Haridah Alias

72 papers receiving 2.5k citations

Hit Papers

The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay 2020 · 604 citations
6042020202620222024200400600

Peers

Haridah Alias
Comparison fields: 5 of 141
  • Health 1.4k
  • Modeling and Simulation 582
  • Infectious Diseases 819
  • Applied Psychology 132
  • Clinical Psychology 511
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Qiuyan Liao Hong Kong
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Citations per year

Countries citing papers authored by Haridah Alias

Since Specialization
Citations

This map shows the geographic impact of Haridah Alias's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haridah Alias with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haridah Alias more than expected).

Fields of papers citing papers by Haridah Alias

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Haridah Alias. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haridah Alias. The network helps show where Haridah Alias may publish in the future.

Co-authors

The 25 scholars most cited alongside Haridah Alias, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Haridah Alias Line = papers co-authored together Haridah Alias links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1
The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay
Hit paper breakdown →
2020604
2
Understanding COVID-19 vaccine demand and hesitancy: A nationwide online survey in China
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2020501
3 2020121
4 2017101
5 202099
6 202188
7 202083
8 202183
9 202065
10 202140
11 201638
12 202037
13 201635
14 201933
15 202033
16 202232
17 202030
18 202029
19 202126
20 202025

About Haridah Alias

Haridah Alias is a scholar working on Health, Modeling and Simulation, Applied Psychology, Applied Microbiology and Biotechnology and Clinical Psychology, having authored 78 papers that have together received 2.6k indexed citations. Recurring topics across this work include Vaccine Coverage and Hesitancy (28 papers), COVID-19 and Mental Health (19 papers), Cervical Cancer and HPV Research (11 papers), Hepatitis B Virus Studies (10 papers), SARS-CoV-2 and COVID-19 Research (10 papers), COVID-19 epidemiological studies (8 papers), COVID-19 Pandemic Impacts (7 papers) and Mosquito-borne diseases and control (5 papers). The work is most often cited by research in Health (1.4k citations), Modeling and Simulation (582 citations), Infectious Diseases (819 citations), Applied Psychology (132 citations) and Clinical Psychology (511 citations). Haridah Alias has collaborated with scholars based in Malaysia, China and United States. Frequent co-authors include Li Ping Wong, Yulan Lin, Zhijian Hu, Sazaly AbuBakar, Hai Yen Lee, Pooi‐Fong Wong, Mahmoud Danaee, Qinjian Zhao, Nasrin Aghamohammadi and Gregory D. Zimet. Their work appears in journals such as Human Vaccines & Immunotherapeutics, Frontiers in Public Health, Vaccine, International Journal of Environmental Research and Public Health and OMEGA - Journal of Death and Dying.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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