A5048386397- Remote Sensing in Agriculture
- Mosquito-borne diseases and control
- Land Use and Ecosystem Services
- Statistical Methods and Bayesian Inference
- Spatial and Panel Data Analysis
- Point processes and geometric inequalities
- Malaria Research and Control
- Economic and Environmental Valuation
- Data-Driven Disease Surveillance
- COVID-19 epidemiological studies
- Impact of Light on Environment and Health
- Remote-Sensing Image Classification
- Rangeland Management and Livestock Ecology
- Gaussian Processes and Bayesian Inference
- Zoonotic diseases and public health
- Remote Sensing and LiDAR Applications
- Target Tracking and Data Fusion in Sensor Networks
- HIV/AIDS Research and Interventions
- Spectroscopy and Chemometric Analyses
- Time Series Analysis and Forecasting
- Global Maternal and Child Health
- Video Surveillance and Tracking Methods
- Parasitic Diseases Research and Treatment
- Urban Transport and Accessibility
- Influenza Virus Research Studies
University of California, San Francisco
2016-2020
Global Brain Health Institute
2020
University of Sheffield
2014-2015
Quantifying and monitoring the spatial temporal dynamics of global land cover is critical for better understanding many Earth's surface processes. However, lack regularly updated, continental-scale, high resolution (30 m) data limit our ability to understand extent changes. Despite free availability Landsat satellite data, continental-scale mapping using was not feasible until now due need high-performance computing store, process, analyze this large volume data. In study, we present an...
As malaria cases have declined throughout Nepal, imported comprise an increasing share of the remaining caseload, yet how to effectively target mobile and migrant populations (MMPs) at greatest risk is not well understood. This formative research aimed confirm link between indigenous cases, characterize high-risk MMPs, identify opportunities adapt surveillance intervention strategies them. The study used a mixed-methods approach in three districts far mid-western including (i) retrospective...
Having accurate maps depicting the locations of residential buildings across a region benefits range sectors. This is particularly true for public health programs focused on delivering services at household level, such as indoor residual spraying with insecticide to help prevent malaria. While open source data from OpenStreetMap (OSM) and shapes rapidly improving in terms quality completeness globally, even settings where all have been mapped, information whether these are residential,...
Abstract The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers opportunity to predict the locations using suitable environmental and climatological data, little attention has been paid optimizing design surveys used inform such models. Here we introduce adaptive sampling scheme optimized identify hotspot where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory...
Four malaria indicator surveys (MIS) were conducted in Zambia between 2006 and 2012 to evaluate control scale-up. Nationally, coverage of insecticide-treated nets (ITNs) indoor residual spraying (IRS) increased over this period, while parasite prevalence children 1–59 months decreased dramatically 2008, but then from 2008 2010. We assessed the relative effects vector climate variability on period. Nationally-representative MISs April-June 2006, 2010 collect household-level information...
Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding spatiotemporal dynamics urbanization across is important to a range fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by National Oceanic Atmospheric Administration, offers unique vantage point for studying trends in urbanization. well-documented deficiency this dataset lack intra-...
Summary The paper introduces new methods for inference with count data registered on a set of aggregation units. Such are omnipresent in epidemiology because confidentiality issues: it is much more common to know the county which an individual resides, say, than their exact location space. Inference aggregated has traditionally made use models discrete spatial variation, e.g. conditional auto-regressive models. We argue that such can be improved from both scientific and inferential...
Household electricity access data in Africa are scarce, particularly at the subnational level. We followed a model-based Geostatistics approach to produce maps of between 2000 and 2013 5 km resolution. collated from 69 nationally representative household surveys conducted incorporated nighttime lights imagery as well land use cover 2013. The information produced here can be an aid for understanding how has changed region during this 14 year period. resolution continental scale makes it...
Abstract The global elimination of lymphatic filariasis (LF) is a major focus the World Health Organization. One key challenge locating residual infections that can perpetuate transmission cycle. We show how targeted sampling strategy using predictions from geospatial model, combining random forests and geostatistics, improve efficiency for identifying locations with high infection prevalence. Predictions were made based on household infected persons identified previous surveys,...
Background Health Management Information Systems (HMIS) are a crucial tool for supporting planning and decision-making. The benefits of such systems will depend on the quality data they provide response capacity decision-makers [1]. analysis malaria incidence records HMIS, in Uganda, faces two main complications. First, artificial trends induced by non-negligible variable rate non-reporting hospitals. Second, lack comparability across time, due to changes districts boundaries.
Abstract The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers opportunity to predict the locations using suitable environmental and climatological data, little attention has been paid optimizing design surveys used inform such models. Here we introduce adaptive sampling scheme optimized identify hotspot where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory...
This article introduces new methods for inference with count data registered on a set of aggregation units. Such are omnipresent in epidemiology due to confidentiality issues: it is much more common know the county which an individual resides, say, than their exact location space. Inference aggregated has traditionally made use models discrete spatial variation, example conditional autoregressive (CAR). We argue that such can be improved from both scientific and inferential perspective by...
Improvements to Zambia's malaria surveillance system allow better monitoring of incidence and targetting responses at refined spatial scales. As transmission decreases, understanding heterogeneity in risk fine scales becomes increasingly important. However, there are challenges using health data for high-resolution mapping: facilities have undefined overlapping catchment areas, report on an inconsistent basis. We propose a novel inferential framework mapping based formal down-scaling...