About

Jing Chen is an Associate Professor of Biological Sciences at Virginia Tech, located in Derring Hall. Her research focuses on theoretical and computational modeling of cell biology, systems biology, spatiotemporal regulation, and mechano-biochemistry. Her current research interests include coordinated motility in bacterial colonies and mitotic signaling, with an emphasis on understanding the coupling between biological signaling and spatiotemporal regulation and mechanical interactions. She investigates how biological systems self-assemble into highly heterogeneous and dynamic structures, exploring the roles of spatiotemporal regulation and mechanical interactions within biological signaling mechanisms. Chen's work involves integrating experimental data into physically viable models to gain insights into the functional roles of spatiotemporal regulation and mechanical interactions, often working in close collaboration with experimental groups to ensure effective feedback between theory and experiments. Her educational background includes a Ph.D. in Biophysics from the University of California, Berkeley, a M.S. in Mathematics in Bioscience from the Technical University of Munich, and a B.S. in Biology from Fudan University. She completed a postdoctoral fellowship at the National Heart, Lung, and Blood Institute, NIH. Her research aims to deepen understanding of the complex interactions that govern cellular behavior and biological signaling processes.

Research topics

• Virology
• Medicine
• Computer Science
• Computer Security
• International trade
• Business
• Engineering
• Marketing
• Internal medicine
• Environmental health
• Demography
• Geography
• Transport engineering

Selected publications

• Scenario Projections of COVID-19 Burden in the US, 2024-2025

  JAMA Network Open · 2025-09-18 · 4 citations

  articleOpen access

  Importance: COVID-19 remains a disease with high burden in the US, prompting continued debate about optimal targets for annual vaccination. Objective: To project COVID-19 burden in the US for April 2024 to April 2025 under 6 scenarios of immune escape (20% and 50% per year) and levels of vaccine recommendation (no recommendation, vaccination for individuals at high risk only, vaccination for all eligible groups) and to assess the potential benefit of vaccine recommendations in reducing disease burden. Design, Setting, and Participants: For this decision analytical model, the US Scenario Modeling Hub, a collaborative modeling effort, convened 9 teams to provide scenario projections of US COVID-19 hospitalizations and deaths for April 2024 to April 2025, under 6 scenarios combining levels of immune escape and possible vaccine recommendations. Exposure: Annually reformulated vaccines were assumed to be 75% effective against hospitalization for variants circulating on June 15, 2024, and available on September 1, 2024. Age- and state-specific coverage was assumed to be as reported in September 2023 to April 2024. Main Outcomes and Measures: Ensemble estimates were made for weekly COVID-19 hospitalizations and deaths. Projections are presented for relative and absolute prevented hospitalizations and deaths averted due to vaccination over the April 2024 to April 2025 period. Results: For the US population (332 million, with an estimated 58 million aged ≥65 years), COVID-19 was expected to cause 814 000 (95% projection interval [PI], 400 000-1.2 million) hospitalizations and 54 000 (95% PI, 17 000-98 000) deaths for April 2024 to April 2025, comparable in magnitude to the prior year. Vaccination of high-risk groups only was projected to reduce hospitalizations (compared to no vaccination recommendation) by 76 000 (95% CI, 34 000-118 000) and deaths by 7000 (95% CI, 3000-11 000) across both immune escape scenarios. Compared with vaccinating high-risk groups only, a universal vaccine recommendation was projected to provide direct and indirect benefits, further preventing 11 000 hospitalizations and 1000 deaths in those aged 65 years and older. Conclusions and Relevance: In this decision analytical modeling study of COVID-19 burden in the US in 2024 to 2025, ensemble projections suggested that although vaccinating high-risk groups had substantial benefits in reducing disease burden, maintaining the vaccine recommendation for all individuals had the potential to save thousands more lives. Despite divergence of projections from observed disease trends in 2024 to 2025-possibly driven by variant emergence patterns and immune escape-averted COVID-19 burden due to vaccination was robust across immune escape scenarios, emphasizing the substantial benefit of broader vaccine availability for all individuals.

  PublisherOA PDFDOI

• Pandemics in Silico: Scaling Agent-Based Simulations on Realistic Social Contact Networks

  2025-06-03

  article

  Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC.

