Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesIn Ethiopia, still, malaria is killing and affecting a lot of people of any age group somewhere in the country at any time. However, due to limited research, little is known about the spatial patterns and correlated risk factors on the wards scale. In this research, we explored spatial patterns and evaluated related potential environmental risk factors in the distribution of malaria cases in Ethiopia in 2015 and 2016. Hot Spot Analysis (Getis-Ord Gi* statistic) was used to assess the clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semiparametric geographically weighted regression (s-GWR) models were compared to describe the spatial association of potential environmental risk factors with malaria cases. Our results revealed a heterogeneous and highly clustered distribution of malaria cases in Ethiopia during the study period. The s-GWR model best explained the spatial correlation of potential risk factors with malaria cases and was used to produce predictive maps. The GWR model revealed that the relationship between malaria cases and elevation, temperature, precipitation, relative humidity, and normalized difference vegetation index (NDVI) varied significantly among the wards. During the study period, the s-GWR model provided a similar conclusion, except in the case of NDVI in 2015, and elevation and temperature in 2016, which were found to have a global relationship with malaria cases. Hence, precipitation and relative humidity exhibited a varying relationship with malaria cases among the wards in both years. This finding could be used in the formulation and execution of evidence-based malaria control and management program to allocate scare resources locally at the wards level. Moreover, these study results provide a scientific basis for malaria researchers in the country

Spatiotemporal cluster patterns of hand, foot, and mouth disease at the county level in Mainland China, 2008-2012

Background: Hand, foot, and mouth disease (HFMD) is known to be a highly contagious childhood illness. In recent years, the number of reported cases of HFMD has significantly increased in mainland China. This study aims at the epidemiological features, spatiotemporal patterns of HMFD at the county/district level in mainland China. Methods: Data on reported HFMD cases for each county from 1 January 2008 to 31 December 2012 were obtained from the Chinese Center for Disease Control and Prevention. Cluster analysis, spatial autocorrelation, and retrospective scan methods were used to explore the spatiotemporal patterns of the disease. Results: The annual incidences varied greatly among the counties, ranging from 0 to 74.31‰with the median of 5.42‰ (interquartile range: 1.54‰–13.55‰) during 2008–2012 in mainland China. Counties close to provincial capital cities generally had higher incidences than rural counties. A seasonal distribution was observed between the northern and southern China, of which dual epidemic were shown in southern China and usually only one in northern China. Based on the global and local spatial autocorrelation analysis, we found that the spatial distribution of HFMD was presented a significant clustering pattern for each year (P \u3c 0.001), and hotspots of the disease were mostly distributed in coastal provinces of China. The retrospective scan statistic further identified the dynamics of spatiotemporal clustering areas of the disease, which were mainly distributed in the counties of eastern and southern China, as well as provincial capitals and their surrounding counties. Conclusions: The spatiotemporal clustering areas of the disease identified in this way were relatively stable, and imminent public health planning and resource allocation should be focused within those areas

Spatiotemporal cluster patterns of hand, Foot, and mouth disease at the county level in Mainland China, 2008-2012

Background: Hand, foot, and mouth disease (HFMD) is known to be a highly contagious childhood illness. In recent years, the number of reported cases of HFMD has significantly increased in mainland China. This study aims at the epidemiological features, spatiotemporal patterns of HMFD at the county/district level in mainland China. Methods: Data on reported HFMD cases for each county from 1 January 2008 to 31 December 2012 were obtained from the Chinese Center for Disease Control and Prevention. Cluster analysis, spatial autocorrelation, and retrospective scan methods were used to explore the spatiotemporal patterns of the disease. Results: The annual incidences varied greatly among the counties, ranging from 0 to 74.31‰with the median of 5.42‰ (interquartile range: 1.54‰–13.55‰) during 2008–2012 in mainland China. Counties close to provincial capital cities generally had higher incidences than rural counties. A seasonal distribution was observed between the northern and southern China, of which dual epidemic were shown in southern China and usually only one in northern China. Based on the global and local spatial autocorrelation analysis, we found that the spatial distribution of HFMD was presented a significant clustering pattern for each year (P Conclusions: The spatiotemporal clustering areas of the disease identified in this way were relatively stable, and imminent public health planning and resource allocation should be focused within those areas

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

This is the final version. Available from The Royal Society via the DOI in this record.Data extracted from the studies included in this systematic review are available from https://github.com/sophie-a-lee/mbd_connectivity_review and archived in a permanent repository on Zenodo (http://doi.org/10.5281/zenodo.4706866).Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.Engineering and Physical Sciences Research Council (EPSRC)The Royal Societ

