 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 59 | 31% |
| United Kingdom | 4 | 2% |
| United States | 4 | 2% |
| Singapore | 2 | 1% |
| Sri Lanka | 2 | 1% |
| Austria | 2 | 1% |
| Japan | 1 | <1% |
| Indonesia | 1 | <1% |
| Switzerland | 1 | <1% |
| Other | 5 | 3% |
| Unknown | 111 | 58% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 173 | 90% |
| Scientists | 8 | 4% |
| Practitioners (doctors, other healthcare professionals) | 6 | 3% |
| Science communicators (journalists, bloggers, editors) | 5 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 14% |
| Researcher | 8 | 11% |
| Student > Bachelor | 6 | 8% |
| Unspecified | 5 | 7% |
| Lecturer | 3 | 4% |
| Other | 13 | 18% |
| Unknown | 29 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 5 | 7% |
| Medicine and Dentistry | 5 | 7% |
| Unspecified | 5 | 7% |
| Economics, Econometrics and Finance | 4 | 5% |
| Nursing and Health Professions | 4 | 5% |
| Other | 17 | 23% |
| Unknown | 34 | 46% |