Wilkinssuhr6092

Air travel is an increasingly important conduit for the worldwide spread of infectious diseases. However, methods to identify which airports an individual may use to initiate travel, or where an individual may travel to upon arrival at an airport is not well studied. This knowledge gap can be addressed by estimating airport catchment areas the geographic extent from which the airport derives most of its patronage. While airport catchment areas can provide a simple decision-support tool to help delineate the spatial extent of infectious disease spread at a local scale, observed data for airport catchment areas are rarely made publicly available. Therefore, we evaluated a probabilistic choice behavior model, the Huff model, as a potential methodology to estimate airport catchment areas in the United States in data-limited scenarios. We explored the impact of varying input parameters to the Huff model on estimated airport catchment areas distance decay exponent, distance cut-off, and measures of airport attracti model to estimate airport catchment areas as a generalizable decision-support tool in data-limited scenarios. While our work represents an initial examination of the Huff model as a method to approximate airport catchment areas, an essential next step is to conduct a quantitative calibration and validation of the model based on multiple airports, possibly leveraging local human mobility data such as call detail records or online social network data collected from mobile devices. Ultimately, we demonstrate how the Huff model could be potentially helpful to improve the precision of early warning systems that anticipate infectious disease spread, or to incorporate when local public health decision makers need to identify where to mobilize screening infrastructure or containment strategies at a local level.Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. FKBP chemical We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

The risk of anemia in Nigeria is of public health importance, with an increasing number of women of reproductive age being anemic. This study sought to identify the spatial distribution and examine the geographical variation of anemia risk at a regional level while accounting for risk factors associated with anemia among women of childbearing age in Nigeria. The significant interest in spatial statistics lies in identifying associated risk factors that enhance the risk of infection. However, most studies make no or limited use of the data's spatial structure and possible non-linear effects of the risk factors.

The data used in this study were extracted from the 2015 Nigeria Demographic and Health Survey (NDHS). A full Bayesian semi-parametric regression model was fitted to data to accomplish the aims of the study. Model estimation and the inference was fully Bayesian approach via integrated nested Laplace approximations (INLA). The fixed effects were modeled parametrically; non-linear effects were modeledally focus on these regions and subsequently spread across Nigeria.

The study revealed associations between anemia risk and women residing in rural settlements, wealth index, women with no formal education, and unprotected drinking water sources. Community and household-related change interventions should, therefore, be pertinent to the prevention of anemia. The spatial analysis further revealed a significant anemia risk towards the Northern areas of Nigeria. We propose that interventions targeting women of reproductive age should initially focus on these regions and subsequently spread across Nigeria.The most common approach to create spatial prediction of malaria in the literature is to approximate a Gaussian process model using stochastic partial differential equation (SPDE). We compared SPDE to computationally faster alternatives, generalized additive model (GAM) and state-of-the-art machine learning method gradient boosted trees (GBM), with respect to their predictive skill for country-level malaria prevalence mapping. We also evaluated the intuition that incorporation of past data and the use of spatio-temporal models may improve predictive accuracy of present spatial distribution of malaria. Model performances varied among the countries and setting with SPDE and GAM performed well generally. The inclusion of past data is beneficial for GAM and GBM, but not for SPDE. We further investigated the weaknesses of SPDE at spatio-temporal setting and GAM at the edges of the countries. Taken together, we believe that spatial/spatio-temporal SPDE models should be evaluated alongside with the alternatives or at least GAM.In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India.