A retrospective cohort study on predicting infants at a risk of defaulting routine immunization in Uganda using machine learning models.

Objectives: Using machine learning models to predict infants at risk of defaulting routine immunization (RI) and identify significant features for Uganda.

Materials And Methods: Principal component analysis reduced dimensionality. Datasets were balanced using synthetic minority over-sampling technique.

Malaria outbreak facilitated by increased mosquito breeding sites near houses and cessation of indoor residual spraying, Kole district, Uganda, January-June 2019.

Background: In June 2019, surveillance data from the Uganda's District Health Information System revealed an outbreak of malaria in Kole District. Analysis revealed that cases had exceeded the outbreak threshold from January 2019. The Ministry of Health deployed our team to investigate the areas and people affected, identify risk factors for disease transmission, and recommend control and prevention measures.