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Research

A Maximum Entropy Model of the Distribution of Dengue Serotype in Mexico

Pathogen strain diversity is an important driver of the trajectory of epidemics. The role of bioclimatic factors on the spatial distribution of dengue virus serotypes has, however, not been previously studied. Hence, we developed municipality-scale environmental suitability maps for the four dengue virus serotypes using maximum entropy modeling.

Research

Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures.

Research

Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data

Towards the goal of malaria elimination on Hispaniola, the National Malaria Control Program of Haiti and its international partner organisations are conducting a campaign of interventions targeted to high-risk communities prioritised through evidence-based planning. Here we present a key piece of this planning: an up-to-date, fine-scale endemicity map and seasonality profile for Haiti informed by monthly case counts.

Research

Spatial distribution of rotavirus immunization coverage in Ethiopia: a geospatial analysis using the Bayesian approach

Rotavirus causes substantial morbidity and mortality every year, particularly among under-five children. Despite Rotavirus immunization preventing severe diarrheal disease in children, the vaccination coverage remains inadequate in many African countries including Ethiopia.

Research

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

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.

Research

Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions

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.

Research

Comodity forecasting

Project description This project support the development of 10-year global forecasts of nets, insecticides, diagnostics, and treatments for malaria

News & Events

Sophisticated new modelling suggests keeping mask mandate could prevent 147,000 COVID-19 cases

WA’s current Omicron COVID-19 outbreak could jump by 147,000 cases if mask mandates are abandoned before the Easter long weekend, according to sophisticated new modelling.

News & Events

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.