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Dr Kaitlyn Johnson

Assistant Professor

United Kingdom

I'm an infectious disease modeler interested in statistical modeling, forecasting, real-time analysis and open source tool development. I am currently based in Sebastian Funk's EpiForecasts group at the London School of Hygiene and Tropical Medicine. I completed my PhD at the University of Texas at Austin in mathematical modeling in oncology. Prior to joining LSHTM, I worked on various aspects of infectious disease modeling at the UT COVID-19 Modeling Consortium, the Rockefeller Foundation's Pandemic Prevention Institute, and the CDC's Center for Forecasting and Outbreak Analytics.

Affiliations

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

Teaching

I plan to teach short courses in infectious disease modeling methods

Research

My main research interests are in developing methods and tools for nowcasting, forecasting, and real-time analysis of infectious diseases. In particular, I am interested in integrating non-traditional surveillance data streams, such as wastewater, into mechanistic, semi-mechanistic, and statistical modeling frameworks and evaluating the impact of both model design and input data sources on forecast performance. My interest in open source software development is driven by the desire to increase accessibility and reproducibility of these models for local health authorities and decision-makers, as well as to enable other researchers to extend these models and use them to answer novel scientific questions. 

Research Area
Statistical methods
Mathematical modelling
Epidemiology
Disease and Health Conditions
Infectious diseases

Selected Publications

Local Influenza Forecasts Outperform State-Level Forecasts in the United States
Kim, D; Pasco, R; JOHNSON, KE; Fox, SJ; Reich, NG; Meyers, LA;
2026
openRxiv
Collaborative estimation and evaluation of SARS-CoV-2 variant nowcasting in the United States
MacArthur, I; Robacker, T; Case, B; Fox, SJ; Morris, DH; Ray, EL; Rogers, B; Sweger, B; Linton, NM; Huddleston, J; Magee, A; Susswein, Z; Lee, J; Bedford, T; Figgins, MD; Suez, E; Prabhakar, R; Leon, T; Siegel, B; Thakur, M; Hoover, CM; Ryder, R; Elder, J; Kupperman, M; Ke, R; ... Johnson, KE.
2026
epiforecasts/evalvariantnowcasthub
JOHNSON, KE; Robacker, T; Claude,; Linton, N;
2026
Zenodo
Bayesian generative modeling for heterogeneous wastewater data applied to COVID-19 forecasting
JOHNSON, KE; Vega Yon, G; Brand, SP C; Zelaya, CO B; Bayer, D; Volkov, I; Susswein, Z; Magee, A; Gostic, KM; English, KM; Ghinai, I; Hamlet, A; Olesen, SW; Pulliam, JR C; Abbott, S; Morris, DH;
2026
openRxiv
Baseline nowcasting methods for handling delays in epidemiological data.
E Johnson, K; L Tang, M; Tyszka, E; Jones, L; Nemcova, B; Wolffram, D; Ergas, R; G Reich, N; FUNK, S; Mellor, J; Bracher, J; ABBOTT, S;
2026
Wellcome open research
A vision for estimation of the instantaneous reproductive number.
Milando, CW; Yon, GG V; JOHNSON, K; Urbinati, A; St-Onge, G; Klein, B; Cori, A; White, LF; Collabathon participants,;
2026
Epidemics
Code and data for: Composable probabilistic models can lower barriers to rigorous infectious disease modelling
Abbott, S; Brand, SP C; Ge, H; JOHNSON, KE; FROST, SD W; Cori, A; FUNK, S;
2026
Zenodo
Code and data for: The case for composable probabilistic infectious disease models
ABBOTT, S; Brand, SP C; Ge, H; JOHNSON, KE; FROST, SD W; Cori, A; FUNK, S;
2025
Zenodo
Baseline nowcasting methods for handling delays in epidemiological data
JOHNSON, K; Tang, M; Tyszka, E; Jones, L; Nemcova, B; Wolffram, D; Ergas, R; Reich, N; FUNK, S; Mellor, J; Bracher, J; ABBOTT, S;
2025
medRxiv
Baseline nowcasting methods for handling delays in epidemiological data
JOHNSON, K; Tang, M; Tyszka, E; Jones, L; Nemcova, B; Wolffram, D; Ergas, R; Reich, N; FUNK, S; Mellor, J; Bracher, J; ABBOTT, S;
2025
medRxiv
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