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Statistical modelling under differential privacy constraints: a case study in fine-scale geographical analysis with Australian Bureau of Statistics TableBuilder dataConsistent with the principles of differential privacy protection, the Australian Bureau of Statistics artificially perturbs all count data from the Australian Census prior to its release to researchers through the TableBuilder platform. This perturbation involves the addition of random noise to every non-zero cell count followed by the suppression of small values to zero.
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What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of MobilityHuman movement drives spatial transmission patterns of infectious diseases. Population-level mobility patterns are often quantified using aggregated data sets, such as census migration surveys or mobile phone data. These data are often unable to quantify individual-level travel patterns and lack the information needed to discern how mobility varies by demographic groups. Individual-level datasets can capture additional, more precise, aspects of mobility that may impact disease risk or transmission patterns and determine how mobility differs across cohorts; however, these data are rare, particularly in locations such as sub-Saharan Africa.
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Mapping BCG vaccination coverage in Ethiopia between 2000 and 2019The Bacille-Calmette–Guerin (BCG) vaccination remains the primary strategy to prevent severe disseminated TB in young children, particularly in high TB-burden countries such as Ethiopia. Accurate knowledge of vaccination coverage in small geographical areas is critically important to developing targeted immunization campaigns. Thus, this study aimed to investigate the spatiotemporal distributions and ecological level determinants of BCG vaccination coverage in Ethiopia.
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Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventionsSince 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.
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A Maximum Entropy Model of the Distribution of Dengue Serotype in MexicoPathogen 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.
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Patterns and trends of in-hospital mortality due to non-communicable diseases and injuries in Tanzania, 2006–2015Globally, 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.
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Viral haemorrhagic fevers and malaria co-infections among febrile patients seeking health care in TanzaniaIn recent years there have been reports of viral haemorrhagic fever (VHF) epidemics in sub-Saharan Africa where malaria is endemic. VHF and malaria have overlapping clinical presentations making differential diagnosis a challenge.
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Modelling temperature-driven changes in species associations across freshwater communitiesDue 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.
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Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malariaIndividual-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.
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The Centres for Disease Control light trap and the human decoy trap compared to the human landing catch for measuring Anopheles biting in rural TanzaniaVector mosquito biting intensity is an important measure to understand malaria transmission. Human landing catch (HLC) is an effective but labour-intensive, expensive, and potentially hazardous entomological surveillance tool. The Centres for Disease Control light trap (CDC-LT) and the human decoy trap (HDT) are exposure-free alternatives.