Spatial epidemiology of a highly transmissible disease in urban neighbourhoods: Using COVID-19 outbreaks in Toronto as a case study

The emergence of infectious diseases in an urban area involves a complex interaction between the socioecological processes in the neighbourhood and urbanization. As a result, such an urban environment can be the incubator of new epidemics and spread diseases more rapidly in densely populated areas than elsewhere. Most recently, the Coronavirus-19 (COVID-19) pandemic has brought unprecedented challenges around the world. Toronto, the capital city of Ontario, Canada, has been severely impacted by COVID-19. Understanding the spatiotemporal patterns and the key drivers of such patterns is imperative for designing and implementing an effective public health program to control the spread of the pandemic. This dissertation was designed to contribute to the global research effort on the COVID-19 pandemic by conducting spatial epidemiological studies to enhance our understanding of the disease's epidemiology in a spatial context to guide enhancing the public health strategies in controlling the disease. Comprised of three original research manuscripts, this dissertation focuses on the spatial epidemiology of COVID-19 at a neighbourhood scale in Toronto. Each manuscript makes scientific contributions and enhances our knowledge of how interactions between different socioecological processes in the neighbourhood and urbanization can influence spatial spread and patterns of COVID-19 in Toronto with the application of novel and advanced methodological approaches. The findings of the outcomes of the analyses are intended to contribute to the public health policy that informs neighbourhood-based disease intervention initiatives by the public health authorities, local government, and policymakers. The first manuscript analyzes the globally and locally variable socioeconomic drivers of COVID-19 incidence and examines how these relationships vary across different neighbourhoods. In the global model, lower levels of education and the percentage of immigrants were found to have a positive association with increased risk for COVID-19. This study provides the methodological framework for identifying the local variations in the association between risk for COVID-19 and socioeconomic factors in an urban environment by applying a local multiscale geographically weighted regression (MGWR) modelling approach. The MGWR model is an improvement over the methods used in earlier studies of COVID-19 in identifying local variations of COVID-19 by incorporating a correction factor for the multiple testing problem in the geographically weighted regression models. The second manuscript quantifies the associations between COVID-19 cases and urban socioeconomic and land surface temperature (LST) at the neighbourhood scale in Toronto. Four spatiotemporal Bayesian hierarchical models with spatial, temporal, and varying space-time interaction terms are compared. The results of this study identified the seasonal trends of COVID-19 risk, where the spatiotemporal trends show increasing, decreasing, or stable patterns, and identified area-specific spatial risk for targeted interventions. Educational level and high land surface temperature are shown to have a positive association with the risk for COVID-19. In this study, high spatial and temporal resolution satellite images were used to extract LST, and atmospheric corrections methods were applied to these images by adopting a land surface emissivity (LSE) model, which provided a high estimation accuracy. The methodological approach of this work will help researchers understand how to acquire long time-series data of LST at a spatial scale from satellite images, develop methodological approaches for atmospheric correction and create the environmental data with a high estimation accuracy to fit into modelling disease. Applying to policy, the findings of this study can inform the design and implementation of urban planning strategies and programs to control disease risks. The third manuscript developed a novel approach for visualization of the spread of infectious disease outbreaks by incorporating neighbourhood networks and the time-series data of the disease at the neighbourhood level. The findings of the model provide an understanding of the direction and magnitude of spatial risk for the outbreak and guide for the importance of early intervention in order to stop the spread of the outbreak. The manuscript also identified hotspots using incidence rate and disease persistence, the findings of which may inform public health planners to develop priority-based intervention plans in a resource constraint situation

GIS and Health: Enhancing Disease Surveillance and Intervention through Spatial Epidemiology

