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Research

A global mathematical model of climatic suitability for Plasmodium falciparum malaria

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.

Research

Spatio-temporal spread of artemisinin resistance in Southeast Asia

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially.

Research

A malaria seasonality dataset for sub-Saharan Africa

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.

Research

Malaria treatment for prevention: a modelling study of the impact of routine case management on malaria prevalence and burden

Testing and treating symptomatic malaria cases is crucial for case management, but it may also prevent future illness by reducing mean infection duration. Measuring the impact of effective treatment on burden and transmission via field studies or routine surveillance systems is difficult and potentially unethical. This project uses mathematical modeling to explore how increasing treatment of symptomatic cases impacts malaria prevalence and incidence. 

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

Geospatial modelling for malaria risk stratification and intervention targeting for low-endemic countries

Ewan Punam Susan Tasmin Cameron Amratia Rumisha Symons BSc PhD PhD PhD (Biostatistics) Director of Malaria Risk Stratification Honorary Research

Research

Patterns and trends of in-hospital mortality due to non-communicable diseases and injuries in Tanzania, 2006–2015

Globally, non-communicable diseases (NCD) kill about 40 million people annually, with about three-quarters of the deaths occurring in low- and middle-income countries. This study was carried out to determine the patterns, trends, and causes of in-hospital non-communicable disease (NCD) and injury deaths in Tanzania from 2006-2015.

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

Modelling temperature-driven changes in species associations across freshwater communities

Due to global climate change–induced shifts in species distributions, estimating changes in community composition through the use of Species Distribution Models has become a key management tool. Being able to determine how species associations change along environmental gradients is likely to be pivotal in exploring the magnitude of future changes in species’ distributions.

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.