Research

Below is a brief description of three of my major research areas, alongside papers and associated media coverage.

(I) Human Behavior and Disease Dynamics

Understanding human behavior is crucial for epidemiological modeling because population-level changes in behavior directly influence disease transmission dynamics, creating bidirectional feedback loops between behavior change and disease severity. Our empirical study (Urmi and Pant et al. 2025, PNAS) has shown strong correlations between behavior and disease severity metrics (e.g., mortality). Our modeling study demonstrates that incorporating human behavioral responses not only improves model fit to observed mortality data but also enhances predictive performance in cross-validation compared to equivalent models that ignore behavioral dynamics (Pant et al. 2024, BMB). Our latest study (Pant et al. 2025) reveals a troubling paradox: models that fail to explicitly account for behavior change systematically underestimate the basic reproduction number (a critical threshold quantity that determines outbreak occurrence) while simultaneously overestimating the overall disease burden. Moreover, using analytical approaches, we mathematically show that for a fixed reproduction number, the model that does not explicitly account for behavior change leads to a larger final size in comparison to a model that accounts for behavior change.

Papers:

Binod Pant*, Marko Lalovic*, István Z. Kiss, and Mauricio Santillana. "The Paradox of Neglecting Changes in Behavior: How Standard Epidemic Models Misestimate Both Transmissibility and Final Epidemic Size." medRxiv (2025): 2025-12.

Tamanna Urmi*, Binod Pant*, George Dewey, Alexi Quintana-Mathe, Iris Lang, James Druckman, Katherine Ognyanova, Matthew Baum, Roy H. Perlis, Christoph Riedl, David Lazer and Mauricio Santillana. "Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States." Proceedings of the National Academy of Sciences 122, no. 37 (2025): e2500655122.

Binod Pant, Salman Safdar, Mauricio Santillana, and Abba B. Gumel. "Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics in the United States." Bulletin of Mathematical Biology 86, no. 8 (2024): 92.

Fig (1)

Baseline and three behavioral models considered in Pant et al. (2025)

Fig (2)

When fitted to the same data, the baseline model, in comparison to behavioral models, underestimates the basic reproduction number and overestimates the final size. This figure is for the behavioral (Mixed) model. See Pant et al. (2025) for a similar comparison with respect to other behavioral models shown in Fig (3).

(II) Structural Identifiability, Observability, and Uncertainty Quantification in Mathematical Epidemiology

Uncertainty quantification (UQ) is standard practice in many scientific fields, yet mathematical epidemiology models—despite their widespread use in policy decision-making—often lack rigorous UQ analysis. Structural identifiability analysis can be viewed as a foundational first step toward UQ: it examines whether model parameters can be uniquely determined from perfect, noise-free observations generated by the model itself. This theoretical property establishes what a model can and cannot reliably infer under ideal conditions, before confronting the complexities of real-world data, which is inherently discrete, noisy, and generated by unknown underlying processes. Observability can be thought of as "structural identifiability" of state variables.

In a computational modeling study (Pant et al. 2026, IDM) demonstrated that fitting an SIR model to model-generated detected incidence data (where only a fraction of true incidence is detected) yields the expected non-identifiability, but incorporating even a single ideal synthetic seroprevalence measurement, which captures cumulative exposure at one point during the outbreak, reduced parameter uncertainty by orders of magnitude. Moreover, examining the parameter values obtained in the noise-free setting demonstrates practical identifiability, thereby implying the possibility of structural identifiability of the SIR model under the observation of detected incidence combined with one ideal seroprevalence data point. This finding has profound implications: it suggests that even a single noise-free data point from a complementary data stream can resolve structural identifiability issues. In a theoretical study (Pant et al. 2026, medRxiv) demonstrated two key results: (a) structurally unidentifiable models can still yield identifiable quantities of interest, such as the basic reproduction number; and (b) incorporating minimal complementary data (as few as a single data point) can render a previously structurally unidentifiable model structurally identifiable.

