Amazon Biobank: a collaborative genetic database for bioeconomy development.

Biodiversity is proposed as a sustainable alternative for the economic development of high-biodiversity regions. Especially in the field of biodiversity genomics, the development of low-cost DNA sequencing opens an opportunity for new actors beyond academia to engage in genomic sequencing. However, it is challenging to adequately compensate non-academic actors such as local populations for their contribution to the innovation process, preventing better bioeconomy development. Although many repositories register genomic data to support biodiversity research, they do not facilitate the fair sharing of economic benefits. In this work, we propose the creation of the Amazon Biobank, a community-based genetic database. We employed blockchain to build a transparent and verifiable log of transactions involving genomic data, and we used smart contracts to implement an internal monetary system for all participants who collect, insert, process, store, and validate genomic data. We also used peer-to-peer solutions to allow users with commodity computers to collaborate with the storage and distribution of DNA files. By combining emerging technologies, Amazon Biobank provides adequate benefit-sharing among all participants that collaborate with data, knowledge, and computational resources. It also provides traceability and auditability, allowing easy association between biotechnological research and DNA data. In addition, the solution is highly scalable and less dependent on the trust deposited in any system player. Therefore, Amazon Biobank can become an important stepping stone to unlock the potential of bioeconomy in rich ecosystems such as the Amazon Rainforest.

Georeferenced and secure mobile health system for large scale data collection in primary care.

INTRODUCTION: Mobile health consists in applying mobile devices and communication capabilities for expanding the coverage and improving the effectiveness of health care programs. The technology is particularly promising for developing countries, in which health authorities can take advantage of the flourishing mobile market to provide adequate health care to underprivileged communities, especially primary care. In Brazil, the Primary Care Information System (SIAB) receives primary health care data from all regions of the country, creating a rich database for health-related action planning. Family Health Teams (FHTs) collect this data in periodic visits to families enrolled in governmental programs, following an acquisition procedure that involves filling in paper forms. This procedure compromises the quality of the data provided to health care authorities and slows down the decision-making process. OBJECTIVES: To develop a mobile system (GeoHealth) that should address and overcome the aforementioned problems and deploy the proposed solution in a wide underprivileged metropolitan area of a major city in Brazil. METHODS: The proposed solution comprises three main components: (a) an Application Server, with a database containing family health conditions; and two clients, (b) a Web Browser running visualization tools for management tasks, and (c) a data-gathering device (smartphone) to register and to georeference the family health data. A data security framework was designed to ensure the security of data, which was stored locally and transmitted over public networks. RESULTS: The system was successfully deployed at six primary care units in the city of Sao Paulo, where a total of 28,324 families/96,061 inhabitants are regularly followed up by government health policies. The health conditions observed from the population covered were: diabetes in 3.40%, hypertension (age >40) in 23.87% and tuberculosis in 0.06%. This estimated prevalence has enabled FHTs to set clinical appointments proactively, with the aim of confirming or detecting cases of non-communicable diseases more efficiently, based on real-time information. CONCLUSION: The proposed system has the potential to improve the efficiency of primary care data collection and analysis. In terms of direct costs, it can be considered a low-cost solution, with an estimated additional monthly cost of U$ 0.040 per inhabitant of the region covered, or approximately U$ 0.106 per person, considering only those currently enrolled in the system.

SecourHealth: a delay-tolerant security framework for mobile health data collection.

Security is one of the most imperative requirements for the success of systems that deal with highly sensitive data, such as medical information. However, many existing mobile health solutions focused on collecting patients' data at their homes that do not include security among their main requirements. Aiming to tackle this issue, this paper presents SecourHealth, a lightweight security framework focused on highly sensitive data collection applications. SecourHealth provides many security services for both stored and in-transit data, displaying interesting features such as tolerance to lack of connectivity (a common issue when promoting health in remote locations) and the ability to protect data even if the device is lost/stolen or shared by different data collection agents. Together with the system's description and analysis, we also show how SecourHealth can be integrated into a real data collection solution currently deployed in the city of Sao Paulo, Brazil.