The success of an evidence-based intervention depends on precise and accurate evaluation of available data and information. Here, the use of robust methods for evidence evaluation is important. Epidemiology, in its conventional form, relies on statistics and mathematics to draw inferences on disease dynamics in affected populations. Interestingly, most of the data used tend to have spatial aspects to them. However, most of these statistical and mathematical methods tend to either neglect these spatial aspects or consider them as artefacts, thereby biasing the resultant estimates. Thankfully, spatial methods allow for evidence evaluation and prediction in epidemiologic data while considering their inherent spatial characteristics. This, thus, promises more precise and accurate estimates.This thesis documents and illustrates the contribution spatial methods and spatial thinking makes to epidemiology through studies carried out in two countries with different heath-data quality realities, Uganda and Sweden. To be able to use spatial methods for epidemiology studies, proper spatial data need to be available, which is not the case in Uganda. Consequently, this study had two main aims: (1) It proposed and implemented a novel way of spatially-enabling patient registry systems in settings where the existing infrastructures do not allow for the collection of patient-level spatial details, prerequisites for fine-scale spatial analyses; (2) Where spatial data were available, spatial methods were used to study associative relationships between health outcomes and exposure factors. Spatial econometrics approaches, especially spatially autoregressive regression models were adopted. Also, consistent with location-specific epidemiologic intervention, the advantages of using spatial scan statistics, Geographically Weighted (Poisson) Regression and local entropy maps to distil model parameter estimates into their inherent spatial heterogeneities were illustrated. Our results illustrated that through the use of mobile and web technologies and leveraging on existing spatial data pools, systems that enable recording and storage of geospatially referenced patient records can be created. Also, spatial methods outperformed conventional statistical approaches, giving refined and more accurate parameter estimates. Finally, our study illustrates that the use of local spatial methods can inform policy and intervention better through the identification of areas with elevated disease burden or those areas worth additional scrutiny as illustrated by our study of HIV-TB coinfection areas in Uganda, the areas with high CVD-air pollution associations in Sweden, and areas with consistently high joint mortality burden for CVD and cancer among the Swedish elderly.Overall, the incorporation of spatial approaches and spatial thinking in epidemiology cannot be overemphasized. First, by enabling the capture of fine-scale personal-level spatial data, our study promises more robust analyses and seamless data integration. Secondly, associative analyses using spatial methods showed improved results. Thirdly, identification of the areas with elevated disease burden makes identifying the primary drivers of the observed local patterns more informed and focused. Ultimately, our results inform healthcare policy and strategic intervention as the most affected areas can easily be zoned out. Therefore, by illustrating these benefits, this study contributes to epidemiology, through spatial methods, especially in the aspects of disease surveillance, informing policy, and driving possible effective intervention

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

Additional file 6: Annex 1. Right side: Autocorrelation (ACF) and partial autocorrelation (PACF) functions of the residuals from ARIMA model (1, 0, 1) × (1, 0, 1)12 on log-transformed, differenced data. Left side: ACF and PACF of the residuals from ARIMA model (4, 0, 1) × (1, 0, 1)12 on log-transformed, differenced data

Segment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data

Road infrastructure is important to the well-being and economic health of all nations. The performance of road pavement infrastructure is sophisticated and affected by numerous factors and varies greatly across different roads. Large scale spatial analysis for assessing road infrastructure performance is increasingly required for road management, therefore multi-source factors, including satellite remotely sensed climate and environmental data, and ground-monitored vehicles observations, are collected as explanatory variables. Different from the traditional point or area based geospatial attributes, the performance of pavement infrastructure is the line segment based spatial data. Thus, a segment-based spatial stratified heterogeneitymethod is utilized to explore the comprehensive impacts of vehicles, climate, properties of road and socioeconomic conditions on pavement infrastructure performance. Segment-based optimal discretization is applied on discretizing segment-based pavement data, and a segment-based geographical detector is utilized to assess the spatial impacts of variables and their interactions. Results show that the segment-based methods can more reasonably and accurately describe the characteristics of line segment based spatial data and assess the spatial associations. The two major categories of factors associated with pavement damage are the variables of traffic vehicles and heavy vehicles in particular, and climate and environmental conditions. Meanwhile, the interactions between the explanatory variables in these two categories have much more influence than the single explanatory variables, and the interactions can explain more than half of the pavement damage. This study highlights the great potential of remote sensing based large scale spatial analysis of road infrastructures. The approach in this study provides new ideas for spatial analysis for segmented geographical data. The findings indicate that the quantified comprehensive impacts of variables are practical for wise decision-making for road design, construction and maintenance

A Water Poverty Analysis of the Niger Basin, West Africa

This report documents the findings of Work Package 1 for the Niger Basin Focal Project: A water poverty analysis. Research outputs presented here include maps of high poverty incidence and water related vulnerability. The statistical analysis revealed considerable spatial variation in water related poverty in addition to significant differences in the intra and inter-national factors associated with poverty. Poverty was measured as levels of child mortality, child morbidity and an asset wealth index to improve sensitivity in a primarily subsistence economy. Whilst the absolute quantity of water resources was important in some areas, the social and economic capacity to use and access this water was often more important