About

Pamela P. Martinez is an Assistant Professor with appointments in Microbiology, Statistics, and the Center for Latin American and Caribbean Studies. Her scientific interest lies at the intersection of biological and epidemiological mechanisms that govern the population dynamics of infectious diseases. Her research group employs mathematical and statistical approaches to investigate how pathogen diversity, host heterogeneity, and environmental factors influence the transmission and control of human pathogens. A central focus of her work is understanding the emergence and maintenance of pathogen diversity and its impacts on disease control, with particular attention to how reassortment and immune responses affect rotavirus dynamics in endemic settings and interfere with public health interventions. Additionally, she studies the effects of sociodemographic factors such as age, race/ethnicity, income, and past exposures on viral disease transmission to better understand how population heterogeneity shapes disease dynamics. Her research also aims to characterize within-host dynamics of viral infections, including individual-level heterogeneity in infectiousness across different respiratory infections, to investigate viral shedding and clearance dynamics. Furthermore, she quantifies the impact of temperature and specific humidity on the seasonality and long-term dynamics of acute infections by fitting long time series of disease incidence to mechanistic transmission models, thereby measuring the interannual effects of environmental drivers on climate-sensitive diseases.

Research topics

• Medicine
• Biology
• Internal medicine
• Virology
• Sociology
• Political Science
• Immunology
• Geography
• Demography
• Environmental health
• Pathology
• Computational biology
• Genetics
• Economics
• Surgery
• Socioeconomics
• Medical education

Selected publications

• Author Correction: Malaria trends in Ethiopian highlands track the 2000 ‘slowdown’ in global warming

  Nature Communications · 2026-02-13

  articleOpen access

  In the version of this article initially published, while the code for the transmission rate , equation ( 8), was correctly implemented in the code for the numerical simulation of the model and its fitting to the time series data, as shared in Github,

  PublisherOA PDFDOI

• Changing COVID-19 vaccine eligibility could reshape disease burden for all

  medRxiv · 2026-04-29

  article

  Abstract COVID-19 vaccine recommendations are evolving in the United States. While older adults are most at risk of severe COVID-19 outcomes and therefore experience the greatest direct benefits of vaccination, limiting vaccination to only this age group could worsen outcomes in this higher-risk population. Here, we leveraged data from a statewide survey in Illinois to inform transmission models accounting for contact and vaccination rates across age. Simulating a single season of COVID-19 transmission, we compared deaths under existing vaccination coverage against counterfactual scenarios where individuals under 5 or under 65 were never vaccinated. We find substantial indirect vaccine impacts for older adults. Our results suggest that existing vaccination coverage among younger people is mitigating COVID-19 mortality for older populations. These findings can provide insights into the long-term consequences of deprioritizing young adults and children from vaccination campaigns, and suggest that a lack of vaccine-induced immunity may impact outcomes in other age groups. This underscores the importance of considering indirect vaccine impacts when developing policy.

  PublisherDOI

• Reimagining the serocatalytic model for infectious diseases: A case study of common coronaviruses

  Epidemics · 2025-10-09 · 1 citations

  articleOpen accessSenior authorCorresponding

  Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and seroreversion (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) across a wide range of age groups. These methods consistently identified multiple distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. We fit adapted, multi-compartment serocatalytic models with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses, finding that the time for a seronegative individual to become seropositive ranges from 2.40 to 7.03 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We find that despite frequent infection and strong serological responses, for all sHCoVs except 229E, individuals are likely to become seronegative again at some point after their first infection. Nonetheless, our results also indicate that by age 22, for each sHCoV the probability of having seroconverted at least once is over 95%. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.