Papers:

Binod Pant, Omar Saucedo, and Gleb Pogudin. "Uncovering identifiability of epidemiological models: basic reproduction number and complementary data streams." medRxiv (2026): 2026-01.

Binod Pant, Matthew E. Levine, Anjalika Nande, Raul Garrido Garcia, George Dewey, Nicholas B. Link, and Mauricio Santillana ``Resolving Parameter Uncertainty in SIR Models Through Population-Level Serological Surveillance: A Synthetic Study." Infectious Disease Modelling (2026).

Fig (3)

A visual depiction of the consequence of structural unidentifiability. In a noise-free synthetic setting, incorporating even a single data point from the S(t) curve during data fitting reduces uncertainty by orders of magnitude (panel C), compared to fitting only observed new cases (panel B).

Fig (4)

Bayesian computational analysis demonstrates robustness under 'realistic' noise conditions. This image is an extension of Fig. (2) with noise.

(III) Mathematics of Transgenic Fungus-based Malaria Control

Death from malaria remains a critical global threat, with nearly 600,000 deaths reported in 2023 across malaria-endemic regions. Resistance to conventional insecticides used in long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) has rendered these primary control tools increasingly ineffective against Anopheles mosquitoes. Transgenic Metarhizium pingshaense (Met-Hybrid) fungus offers a promising biological alternative, achieving over 80% mosquito mortality within one week through periodic releases of fungus-exposed male mosquitoes that transmit lethal infections to females during mating. The fungal strategy could serve as a valuable complement to existing interventions like IRS and LLINs, particularly in addressing insecticide resistance challenges.

In our recent work (Pant et al. 2025, Applied Mathematical Modelling), we developed the first mathematical model to evaluate transgenic Metarhizium pingshaense fungus (Met-Hybrid) as a biocontrol strategy for malaria-transmitting Anopheles mosquitoes.

Our model incorporates two critical transmission pathways: direct mating-based transmission and indirect transmission through contact with fungus-colonized mosquito cadavers. We mathematically demonstrate that cadaver-mediated transmission is essential for fungal persistence in the mosquito population—without it, the fungus cannot establish in the population since the basic reproduction number is zero. Furthermore, we numerically demonstrate that periodic release of fungus-infected male mosquitoes has comparable performance to genetic approaches (where genetically modified mosquitoes are released). However, the fungal approach offers potential advantages in environmental sustainability and may face less resistance from ethical and regulatory perspectives, making it a promising complementary strategy for malaria vector control.

Paper(s):

Binod Pant, Etienne Bilgo, Arnaja Mitra, Salman Safdar, Abdoulaye Diabaté, Raymond St Leger, and Abba B. Gumel. "Could malaria mosquitoes be controlled by periodic releases of transgenic mosquitocidal Metarhizium pingshaense fungus? A mathematical modeling approach." Applied Mathematical Modelling (2025): 116540.

Media Coverage:
Northeastern University News: "Want to eradicate malaria-bearing mosquitoes? Try fungus, this researcher says" (November 2025)

Social Media Posts by Institutions:
LinkedIn post by the Network Science Institute at Northeastern University, Boston
Twitter post by CCDD, Harvard, Boston
Bluesky post by CCDD, Harvard, Boston

Fig (5)

We consider two mechanisms of fungal transmission within mosquito populations: (a) contact during mating and (b) contact with fungus-carrying cadavers.

Fig (6)

We prove that without cadaver-mediated transmission, the fungus cannot sustain itself within mosquito populations—the basic reproduction number is exactly zero. This figure illustrates the critical role of cadavers in completing the transmission cycle: fungus-exposed male mosquitoes must generate new fungus-exposed or infected males to maintain a non-zero basic reproduction number. Without cadaver contact, this cycle breaks down entirely.

To view my complete work, see Google Scholar, ORCID, or Research Gate

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