Joseph H. Tien
Mathematics · The Ohio State University
Publications
63
Citations
2,276
Est. group size
~3
Recurring co-author estimate
Active years
36
Publishing since 1990
Joseph H. Tien develops mathematical models to study how infectious diseases spread through populations and networks, including diseases transmitted by vectors like mosquitoes and waterborne pathogens like cholera. His work also extends to analyzing social media and network data, such as tracking online misinformation and measuring political polarization on Twitter. This research combines tools from applied mathematics, network science, and epidemiology to understand both disease transmission and information spread.
Publication output rose sharply around 2021-2022 (likely reflecting COVID-19-related work) after a slower 2018-2020 period, and has since tapered off toward earlier, more modest annual counts.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Adapting InfoMap to Absorbing Random Walks Using Absorption-Scaled Graphs
SIAM Journal on Applied Dynamical Systems · 2024
- Social Media Sentiment about COVID-19 Vaccination Predicts Vaccine Acceptance among Peruvian Social Media Users the Next Day
Vaccines · 2023
- Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks
Infectious Disease Modelling · 2022
- Sentinel node approach to monitoring online COVID-19 misinformation
Scientific Reports · 2022
- Relating Eulerian and Lagrangian spatial models for vector-host disease dynamics through a fundamental matrix
Journal of Mathematical Biology · 2022
- Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks
arXiv (Cornell University) · 2022
- Relating Eulerian and Lagrangian spatial models for vector-host diseases dynamics through a fundamental matrix
arXiv (Cornell University) · 2021
- An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs
arXiv (Cornell University) · 2021
- Sentinel node approach to monitoring online COVID-19 misinformation
arXiv (Cornell University) · 2021
- Relating Eulerian and Lagrangian spatial models for vector-host diseases\n dynamics through a fundamental matrix
arXiv (Cornell University) · 2021
- Replication Data for: The Social Media Amplification of Risk: Sentiment about COVID-19 Vaccination Expressed in Social Media Posts Predicts Vaccine Acceptance Among Social Media Users in Peru the Next Day
Harvard Dataverse · 2021
- Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership
Applied Network Science · 2020
- Online reactions to the 2017 'Unite the Right' rally in Charlottesville:\n Measuring polarization in Twitter networks using media followership
arXiv (Cornell University) · 2019
- Complex contagion leads to complex dynamics in models coupling behaviour and disease
Journal of Biological Dynamics · 2018
- The large graph limit of a stochastic epidemic model on a dynamic multilayer network
Journal of Biological Dynamics · 2018
- arXiv (Cornell University)×11
- Journal of Biological Dynamics×3
- medRxiv×3
- Journal of Theoretical Biology×2
- Mathematical Biosciences×2
- Philip E. Paré
Mathematics · Purdue University West Lafayette
- Grzegorz A. Rempała
Mathematics · The Ohio State University
- Eben Kenah
Mathematics · The Ohio State University
- Zonghao Zhang
Mathematics · Purdue University West Lafayette
- Alessandro Vespignani
Mathematics · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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