  PublisherDOI

• Incorporating Authority Perception, Economic Status, and Behavioral Response in Infectious Disease Control

  arXiv (Cornell University) · 2025-12-29

  preprintOpen access

  We introduce a multi-population mean field game framework to examine how economic status and authority perception shape vaccination and social distancing decisions under different epidemic control policies. We carried out a survey to inform our model and stratify the population into six groups based on income and perception of authority, capturing behavioral heterogeneity. Individuals adjust their socialization and vaccination levels to optimize objectives such as minimizing treatment costs, complying with social-distancing guidelines if they are authority-followers, or reducing losses from decreased social interactions if they are authority-indifferents, alongside economic costs. Public health authorities influence behavior through social-distancing guidelines and vaccination costs. We characterize the Nash equilibrium via a forward-backward differential equation system, provide its mathematical analysis, and develop a numerical algorithm to solve it. Our findings reveal a trade-off between social-distancing and vaccination decisions. Under stricter guidelines that target both susceptible and infected individuals, followers reduce both socialization and vaccination levels, while indifferents increase socialization due to followers' preventative measures. Adaptive guidelines targeting infected individuals effectively reduce infections and narrow the gap between low- and high-income groups, even when susceptible individuals socialize more and vaccinate less. Lower vaccination costs incentivize vaccination among low-income groups, but their impact on disease spread is smaller than when they are coupled with social-distancing guidelines. Trust-building emerges as a critical factor in epidemic mitigation, underscoring the importance of data-informed, game-theoretical models that aim to understand complex human responses to mitigation policies.

  PublisherDOI

• Influence of immune history when choosing a SARS-CoV-2 booster strategy

  Scientific Reports · 2025-10-13

  articleOpen access

  Given the continued emergence of SARS-CoV-2 variants of concern as well as unprecedented vaccine development, it is crucial to understand the effect of updated vaccine formulations at the population level. While bivalent formulations developed during 2022 have had higher efficacy in vaccine trials, translating these findings to real-world effectiveness is challenging due to diversity in immune history, especially in settings with a high degree of natural immunity. Known socioeconomic disparities in key metrics such as vaccine coverage, social distancing, and access to healthcare have likely shaped the development and distribution of this immune landscape. Yet little has been done to investigate the impact of booster formulation in the context of host heterogeneity. Here, we present work undertaken in 2022-2023 to inform the World Health Organization's Immunization and Vaccines Related Implementation Research Advisory Committee (IVIR-AC), at a time when policymakers were considering optimal boosting strategies. Using two complementary mathematical models that capture host demographics and immune histories over time, we investigated the potential impacts of bivalent and monovalent boosters, inspired by disease dynamics in low- and middle-income countries (LMICs). These models allowed us to test the role of natural immunity and cross-protection in determining optimal booster strategy. Our results show that in hypothetical populations with high pre-existing immunity in the 2022-23 season, disease-related deaths from a new variant would be more sensitive to boosting/no boosting than booster formulation (bivalent vs. monovalent) - and if using bivalent formulations would result in delayed implementation compared to monovalent, it would almost always be better to implement monovalent immediately. However, deaths might be more sensitive to bivalent formulations in populations with low pre-existing immunity. These findings suggest that for many places where acquiring new vaccine stock may be economically prohibitive, monovalent boosters could still have been implemented where pre-existing immunity was high. While this analysis focuses on policy concerns in 2022, these results remain relevant now amidst ongoing questions about optimal booster formulation and timing to combat emerging transmission waves of COVID-19.

  PublisherOA PDFDOI

• Urban contact patterns shape respiratory syncytial virus epidemics with implications for vaccination

  Science Advances · 2025-11-26 · 2 citations

  articleOpen access

  Urban environments may alter the landscape of disease transmission with implications for control. Yet, it is unclear whether urban-rural differences exist in the dynamics of childhood respiratory diseases, given specific mixing patterns in younger age groups. Here, we leverage county-level data on respiratory syncytial virus (RSV) from the United States to reveal an urban-rural gradient in both the intensity and age structure of the RSV epidemic, where urban locations experience more prolonged epidemics with higher burden in infants (under 1 year of age). We develop a mechanistic epidemiological model to show that these differences can be explained by daycare utilization rates in children under 5. Using our model to consider control measures, we find that expanding seasonal immunization access in urban and rural areas may limit the risk of off season RSV epidemics.

