Geological hazards: from early warning systems to public health toolkits

An Empirical Evaluation of Disaster Data Interoperability—A Case of Uganda

One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate disaster data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish disaster data using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess disaster data interoperability in Uganda and identify generic solutions to interoperability challenges in the context of disaster data. Semi-structured interviews and focus group discussions were used to collect qualitative data from stakeholders in the disaster sector in Uganda. Data interoperability was measured to provide an understanding of interoperability in the disaster sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for disaster data. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder disaster data integration and reuse. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem.

The Potential Impact of Border Security Upon Prevalence of Infectious Disease

In the U.S., migration has been documented to affect the prevalence of infectious disease. As a mitigation entity, border security has been recorded by numerous scholarly works as being essential to the support of the health of the U.S. population. Consequently, the lack of current health care monitoring of the permeable U.S. border places the U.S. population at risk in the broad sectors of infectious disease and interpersonal violence. Visualizing border security in the context of public health mitigation has significant potential to protect migrant health as well as that of all populations on both sides of the border. Examples of how commonly this philosophy is held can be found in the expansive use of security-focused terms regarding public health. Using tools such as GIS to screen for disease in people before their entrance into a nation would be more efficient and ethical than treating patients once they have entered a population and increased the impact on the healthcare system. (Disaster Med Public Health Preparedness. 2018;12:554-562).

Predicting Posttraumatic Stress Symptom Prevalence and Local Distribution after an Earthquake with Scarce Data

BACKGROUND

After a major earthquake, the assignment of scarce mental health emergency personnel to different geographic areas is crucial to the effective management of the crisis. The scarce information that is available in the aftermath of a disaster may be valuable in helping predict where are the populations that are in most need.

OBJECTIVE

The objectives of this study were to derive algorithms to predict posttraumatic stress (PTS) symptom prevalence and local distribution after an earthquake and to test whether there are algorithms that require few input data and are still reasonably predictive.

METHODS

A rich database of PTS symptoms, informed after Chile's 2010 earthquake and tsunami, was used. Several model specifications for the mean and centiles of the distribution of PTS symptoms, together with posttraumatic stress disorder (PTSD) prevalence, were estimated via linear and quantile regressions. The models varied in the set of covariates included.

RESULTS

Adjusted R2 for the most liberal specifications (in terms of numbers of covariates included) ranged from 0.62 to 0.74, depending on the outcome. When only including peak ground acceleration (PGA), poverty rate, and household damage in linear and quadratic form, predictive capacity was still good (adjusted R2 from 0.59 to 0.67 were obtained).

CONCLUSIONS

Information about local poverty, household damage, and PGA can be used as an aid to predict PTS symptom prevalence and local distribution after an earthquake. This can be of help to improve the assignment of mental health personnel to the affected localities. Dussaillant F , Apablaza M . Predicting posttraumatic stress symptom prevalence and local distribution after an earthquake with scarce data. Prehosp Disaster Med. 2017;32(4):357-367.

Development of a browser application to foster research on linking climate and health datasets: Challenges and opportunities

BACKGROUND

Improved data linkages between diverse environment and health datasets have the potential to provide new insights into the health impacts of environmental exposures, including complex climate change processes. Initiatives that link and explore big data in the environment and health arenas are now being established.

OBJECTIVES

To encourage advances in this nascent field, this article documents the development of a web browser application to facilitate such future research, the challenges encountered to date, and how they were addressed.

METHODS

A 'storyboard approach' was used to aid the initial design and development of the application. The application followed a 3-tier architecture: a spatial database server for storing and querying data, server-side code for processing and running models, and client-side browser code for user interaction and for displaying data and results. The browser was validated by reproducing previously published results from a regression analysis of time-series datasets of daily mortality, air pollution and temperature in London.

RESULTS

Data visualisation and analysis options of the application are presented. The main factors that shaped the development of the browser were: accessibility, open-source software, flexibility, efficiency, user-friendliness, licensing restrictions and data confidentiality, visualisation limitations, cost-effectiveness, and sustainability.

CONCLUSIONS

Creating dedicated data and analysis resources, such as the one described here, will become an increasingly vital step in improving understanding of the complex interconnections between the environment and human health and wellbeing, whilst still ensuring appropriate confidentiality safeguards. The issues raised in this paper can inform the future development of similar tools by other researchers working in this field.