About

Ayesha S. Mahmud is an Associate Professor in the Department of Demography at the University of California, Berkeley. She is a demographer with broad research interests focused on the interplay between human population changes, environmental factors, and infectious disease dynamics. Her work integrates theory and methods from demography and disease ecology to address key questions such as the timing of outbreaks, variations in the mortality burden of infectious diseases over time, the influence of population travel patterns on the spatial dynamics of outbreaks, and the impact of global environmental and demographic changes on the future landscape of infectious disease burden. Mahmud employs statistical methods and biologically mechanistic models to investigate these questions across multiple diseases in countries in Asia, Africa, and Central America. Her research utilizes diverse data sources including disease surveillance systems, hospital databases, climate models, human mobility data, and population surveys and censuses. Prior to joining Berkeley, she was a Rockefeller Foundation Planetary Health Fellow at Harvard University. She earned her Ph.D. in Demography from Princeton University in 2017.

Research topics

• Medicine
• Operations management
• Geography
• Demography
• Sociology
• Computer Science
• Economics
• Environmental health
• Virology
• Management
• Business
• Medical emergency
• Nursing
• Surgery
• Biology
• Internal medicine
• Emergency medicine
• Pediatrics
• Socioeconomics
• Engineering
• Marketing
• Intensive care medicine

Selected publications

• Interactions between immuno-epidemiology and individual decision-making for nonpharmaceutical interventions

  Trends in Microbiology · 2026-03-17

  article
  PublisherDOI

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

  medRxiv · 2026-04-29

  articleSenior author

  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

• Partisan differences in health behaviors can impact respiratory disease dynamics

  medRxiv · 2026-01-19

  articleOpen accessSenior author

  Abstract The transmission of respiratory pathogens is fundamentally shaped by human behaviors such as interpersonal contacts, use of face masks, and vaccination. Political party affiliation has been shown to be associated with health-related behaviors. Yet, partisan heterogeneity in health-related behaviors is typically not included in infectious disease transmission models. Here, we leveraged uniquely detailed data from the Berkeley Interpersonal Contacts Study (BICS) on partisan differences in contact rates, mask usage, and vaccination patterns during the first year of the COVID-19 pandemic. We find substantial differences in health-related behaviors by political affiliation. Republicans reported a significantly greater number of average daily contacts, lower propensity of using masks and of getting vaccinated for COVID-19. These findings hold even after controlling for observable demographic and location-based differences across survey respondents. We adapt the classic Susceptible-Infected-Recovered (SIR) model to incorporate partisan-specific behaviors and varying levels of political homophily to simulate an outbreak of a hypothetical respiratory pathogen. We find that the observed behavior differences lead to simulated Republicans experiencing higher infection and mortality rates and earlier peaks compared to Democrats. Incorporating greater within-group mixing further amplified partisan differences in disease outcomes. Finally, we show that failure to incorporate partisan behavioral heterogeneity in disease models can lead to inaccurate predictions about the size and timing of outbreaks in a population. Significance The timing and size of infectious disease outbreaks are shaped by health-related behaviors that affect disease transmission. Using data specifically designed to measure interpersonal contacts and other health behaviors, we find that during the COVID-19 pandemic, Republicans reported a significantly greater number of average daily contacts, lower propensity of using masks and of getting vaccinated. These observed differences lead to Republicans experiencing higher infection and mortality rates and earlier peaks compared to Democrats in a model simulating a hypothetical respiratory infection. Incorporating a preference for within-group mixing further amplified partisan differences in disease outcomes. Failure to incorporate partisan behavioral heterogeneity in disease models can lead to inaccurate predictions about the size and timing of outbreaks in a population.

  PublisherDOI

• Modeling the Impact of Climate Extremes on Seasonal Influenza Outbreaks Across Tropical and Temperate Locations

  GeoHealth · 2025-03-27 · 4 citations

  articleOpen access

  Influenza epidemics, a major contributor to global morbidity and mortality, are influenced by climate factors including absolute humidity and temperature. Climate change is expected to increase the frequency and severity of climate extremes, potentially impacting the duration and magnitude of future influenza epidemics. However, the extent of these projected effects on influenza outbreaks remains understudied. Here, we use an epidemiologic model adapted for temperate and tropical climates to explore how climate variability may affect seasonal influenza. Using climate anomalies derived from historical data, we found that simulated periods of anomalous climate conditions impacted both the projected influenza outbreak peak size and the total proportion infected, with the strongest effects observed when the anomaly was included just before the typical peak. Effects varied by climate: temperate regions showed a unimodal relationship, while tropical climates exhibited a nonlinear pattern. Our results emphasize that the intensity of weather extremes is key to understanding how climate change may affect influenza outbreaks, laying the groundwork for utilizing weather variability as a potential early warning for influenza activity.

