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Malaria is a leading cause of death in school-aged children in sub-Saharan Africa, and non-fatal chronic malaria infections are associated with anaemia, school absence and decreased learning, preventing children from reaching their full potential. Malaria chemoprevention has led to substantial reductions in malaria in younger children in sub-Saharan Africa.

Mass drug administration (MDA) with monthly dihydroartemisinin-piperaquine (DHA-PQP) appears useful in malaria control and elimination strategies. Determining the relationship between consecutive piperaquine phosphate (PQP) exposure and its impact on QT interval prolongation is a key safety consideration for MDA campaigns.

Providing protection from malaria vector bites, both indoors and outdoors, is crucial to curbing malaria parasite transmission. Screening of house entry points, especially with incorporated insecticides, confers significant protection but remains a costly and labour-intensive application. Use of spatial repellents has shown promise in creating areas of protection in peri-domestic areas.

Vector mosquito biting intensity is an important measure to understand malaria transmission. Human landing catch (HLC) is an effective but labour-intensive, expensive, and potentially hazardous entomological surveillance tool. The Centres for Disease Control light trap (CDC-LT) and the human decoy trap (HDT) are exposure-free alternatives.

Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species-questions rarely answerable from individual entomological studies (that typically focus on a single location or species).

In recent years there have been reports of viral haemorrhagic fever (VHF) epidemics in sub-Saharan Africa where malaria is endemic. VHF and malaria have overlapping clinical presentations making differential diagnosis a challenge.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator.

A retrospective cohort study was conducted using clinical data from patients attending TB clinics at four public hospitals in Northwest Ethiopia. We modelled BMI trajectories after TB treatment initiation using linear mixed models, with adjustments for potential confounders.

Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach.

Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country.