Timothy M. Brenza
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies 3
- Neurology top 10%
- Pharmaceutical Science top 10%
- Advanced Drug Delivery Systems 3
-
- Nanoparticle-Based Drug Delivery 2
-
- RNA Interference and Gene Delivery 3
-
- Autophagy in Disease and Therapy 2
-
- COVID-19 Pandemic Impacts 2
-
- Chronic Obstructive Pulmonary Disease (COPD) Research 2
- Inhalation and Respiratory Drug Delivery 2
- Co-authors
- Dinesh V. KalagaMasahiro KawajiCh. Mohan Sai KumarK.E. ArunKumarAnumantha G. KanthasamyGovinda ChilkoorBalaraman KalyanaramanBalaji Narasimhan
- Journals
- Journal of Controlled Release (2 papers)Antioxidants and Redox Signaling (1 paper)Pharmaceutical Research (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Timothy M. Brenza
14 papers receiving 888 citations
Hit Papers
Peers
Comparison fields: 5 of 146
- Modeling and Simulation 97
- Neurology 75
- Pharmaceutical Science 46
- Biomaterials 89
- Management Science and Operations Research 78
Countries citing papers authored by Timothy M. Brenza
This map shows the geographic impact of Timothy M. Brenza'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 Timothy M. Brenza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timothy M. Brenza more than expected).
Fields of papers citing papers by Timothy M. Brenza
This network shows the impact of papers produced by Timothy M. Brenza. 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 Timothy M. Brenza. The network helps show where Timothy M. Brenza may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Timothy M. Brenza, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trendsbreakdown → | 2022 | 154 |
| 3 | 2021 | 129 | |
| 4 | 2021 | 156 | |
| 5 | 2020 | 42 | |
| 6 | 2018 | 23 | |
| 7 | 2017 | 106 | |
| 8 | 2016 | 86 | |
| 9 | 2016 | 86 | |
| 10 | 2015 | 58 | |
| 11 | 2015 | 38 | |
| 12 | 2014 | 17 | |
| 13 | 2014 | 2 | |
| 14 | 2009 | 6 |
About Timothy M. Brenza
Timothy M. Brenza is a scholar working on Modeling and Simulation, Pharmaceutical Science and Neurology, having authored 14 papers that have together received 904 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (3 papers), RNA Interference and Gene Delivery (3 papers), Advanced Drug Delivery Systems (3 papers), Nanoparticle-Based Drug Delivery (2 papers), Autophagy in Disease and Therapy (2 papers), COVID-19 Pandemic Impacts (2 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (2 papers) and Inhalation and Respiratory Drug Delivery (2 papers). The work is most often cited by research in Modeling and Simulation (97 citations), Neurology (75 citations) and Pharmaceutical Science (46 citations). Timothy M. Brenza has collaborated with scholars based in United States and India. Frequent co-authors include Dinesh V. Kalaga, Masahiro Kawaji, Ch. Mohan Sai Kumar, K.E. ArunKumar, Anumantha G. Kanthasamy, Govinda Chilkoor, Balaraman Kalyanaraman, Balaji Narasimhan, Vellareddy Anantharam and Dilshan S. Harischandra. Their work appears in journals such as Journal of Controlled Release, Antioxidants and Redox Signaling and Pharmaceutical Research.
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.