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Dr James Azam

Research Fellow

United Kingdom

I am a Research Fellow at the Centre for Mathematical Modelling of Infectious Diseases (CMMID), where I work with Dr. Kathleen O’Reilly on:

 

  • Analyzing the utility of wastewater surveillance for routine monitoring of infectious diseases in South Africa
  • Developing mathematical and statistical models to predict the risk of polio outbreaks in Africa

Previously, I was a Research Software Engineer with the Epiverse-TRACE Initiative, developing and maintaining R packages like bpmodels, epichains, and EpiNow2 for reproducible outbreak/epidemic analytics.

Affiliations

Department of Infectious Disease Epidemiology and Dynamics
Faculty of Epidemiology and Population Health

Research

I am interested in developing mathematical/statistical models, methods, and open-source software to evaluate the impact of pharmaceutical and non-pharmaceutical interventions on the spread of infectious diseases. More broadly, I am interested in quantitative research that provides insights into improving health outcomes, especially in low-resource settings.

 

Additionally, I like contributing to and indulging in the literature on improving science, including science communication, science policy, and science cultural research around diversion and inclusion.

Research Area
Mathematical modelling
Modelling
Epidemiology
Applied mathematics
Statistical methods
Bayesian analysis
Disease and Health Conditions
Measles
COVID-19

Selected Publications

The risk of global Ebola virus spread is low: epidemiology of Ebola disease cases outside Africa, 1976 to May 2026.
VAN ZANDVOORT, K; PROCTER, SR; AZAM, JM; Sherratt, K; DAVIES, NG;
2026
Euro Surveill
Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches.
Aheto, JM K; Auzenbergs, M; Ferrari, MJ; Portnoy, A; Utazi, CE; Kakaï, RG; Gayawan, E; AZAM, JM; Nonvignon, J;
2026
PLOS global public health
cmmid/h5n1_uk_scenario_modelling
RUSSELL, TW; Lambert, JW; Quilty, B; AZAM, JM;
2026
Github
Estimates of epidemiological parameters for H5N1 influenza in humans: a rapid review.
Ward, J; Lambert, JW; Russell, TW; AZAM, JM; KUCHARSKI, AJ; FUNK, S; Quilty, BJ; Gressani, O; Hens, N; EDMUNDS, WJ;
2025
BMC infectious diseases
Estimates of epidemiological parameters for H5N1 influenza in humans: a rapid review
Ward, J; LAMBERT, J; RUSSELL, T; AZAM, J; KUCHARSKI, A; FUNK, S; Quilty, B; Gressani, O; Hens, N; EDMUNDS, J;
2024
medRxiv
epichains: Simulating and Analysing Transmission Chain Statistics Using Branching Process Models
AZAM, JM; FUNK, S; Finger, F; Kamvar, ZN; Gruson, H; Mané, K; Gupte, P; LAMBERT, JW;
2024
Comprehensive R Archive Network
EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters
ABBOTT, S; Hellewell, J; Sherratt, K; Gostic, K; Hickson, J; Badr, HS; DeWitt, M; AZAM, JM; EpiForecasts,; FUNK, S;
2024
Zenodo
Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050.
GBD 2021 Antimicrobial Resistance Collaborators,;
2024
Lancet (London, England)
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