  PublisherOA PDFDOI

• Clinical Audit of Gynecology Ward Shahida Islam Medical Complex, Lodhran: Evaluating Patient Care and Record Management

  Annals of Quaid-e-Azam Medical College · 2025-03-11

  articleOpen access

  Introduction: An audit is the objective, systematic, and critical analysis of medical care. It is a quality improvement process that seeks to improve patient care and outcomes. Objective: To review the performance of the Gynecology ward, assess the quality of patient care, and record maintenance. Methodology: This was a cross-sectional study conducted in the gynecology ward of Shahida Islam Medical Complex, Lodhran, from 30th March to 30th April 2024. The study population comprised patients admitted to the gynecology ward selected by a non-probability convenient sampling technique. A predesigned, pretested questionnaire was the tool for data collection. Informed consent was taken from all willing admitted patients. Data was analyzed using SPSS version 24. Results: Maintenance of patient records was good. All the essential drugs were available in the ward. Regarding the outcome of patients, out of 100 patients, 97(97%) were discharged from the ward, 1% left against medical advice, and one death was reported during the survey. The provisional diagnosis made was 94.%. Reports were properly checked (99%). Documentation of progress notes was excellent(100%). So, the overall performance of the gynecology ward was good. Conclusion: The overall performance of the gynecology ward was good.

  PublisherOA PDFDOI

• Bias in mobility datasets drives divergence in modeled outbreak dynamics

  Communications Medicine · 2025-01-07 · 3 citations

  articleOpen accessSenior authorCorresponding

  BACKGROUND: Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates. METHODS: We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources' effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country. RESULTS: We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources. CONCLUSIONS: Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.

  PublisherOA PDFDOI

• Childhood immune imprinting shapes cohort and period influenza mortality

  Science Advances · 2025-12-04

  articleOpen accessSenior author

  Influenza viruses encountered in childhood can leave a lasting immunological imprint. To disentangle the mortality effects of age, the circulating seasonal strain, and immune history, we fit statistical mortality models to 54 years of influenza mortality data from the United States. We find strong signatures of subtype-level imprinting in H1N1-dominated seasons following the 2009 pandemic-cohorts imprinted with more similar H1N1 strains experience greater protection. Furthermore, we find large differences in age-specific mortality risk across cohorts based on their imprinted strain and the seasonal strains that were dominant throughout their lifetime. In contrast to older H1N1- and H2N2-imprinted cohorts, our results show that more recent cohorts imprinted with H3N2 have experienced substantially higher mortality through most of their lifetime. Our results highlight the long-term consequences of immune imprinting and its impact on period and cohort influenza mortality. Overall, our findings have important implications for vaccination efforts and future influenza mortality burden.

  PublisherDOI

• 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

• Identifying malaria elimination strategies in the presence of human movement in Bangladesh

  Communications Medicine · 2025-11-07

  articleOpen access1st authorCorresponding

  BACKGROUND: Malaria transmission in the Chittagong Hill Tracts (CHT) districts in Bangladesh is characterized by considerable heterogeneity in incidence and the frequent mixing and importation of parasites across districts. Thus, elimination efforts must account for human mobility between endemic and non-endemic locations, and the relative importance of local transmission and parasite importation domestically. METHODS: We construct a metapopulation malaria model, parameterized by human mobility data and fit to epidemiological data, to guide elimination efforts in the region. RESULTS: We find substantial heterogeneity in the transmission intensity across the CHT, with the estimated basic reproduction number varying greatly across places with similar levels of observed incidence. When vector control interventions are applied locally, the greatest impact in reducing overall incidence are in places with both high transmission intensity and high connectivity with more populated districts in the western part of the CHT. CONCLUSIONS: Local elimination in several areas with low or intermediate incidence has a moderate impact in reducing overall incidence, indicating that only focusing on high incidence areas is not sufficient for malaria elimination. More generally, our modeling framework can be used to prioritize resource allocation and identify the conditions necessary for malaria elimination in the region.

  PublisherOA PDFDOI

• Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study

  PLOS Global Public Health · 2023-06-14 · 3 citations

  articleOpen accessCorresponding

  BACKGROUND AND OBJECTIVE: Estimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia. METHODS: We used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making. FINDINGS: This study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20-24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30-34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75-79 year group. For children 0-4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20-24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors' assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services. CONCLUSIONS: Our findings confirm the universality of certain COVID-19 risk factors-such as gender and age-while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

  PublisherOA PDFDOI

Frequent coauthors

• Caroline O. Buckee

  Harvard University

  45 shared

• Richard J. Maude

  Mahidol University

  34 shared

• Pamela P. Martinez

  University of Illinois Urbana-Champaign

  31 shared

• Mohammad Yunus

  State University of Gorontalo

  23 shared

• Olivo Miotto

  Mahidol University

  20 shared

• Ipsita Sinha

  University of Oxford

  17 shared

• Pablo A. Marquet

  Santa Fe Institute

  17 shared

• David A. Sack

  16 shared

Education

• PhD in Demography, Office of Population Research

  Princeton University

  2017

Awards & honors

• Rockefeller Foundation Planetary Health Fellow at Harvard Un…