MEPI-04 Contributed Talks

Somdatta Sinha

Indian Institute of Science Education Research

"Compositional Complexity in Genomic Patterns and Classification"

A genome consists of a long string of four letters (bases A, T, C, G). How the information of biochemical processes stored in this string of bases is an open question. Are their higher order structures, such as, words, sentences, semantics, and a grammar in the DNA language (compositional complexity)? DNA from different species exhibit differences in global sequence composition, and this is used as markers to align larger sequences - grouping of genomes based on homology. Classification of genomes through similarity and dissimilarity is at the heart of Phylogenetics/Genomic Epidemiology. It uses several statistical-mathematical methods to align and compare the base sequences of multiple genomes, which are both computational resource intensive and time consuming for similar sequences. We develop and use an “alignment-free” method based on the Chaos-Game-Representation (CGR) of Statistical Physics, to successfully classify very closely related genomes of sub and sub-sub-species of HIV1 and mutants of Covid19. This useful approach requires less computational resources and time for analysis.

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Qi Deng

York University

"Exploring the potential impact of a chlamydia vaccine in the US population using an agent-based model"

Chlamydia trachomatis (CT) infection is the most reported bacterial sexually transmitted infection (STI) in the United States (US). Despite many cases being asymptomatic, infection can lead to complications such as pelvic inflammatory disease (PID) in females, and infertility in both females and males. We developed an agent-based transmission model to evaluate the impact of a potential CT vaccine on the prevalence of CT infections and associated PID in the US population. The model simulates an evolving sexual network of 10,000 sexually active agents aged 15–54, including heterosexuals, female sex workers, and men who have sex with men, following Susceptible–Exposed–Infected–Recovered–Susceptible (SEIRS) transmission dynamics. A key strength of the model is its rigorous two-step calibration procedure, which first matches real CT prevalence by age and sex, followed by real PID prevalence by age in the US. This ensures realistic alignment with epidemiological patterns. The model incorporates both vaccination and test-and-treat strategies, enabling direct comparisons between interventions. We then evaluated the impact of different scenarios of vaccination coverage and targeting, assuming a vaccine with 80% efficacy against infection and a 5-year duration of protection. The results demonstrate a gender-neutral vaccine recommendation is projected to achieve the highest impact in reducing CT prevalence and PID burden, even with a moderate vaccination coverage. Beyond CT, this is flexible, computationally efficient framework is adaptable to study other STIs and assess the effectiveness of various intervention strategies, given appropriate epidemiological and behavioral data. By providing actionable insights, this framework serves as a decision-support tool for policymakers, public health officials, and vaccine developers.

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Woldegebriel Assefa Woldegerima

York University

"Singular Perturbation Analysis of a Two-Time Scale Model of Vector-Borne Disease"

Biological systems evolve across different spatial and temporal scales. Modeling such complex systems gives rise to multi-scale differential equations that may be written as ODEs, PDEs, DDEs, SDEs, or Difference Equations. Particularly, vector-borne disease models are often described using ordinary differential equations with multiple time scales, which can involve singular perturbations—situations where rapid transitions or significant changes in system behavior occur due to small parameter variations or the interaction between fast and slow dynamics. To analyze these multi- time scale problems, we employ tools such as Geometric Singular Perturbation Theory (GSPT), Tikhonov’s Theorem, and Fenichel’s Theory. These methods provide insights into complex phenomena, including the loss of normal hyperbolicity and other intricate behaviors. Particularly, applying singular perturbation theory to vector-borne diseases allows us to reduce the dynamics to a one-time scale and understand their dynamics. To illustrate this, we present a Zika virus model and apply Tikhonov’s theorem and GSPT to investigate the model’s asymptotic behavior. Additionally, we conduct a bifurcation analysis to explore how the system’s behavior changes with variations in the parameter . We illustrate the various qualitative scenarios of the reduced system under singular perturbation. We will show that the fast–slow models, even though in nonstandard form, can be studied by means of GSPT.

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Sarita Bugalia

The University of Arizona

"Modeling the Impact of Social Behavior, Under-Reporting, and Resources on Tuberculosis During COVID-19"

Despite being curable and preventable, tuberculosis (TB) still causes the highest mortality rates in the human population. However, the number of TB cases significantly reduced globally in 2020, according to the Global Tuberculosis Report by the World Health Organization, coinciding with the COVID-19 pandemic. These reductions in TB cases are likely due to a complex interplay between disruptions in TB health services and the case counts resulting from COVID-19. We developed a compartmental model for the co-infection of tuberculosis and COVID-19 in the human population to assess the impact of medical resources, mobility, under-reporting, and the social behavior (follow social distancing and face mask) of infected individuals with either disease. We computed the basic reproduction numbers for TB alone, COVID-19 alone, and the co-infection scenario. Additionally, key parameters and basic reproduction numbers were estimated by utilizing case studies from low-income, middle-income, and high-income countries in a multi-patch scenario. Our results indicate that increased social behavior among infected individuals significantly reduces the number of co-infected individuals. The impact of mobility was assessed using a two-patch model with emigration and immigration rates. It shows that the mobility of unreported infectious individuals significantly increases both active cases of TB and COVID-19. This study provides significant recommendations for medical providers and public health officials regarding TB elimination in high-income countries and TB reduction in lower-income countries with a high disease burden. The findings are also relevant for studying TB in the context of future pandemic scenarios.

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