Research Topic

Advances in Spatio-Temporal Modelling of Waterborne and Vector-borne Diseases Based on Hydrometeorological and Socio-environmental Factors

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About this Research Topic

Waterborne and vector-borne diseases such as Cholera, Hepatitis A, Hepatitis E, Typhoid, Leptospirosis, Schistosomiasis, Malaria and Dengue continue to occur worldwide, especially in underdeveloped and developing countries and causing millions of deaths yearly. Their transmission is strongly driven by hydro-meteorological factors (e.g. flooding), poor water quality and hygiene practices, as well as environmental exposure (household, recreational and occupational). The uptake of environmental informatics in public health decision-making has been limited and may be attributed to insufficient mechanistic understanding and deficiency of robust models for early warning and risk assessment.

In this Research Topic, we aim to gather current findings in a rapidly emerging research area at the intersect of environmental and earth sciences, epidemiology, and data science. On one hand, traditional epidemiological research rely on observational studies i.e. case-control, cohort, and cross-sectional, using methods such as statistical regression and Bayesian inference to infer causality. On the other hand, environmental and earth system research is rooted in the study of natural physics, chemistry, and microbiology and representation of these processes in the form of mathematical models; often, there is emphasis on the space-time dimensions and reanalysis of historical time series in the prediction of the future or other alternative scenarios.

This attention to spatial and temporal variability is advantageous for pushing the boundary of epidemic predictability, since history suggests a link between hydrologic variability and disease outbreaks. High incidence rates of Cholera for example, have been attributed to direct use of river water during periods of water scarcity during extreme dry weather. Large outbreaks of Cholera, Hepatitis A, Typhoid, Leptospirosis and Malaria, have also occured after massive flooding; furthermore, due consideration is required for lag effects that range from days to months.

The increased power of computing, Internet connectivity and availability of multiple data sources especially earth observations from remote sensing allows an unprecedented amount of information - temporal, spatial, or both – to be harnessed by advanced computing and analytical software. Yet applications of this in epidemiology is in infancy. Significant challenge remains in collating, curating, and integrating these multitude of information types, including socio-demographic, to generate analytical and prediction models with high precision and accuracy.

In light of this, we are welcoming contributions spanning all geographical regions on:
• New methodological development and empirical findings on the environmental and hydrometeorological drivers of disease outbreaks using data mining methods. Studies should emphasise the spatial and/or temporal variability in the drivers and how the use of informatics enables knowledge extraction;
• Spatial, temporal, or spatiotemporal machine learning models of disease outbreaks, whereby environmental and earth observations from ground based sensors and/or remote sensing are requisite model inputs;
• Emulators, where complex (e.g. physically based) models are calibrated using less accurate but much faster algorithms;
• Mapping and zoning, where novel GIS and remote sensing techniques are used to identify hydro-meteorological conditions associated with disease hotspots;
• Use of Internet of Things, low cost sensors, cloud-based computing or/and citizen involvement to monitor hydro-meteorological conditions in disease prone areas;
• Use of web technologies for sharing and transferring hydro-meteorological information among stakeholders in disease prone areas; and
• Data fusion techniques to integrate multiple heterogeneous information (health, hydro, meteo, etc) into cohesive data sources.

Epidemiological studies related to analysis and modelling of diseases based on clinical symptoms and lab diagnosis do not fall under the scope of the Research Topic.

Photo credits: MD Duran


Keywords: waterborne diseases, vector-borne diseases, statistical analysis, data mining, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Waterborne and vector-borne diseases such as Cholera, Hepatitis A, Hepatitis E, Typhoid, Leptospirosis, Schistosomiasis, Malaria and Dengue continue to occur worldwide, especially in underdeveloped and developing countries and causing millions of deaths yearly. Their transmission is strongly driven by hydro-meteorological factors (e.g. flooding), poor water quality and hygiene practices, as well as environmental exposure (household, recreational and occupational). The uptake of environmental informatics in public health decision-making has been limited and may be attributed to insufficient mechanistic understanding and deficiency of robust models for early warning and risk assessment.

In this Research Topic, we aim to gather current findings in a rapidly emerging research area at the intersect of environmental and earth sciences, epidemiology, and data science. On one hand, traditional epidemiological research rely on observational studies i.e. case-control, cohort, and cross-sectional, using methods such as statistical regression and Bayesian inference to infer causality. On the other hand, environmental and earth system research is rooted in the study of natural physics, chemistry, and microbiology and representation of these processes in the form of mathematical models; often, there is emphasis on the space-time dimensions and reanalysis of historical time series in the prediction of the future or other alternative scenarios.

This attention to spatial and temporal variability is advantageous for pushing the boundary of epidemic predictability, since history suggests a link between hydrologic variability and disease outbreaks. High incidence rates of Cholera for example, have been attributed to direct use of river water during periods of water scarcity during extreme dry weather. Large outbreaks of Cholera, Hepatitis A, Typhoid, Leptospirosis and Malaria, have also occured after massive flooding; furthermore, due consideration is required for lag effects that range from days to months.

The increased power of computing, Internet connectivity and availability of multiple data sources especially earth observations from remote sensing allows an unprecedented amount of information - temporal, spatial, or both – to be harnessed by advanced computing and analytical software. Yet applications of this in epidemiology is in infancy. Significant challenge remains in collating, curating, and integrating these multitude of information types, including socio-demographic, to generate analytical and prediction models with high precision and accuracy.

In light of this, we are welcoming contributions spanning all geographical regions on:
• New methodological development and empirical findings on the environmental and hydrometeorological drivers of disease outbreaks using data mining methods. Studies should emphasise the spatial and/or temporal variability in the drivers and how the use of informatics enables knowledge extraction;
• Spatial, temporal, or spatiotemporal machine learning models of disease outbreaks, whereby environmental and earth observations from ground based sensors and/or remote sensing are requisite model inputs;
• Emulators, where complex (e.g. physically based) models are calibrated using less accurate but much faster algorithms;
• Mapping and zoning, where novel GIS and remote sensing techniques are used to identify hydro-meteorological conditions associated with disease hotspots;
• Use of Internet of Things, low cost sensors, cloud-based computing or/and citizen involvement to monitor hydro-meteorological conditions in disease prone areas;
• Use of web technologies for sharing and transferring hydro-meteorological information among stakeholders in disease prone areas; and
• Data fusion techniques to integrate multiple heterogeneous information (health, hydro, meteo, etc) into cohesive data sources.

Epidemiological studies related to analysis and modelling of diseases based on clinical symptoms and lab diagnosis do not fall under the scope of the Research Topic.

Photo credits: MD Duran


Keywords: waterborne diseases, vector-borne diseases, statistical analysis, data mining, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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