  PublisherDOI

• Incorporating Authority Perception, Economic Status, and Behavioral Response in Infectious Disease Control

  ArXiv.org · 2025-12-29

  articleOpen access

  We introduce a multi-population mean field game framework to examine how economic status and authority perception shape vaccination and social distancing decisions under different epidemic control policies. We carried out a survey to inform our model and stratify the population into six groups based on income and perception of authority, capturing behavioral heterogeneity. Individuals adjust their socialization and vaccination levels to optimize objectives such as minimizing treatment costs, complying with social-distancing guidelines if they are authority-followers, or reducing losses from decreased social interactions if they are authority-indifferents, alongside economic costs. Public health authorities influence behavior through social-distancing guidelines and vaccination costs. We characterize the Nash equilibrium via a forward-backward differential equation system, provide its mathematical analysis, and develop a numerical algorithm to solve it. Our findings reveal a trade-off between social-distancing and vaccination decisions. Under stricter guidelines that target both susceptible and infected individuals, followers reduce both socialization and vaccination levels, while indifferents increase socialization due to followers' preventative measures. Adaptive guidelines targeting infected individuals effectively reduce infections and narrow the gap between low- and high-income groups, even when susceptible individuals socialize more and vaccinate less. Lower vaccination costs incentivize vaccination among low-income groups, but their impact on disease spread is smaller than when they are coupled with social-distancing guidelines. Trust-building emerges as a critical factor in epidemic mitigation, underscoring the importance of data-informed, game-theoretical models that aim to understand complex human responses to mitigation policies.

  PublisherOA PDF

• Linkage-based ortholog refinement in bacterial pangenomes with CLARC

  Nucleic Acids Research · 2025-06-18 · 1 citations

  articleOpen access

  Bacterial genomes exhibit significant variation in gene content and sequence identity. Pangenome analyses explore this diversity by classifying genes into core and accessory clusters of orthologous groups (COGs). However, strict sequence identity cutoffs can misclassify divergent alleles as different genes, inflating accessory gene counts. CLARC (Connected Linkage and Alignment Redefinition of COGs) (https://github.com/IndraGonz/CLARC) improves pangenome analyses by condensing accessory COGs using functional annotation and linkage information. Through this approach, orthologous groups are consolidated into more practical units of selection. Analyzing 8000+ Streptococcus pneumoniae genomes, CLARC reduced accessory gene estimates by >30% and improved evolutionary predictions based on accessory gene frequencies. CLARC is effective across different bacterial species, making it a broadly applicable tool for comparative genomics. By refining COG definitions, CLARC offers critical insights into bacterial evolution, aiding genetic studies across diverse populations.

  PublisherOA PDFDOI

• Reimagining the serocatalytic model for infectious diseases: a case study of common coronaviruses

  medRxiv · 2024-12-11 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and serore-version (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) throughout the lifespan. These methods identified four (NL63, 229E, OC43) to five (HKU1) distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. For each sHCoV, we fit adapted, multi-compartment serocatalytic models across 10 scenarios with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for a gradient of seropositivity as well as host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses across sHCoVs, finding that the time for a seronegative individual to become seropositive ranges from 2.33-4.07 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We also find that despite frequent infection and strong serological responses, it is rare for an individual to remain seropositive throughout the lifetime. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.

  PublisherOA PDFDOI

• Linkage-based ortholog refinement in bacterial pangenomes with CLARC

  bioRxiv (Cold Spring Harbor Laboratory) · 2024

  • Computational biology
  • Genetics
  • Biology

  genomes, CLARC reduced accessory gene estimates by more than 30% and improved evolutionary predictions based on accessory gene frequencies. By refining COG definitions, CLARC offers critical insights into bacterial evolution, aiding genetic studies across diverse populations.

  PublisherOA PDFDOI

Frequent coauthors

• Ayesha S. Mahmud

  University of California, Berkeley

  31 shared

• Jukka Corander

  University of Oslo

  24 shared

• Taj Azarian

  University of Central Florida

  19 shared

• Pablo A. Marquet

  Santa Fe Institute

  17 shared

• John Broach

  University of Massachusetts Chan Medical School

  15 shared

• Mauricio Santillana

  Northeastern University

  13 shared

• Matthew L. Robinson

  13 shared

• Caroline O. Buckee

  Harvard University

  13 shared