Fractional diffusion emulates a human mobility network during a simulated disease outbreak

From footpaths to flight routes, human mobility networks facilitate the spread of communicable diseases. Control and elimination efforts depend on characterizing these networks in terms of connections and flux rates of individuals between contact nodes. In some cases, transport can be parameterized with gravity-type models or approximated by a diffusive random walk. As a alternative, we have isolated intranational commercial air traffic as a case study for the utility of non-diffusive, heavy-tailed transport models. We implemented new stochastic simulations of a prototypical influenza-like infection, focusing on the dense, highly-connected United States air travel network. We show that mobility on this network can be described mainly by a power law, in agreement with previous studies. Remarkably, we find that the global evolution of an outbreak on this network is accurately reproduced by a two-parameter space-fractional diffusion equation, such that those parameters are determined by the air travel network.Comment: 26 pages, 4 figure

A framework for exploration and cleaning of environmental data : Tehran air quality data experience

Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013. ; The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase.; The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR.  The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data.; We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km x 1 km spatial resolution across the four provinces. An out-of-bag R(2) of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R(2) of 0.48 and temporal CV R(2) of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data

An agent-based approach for modeling dynamics of contagious disease spread

Background: The propagation of communicable diseases through a population is an inherentspatial and temporal process of great importance for modern society. For this reason a spatiallyexplicit epidemiologic model of infectious disease is proposed for a greater understanding of thedisease\u27s spatial diffusion through a network of human contacts.Objective: The objective of this study is to develop an agent-based modelling approach theintegrates geographic information systems (GIS) to simulate the spread of a communicable diseasein an urban environment, as a result of individuals\u27 interactions in a geospatial context.Methods: The methodology for simulating spatiotemporal dynamics of communicable diseasepropagation is presented and the model is implemented using measles outbreak in an urbanenvironment as a case study. Individuals in a closed population are explicitly represented by agentsassociated to places where they interact with other agents. They are endowed with mobility,through a transportation network allowing them to move between places within the urbanenvironment, in order to represent the spatial heterogeneity and the complexity involved ininfectious diseases diffusion. The model is implemented on georeferenced land use dataset fromMetro Vancouver and makes use of census data sets from Statistics Canada for the municipality ofBurnaby, BC, Canada study site.Results: The results provide insights into the application of the model to calculate ratios ofsusceptible/infected in specific time frames and urban environments, due to its ability to depict thedisease progression based on individuals\u27 interactions. It is demonstrated that the dynamic spatialinteractions within the population lead to high numbers of exposed individuals who performstationary activities in areas after they have finished commuting. As a result, the sick individuals areconcentrated in geographical locations like schools and universities.Conclusion: The GIS-agent based model designed for this study can be easily customized to studythe disease spread dynamics of any other communicable disease by simply adjusting the modeleddisease timeline and/or the infection model and modifying the transmission process. This type ofsimulations can help to improve comprehension of disease spread dynamics and to take bettersteps towards the prevention and control of an epidemic outbreak

17-09 Assessing the Impact of Air Pollution on Public Health Along Transit Routes

Transportation sources account for a large proportion of the pollutants found in most urban areas. Also, transportation activity and intensity appear likely to contribute to the risk of respiratory disease occurrence. This research investigates the impacts of transportation, urban design and socioeconomic characteristics on the risk of air pollution-related respiratory diseases in two of the biggest MSAs (Metropolitan Statistical Areas) in the US, Dallas-Fort Worth (DFW) and Los Angeles at the block group (BG) level, by considering the US Environmental Protection Agency’s respiratory hazard quotient (RHQ) as the dependent variable. The researchers identify thirty candidate indicators of disease risk from previous studies and use them as independent variables in the model. The study applies a three-step modeling including Principal Component Analysis (PCA), Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) to reach the final model. The results of this study demonstrate strong spatial correlations in the variability in both MSAs which help explain the impact of the indicators such as socioeconomic characteristics, transit access to jobs, and automobile access on the risk of respiratory diseases. The populations living in areas with higher transit access to jobs in urbanized areas and greater automobile access in more rural areas appear more prone to respiratory diseases after controlling for demographic characteristics

Air pollution exposure assessment in sparsely monitored settings; applying machine-learning methods with remote sensing data in South Africa.

Air pollution is one of the leading environmental risk factors to human health – Both short and long-term exposure to air pollution impact human health accounting for over 4 million deaths. Although the risk of exposure to air pollution has been quantified in different settings and countries of the world. The majority of these studies are from high-income countries with historical air pollutant measurement data and corresponding health outcomes data to conduct such epidemiological studies. Air pollution exposure levels in these high-income settings are lower than the exposure levels in low-income countries. The exposure level in sub-Saharan Africa (SSA) countries has continued to increase due to rapid industrialization and urbanization. In addition, the underlying susceptibility profile of SSA population is different from the profiles of the population in high-income settings. However, a major limitation to conducting epidemiological studies to quantify the exposure-response relationship between air pollution and adverse health outcomes in SSA is the paucity of historical air pollution measurement data to inform such epidemiological studies. South Africa an SSA country with some air quality monitoring stations especially in areas classified as air pollution priority areas have historical particulate matter less than or equal to 10 micrometres in aerodynamic diameter (PM10 μg/m3) measurement data. PM10 is one of the most monitored criteria for air pollutants in South Africa. The availability of satellite-derived aerosol optical depth (AOD) at high spatial and temporal resolutions provides information about how particles in the atmosphere can prevent sunlight from reaching the ground. This satellite product has been used as a proxy variable to explain ground-level air pollution levels in different settings. This thesis main objective was to use satellite-derived AOD to bridge the gap in ground-monitored PM10 across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape). We collected PM10 ground monitor measurement data from the South Africa Weather Services across the four provinces for the years 2010 – 2017. Due to the gaps in the daily PM10 across the sites and years. In study I, we compared methods for imputing daily ground-level PM10 data at sites across the four provinces for the years 2010 – 2017 using random forest (RF) models. The reliability of air pollution exposure models depends on how well the models capture the spatial and temporal variation of air pollution. Thus, study II explored the spatial and temporal variations in ground monitor PM10 across the four provinces for the years 2010 – 2017. To explore the feasibility of using satellite-derived AOD and other spatial and temporal predictor variables, Study III used an ensemble machine-learning framework of RF, extreme gradient boosting (XGBoost) and support vector regression (SVR) to calibrate daily ground-level PM10 at 1 × 1 km spatial resolution across the four provinces for the year 2016. In conclusion, we developed a spatiotemporal model to predict daily PM10 concentrations across four provinces of South Africa at 1 × 1 km spatial resolution for 2016. This model is the first attempt to use a satellite-derived product to fill the gap in ground monitor air pollution data in SSA