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

Punam Susan Tasmin Amratia Rumisha Symons PhD PhD (Biostatistics) Honorary Research Associate Honorary Research Associate Honorary Research Associate

Modelling the COVID pandemic with the Geographical COVID-19 Model (GEO-COV)

Researchers have developed a new model for simulating covid-19 outbreaks in Western Australia. 

Tracking global intervention coverage

Adam Dan Saddler Weiss PhD PhD Senior Research Officer Honorary Research Fellow Daniel.Weiss@thekids.org.au Senior Research Officer Honorary

Introduction of Aedes aegypti mosquitoes carrying wAlbB Wolbachia sharply decreases dengue incidence in disease hotspots

Partial replacement of resident Aedes aegypti mosquitoes with introduced mosquitoes carrying certain strains of inherited Wolbachia symbionts can result in transmission blocking of dengue and other viruses of public health importance. Wolbachia strain wAlbB is an effective transmission blocker and stable at high temperatures, making it particularly suitable for hot tropical climates.

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.

Spatio-temporal mapping of stunting and wasting in Nigerian children: A bivariate mixture modeling

Studies have shown that stunting and wasting indicators are strongly correlated among children, with the potential of concurrently affecting their physical and cognitive development. However, the identification of subpopulations of children with varying risks of stunting and wasting could be valuable for targeted intervention.

Comparison of new computational methods for spatial modelling of malaria

Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes.

Community knowledge, attitude, practices and beliefs associated with persistence of malaria transmission in North-western and Southern regions of Tanzania

Despite significant decline in the past two decades, malaria is still a major public health concern in Tanzania; with over 93% of the population still at risk. Community knowledge, attitudes and practices, and beliefs are key in enhancing uptake and utilization of malaria control interventions, but there is a lack of information on their contribution to effective control of the disease.

Trends in treatment-seeking for fever in children under five years old in 151 countries from 1990 to 2020

Access to medical treatment for fever is essential to prevent morbidity and mortality in individuals and to prevent transmission of communicable febrile illness in communities. Quantification of the rates at which treatment is accessed is critical for health system planning and a prerequisite for disease burden estimates. 

Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis\

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.