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Our Child Health Analytics Team uses cutting-edge technologies to better understand how and why the health and wellbeing of children varies from place to place. We develop innovative geospatial methods that can harness large, complex datasets to pinpoint hotspots of elevated risk, evaluate change through time, and explore underlying drivers.
Honorary Team Member
A potential response to the COVID-19 pandemic in sub-Saharan Africa (SSA) with long-term benefits is to provide electricity for medical equipment in rural health centers and communities. This study identifies a large gap in the electrification of healthcare facilities in SSA, and it shows that decentralized photovoltaic systems can offer a clean, reliable, quick, and cost-effective solution.
Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data.
Malaria imposes a significant global health burden and remains a major cause of child mortality in sub-Saharan Africa. In many countries, malaria transmission varies seasonally. The use of seasonally-deployed interventions is expanding, and the effectiveness of these control measures hinges on quantitative and geographically-specific characterisations of malaria seasonality.
Post-tuberculosis (TB) sequelae present a significant challenge in the management of TB survivors, often leading to persistent health issues even after successful treatment. Identifying risk factors associated with post-TB sequelae is important for improving outcomes and quality of life of TB survivors. This systematic review and meta-analysis aims to identify risk factors associated with long-term physical sequelae among TB survivors.
Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control.
To effectively inform infectious disease control strategies, accurate knowledge of the pathogen's transmission dynamics is required. Since the timings of infections are rarely known, estimates of the infection incidence, which is crucial for understanding the transmission dynamics, often rely on measurements of other quantities amenable to surveillance.
Nipah virus is a zoonotic paramyxovirus responsible for disease outbreaks with high fatality rates in south and southeast Asia. However, knowledge of the potential geographical extent and risk patterns of the virus is poor. We aimed to establish an integrated spatiotemporal and phylogenetic database of Nipah virus infections in humans and animals across south and southeast Asia.
Malaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017-2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.