Group | Bayesian Modelling and Analysis

Predicted Risk Map of Malaria Parasite among Children under 5 year in Nigeria

The Bayesian Modelling and Analysis group carries out research in advanced statistical modelling and Bayesian computation. The main thematic areas of our research are spatio-temporal modelling and forecasting, modelling of multivariate data, including prediction of model uncertainty.

Spatio-temporal modelling is a focal research area of the group. It involves developing data-driven statistical methods and applications for malaria, neglected infectious diseases, cancer and mortality to obtain spatially explicit estimates of disease exposures and its associated burden, assess determinants of the space-time disease distribution and project disease dynamics.

Latest Publications

All Publications

Grolimund C.M et al. Modeling transmission mechanism to infer treatment efficacy of different drugs and combination therapy against Trichuris trichiura. Sci Rep. 2024;14(1):23543. DOI: 10.1038/s41598-024-73164-7

Kim J, Vounatsou P, Chun B.C. Changes in seasonality and sex ratio of scrub typhus: a case study of South Korea from 2003 to 2019 based on wavelet transform analysis. BMC Infect Dis. 2024;24:1066. DOI: 10.1186/s12879-024-09858-0

Nyawanda B.O et al. Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya. J Glob Health. 2024;14:04208. DOI: 10.7189/jogh.14.04208

Nyawanda B.O et al. The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019. BMJ Glob Health. 2024;9(9):e014614. DOI: 10.1136/bmjgh-2023-014614

Nyawanda B.O et al. The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020. Int J Health Geogr. 2024;23(1):22. DOI: 10.1186/s12942-024-00381-8