Dataset Information

Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework.

ABSTRACT: Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

SUBMITTER: Jia Q

PROVIDER: S-EPMC7503476 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework.

Jia Qiong Q Guo Yue Y Wang Guanlin G Barnes Stuart J SJ

International journal of environmental research and public health 20200825 17

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic preven ...[more]

PMID: 32854265

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Project description:BackgroundNumerous determinants have been linked to public mental health; however, they have not been brought together in a comprehensive conceptual framework. The goal of this work was to bring together academic research, practitioner expertise, and public perspectives to create a public mental health conceptual framework.MethodsThe development process proceeded in four stages. First, we identified a comprehensive list of potential determinants through a state-of-the-art academic literature review, grey literature review, and created mind maps created by peer researchers. Next, we conducted in-person workshops, consultations, and an online survey with academics, practitioners, policy makers, and members of the public to review the potential determinants, nominate additional determinants, and prioritise determinants by importance for understanding public mental health. This iterative process resulted in the final list of determinants contained in the framework. We then conducted rapid reviews to define each determinant and to identify key research, interventions, and resources. Finally, we worked with a design team to visualise the conceptual framework as an online tool and printable infographic.ResultsWe found substantial overlap between sources reflecting a shared understanding of the key drivers of public mental health. The unique determinants that emerged from each data source highlighted the importance of using multiple sources to create a comprehensive model. 72 potential determinants were prioritised through stakeholder consultations, resulting in a final list of 55 determinants and organised into four levels: individual, family, community, and structural.ConclusionsThis is the most complete conceptual framework for public mental health to date, bringing together academic research, policy and practitioner views, and lived experience perspectives. The co-production processes and tools we used provides a template for researchers looking to include multiple perspectives in their research. The conceptual framework draws together current knowledge on each determinant, but also highlights areas where further research is needed to better understand the relationship between each factor and mental health, which can inform the research agenda. This online tool and infographic can be used by practitioners to identify interventions for promoting mental health, and by the general public as a resource to increase awareness of the broad factors which shape public mental health.

Project description:Background Failing to account for the resources required to successfully implement public health interventions can lead to an underestimation of costs and budget impact, optimistic cost-effectiveness estimates, and ultimately a disconnect between published evidence and public health decision-making. Methods We developed a conceptual framework for assessing implementation costs. We illustrate the use of this framework with case studies involving interventions for tuberculosis and HIV/AIDS in resource-limited settings. Results Costs of implementing public health interventions may be conceptualized as occurring across three phases: design, initiation, and maintenance. In the design phase, activities include developing intervention components and establishing necessary infrastructure (e.g., technology, standard operating procedures). Initiation phase activities include training, initiation of supply chains and quality assurance procedures, and installation of equipment. Implementation costs in the maintenance phase include ongoing technical support, monitoring and evaluation, and troubleshooting unexpected obstacles. Within each phase, implementation costs can be incurred at the site of delivery (“site-specific” costs) or more centrally (“above-service” or “central” costs). For interventions evaluated in the context of research studies, implementation costs should be classified as programmatic, research-related, or shared research/program costs. Purely research-related costs are often excluded from analysis of programmatic implementation. Conclusions In evaluating public health interventions in resource-limited settings, accounting for implementation costs enables more realistic estimates of budget impact and cost-effectiveness and provides important insights into program feasibility, scale-up, and sustainability. Assessment of implementation costs should be planned prospectively and performed in a standardized manner to ensure generalizability.

Dataset Information

Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework.

Publications

Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework.

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