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New study identifies African ‘hotspot’ for highly infectious diseases

A regional corner of Africa is a hotspot for cases of HIV, tuberculosis and malaria, prompting researchers to call for targeted health support rather than a national response.

Geospatial Health and Development

Our Geospatial Health and Development 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.

Research

Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico

The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus, Dengue virus, and Zika virus clusters in Mexico.

Research

Rift Valley fever seropositivity in humans and domestic ruminants and associated risk factors in Sengerema, Ilala, and Rufiji districts, Tanzania

Data on Rift Valley fever virus (RVFV) prevalence in urban settings and pastoral areas of Tanzania are scarce. We performed a cross-sectional study of RVFV seroprevalence and determinants in humans and animals from Ilala, Rufiji, and Sengerema districts of Tanzania.

Research

Gaussian random fields: with and without covariances

We begin with isotropic Gaussian random fields, and show how the Bochner-Godement theorem gives a natural way to describe their covariance structure. We continue with a study of Matérn processes on Euclidean space, spheres, manifolds and graphs, using Bessel potentials and stochastic partial differential equations (SPDEs).

Research

A modelling approach to estimate the transmissibility of SARS-CoV 2 during periods of high, low, and zero case incidence

Against a backdrop ofwidespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major out-breaks, the effective reproduction number can be estimated froma time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority.

Research

Cholera risk in Lusaka: A geospatial analysis to inform improved water and sanitation provision

Urbanization combined with climate change are exacerbating water scarcity for an increasing number of the world’s emerging cities. Water and sanitation infrastructure, which in the first place was largely built to cater only to a small subsector of developing city populations, is increasingly coming under excessive strain.

Research

A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database

Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection.

Research

Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia

HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia.

Research

Individual variation in vaccine immune response can produce bimodal distributions of protection

The ability for vaccines to protect against infectious diseases varies among individuals, but computational models employed to inform policy typically do not account for this variation. Here we examine this issue: we implement a model of vaccine efficacy developed in the context of SARS-CoV-2 in order to evaluate the general implications of modelling correlates of protection on the individual level.