  PublisherDOI

• Scenario Projections of COVID-19 Burden in the US, 2024-2025

  UNC Libraries · 2025-09-25

  articleOpen access

  Importance: COVID-19 remains a disease with high burden in the US, prompting continued debate about optimal targets for annual vaccination. Objective: To project COVID-19 burden in the US for April 2024 to April 2025 under 6 scenarios of immune escape (20% and 50% per year) and levels of vaccine recommendation (no recommendation, vaccination for individuals at high risk only, vaccination for all eligible groups) and to assess the potential benefit of vaccine recommendations in reducing disease burden. Design, Setting, and Participants: For this decision analytical model, the US Scenario Modeling Hub, a collaborative modeling effort, convened 9 teams to provide scenario projections of US COVID-19 hospitalizations and deaths for April 2024 to April 2025, under 6 scenarios combining levels of immune escape and possible vaccine recommendations. Exposure: Annually reformulated vaccines were assumed to be 75% effective against hospitalization for variants circulating on June 15, 2024, and available on September 1, 2024. Age- and state-specific coverage was assumed to be as reported in September 2023 to April 2024. Main Outcomes and Measures: Ensemble estimates were made for weekly COVID-19 hospitalizations and deaths. Projections are presented for relative and absolute prevented hospitalizations and deaths averted due to vaccination over the April 2024 to April 2025 period. Results: For the US population (332 million, with an estimated 58 million aged ≥65 years), COVID-19 was expected to cause 814 000 (95% projection interval [PI], 400 000-1.2 million) hospitalizations and 54 000 (95% PI, 17 000-98 000) deaths for April 2024 to April 2025, comparable in magnitude to the prior year. Vaccination of high-risk groups only was projected to reduce hospitalizations (compared to no vaccination recommendation) by 76 000 (95% CI, 34 000-118 000) and deaths by 7000 (95% CI, 3000-11 000) across both immune escape scenarios. Compared with vaccinating high-risk groups only, a universal vaccine recommendation was projected to provide direct and indirect benefits, further preventing 11 000 hospitalizations and 1000 deaths in those aged 65 years and older. Conclusions and Relevance: In this decision analytical modeling study of COVID-19 burden in the US in 2024 to 2025, ensemble projections suggested that although vaccinating high-risk groups had substantial benefits in reducing disease burden, maintaining the vaccine recommendation for all individuals had the potential to save thousands more lives. Despite divergence of projections from observed disease trends in 2024 to 2025-possibly driven by variant emergence patterns and immune escape-averted COVID-19 burden due to vaccination was robust across immune escape scenarios, emphasizing the substantial benefit of broader vaccine availability for all individuals.

  PublisherDOI

• The impact of risk compensation adaptive behavior on the final epidemic size

  Mathematical Biosciences · 2025-01-01 · 5 citations

  article
  PublisherDOI

• Spillover-Aware Simulation Analysis for Policy Evaluation in Epidemic Networks

  2025-12-07

  article
  PublisherDOI

• A health and economic evaluation of the spatial spillover effect from measles resurgence

  Scientific Reports · 2025-10-14

  articleOpen access

  The administration of the Measles, Mumps, and Rubella (MMR) vaccination has had a substantial impact on controlling the spread of measles on a global scale. Nevertheless, the COVID-19 pandemic caused major disruptions to normal immunization schedules, causing the omission or delay of routine immunizations. Expanding on previous research that simulated measles outbreaks using a detailed agent-based model, this study integrates epidemiological forecasts with spatial econometrics analysis. Our objective is to quantify the household-level direct and indirect health and economic impact of measles outbreaks caused by reduction in MMR vaccine uptake. A network-based SEIR (susceptible-exposed-infected-recovered) model is used to simulate the transmission of measles over a synthetic social contact network of Virginia, under various scenarios. Household-level costs of measles outbreak, encompassing MMR vaccine expenses, treatment costs, and productivity losses, are estimated from the simulation results. A Generalized Spatial Autoregressive (GSAR) model is used to estimate the spatial 'spillover effect' on neighboring counties. Our findings indicate that reduced MMR vaccination rates are associated with increased measles cases and related economic costs, which are intensified by disease transmissibility and moderated by home quarantine. The GSAR model, with spatial lag coefficients, shows significant spatial interdependencies. A small decrease in vaccination rate in an urban region like Richmond, Virginia, has significant economic and epidemiological spillover effect, while similar reductions in rural regions like Highland County, Virginia, have a negligible impact. A decline in MMR vaccination rate has ramifications for both disease incidence and the economy, presenting diverse consequences influenced by regional disparities. Policymakers should acknowledge the interconnectedness of health and economic outcomes across regions. This research underscores the necessity of implementing broad, region-wide policy measures in response to fluctuations in vaccination rates, prioritizing overarching strategies over localized interventions.

  PublisherOA PDFDOI

• State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences

  2024-10-28

  article

  Causal inference on graphs has emerged as a critical area of research, with applications ranging from epidemiology to social networks and economics. In graph-structured data, estimating causal effects poses significant challenges, primarily due to the presence of confounding (including non-local confounding) and interference (violations of the Stable Unit Treatment Value Assumption, SUTVA), where the treatment of one node can influence the outcomes of other nodes. This work provides a comprehensive overview of methods developed to address these challenges, focusing on static and dynamic graphs. We categorize methods based on their ability to handle observed and unobserved confounding, interference, or both. We also explore the practical applications of these methods in various domains, demonstrating how they are used to address real-world causal inference problems. Finally, we identify key challenges in this field and suggest future directions for addressing them.

  PublisherDOI

• Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub

  PLoS Medicine · 2024-04-17 · 14 citations

  articleOpen accessCorresponding

  BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

  PublisherOA PDFDOI

• Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

  Journal of Parallel and Distributed Computing · 2024-05-03 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• A simple model of coupled individual behavior and its impact on epidemic dynamics

  Mathematical Biosciences · 2024-12-16 · 8 citations

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Madhav Marathe

  200 shared

• Bryan Lewis

  Biocom

  140 shared

• Srinivasan Venkatramanan

  Biocom

  119 shared

• Anil Vullikanti

  University of Virginia

  110 shared

• Henning Mortveit

  University of Virginia

  95 shared

• Stefan Hoops

  University of Virginia

  59 shared

• Aniruddha Adiga

  54 shared

• Achla Marathe

  51 shared