We are inspired by the current situation in which the whole planet finds itself. We understand that it would be a faster solution of containment if the government had the means to obtain information more quickly for decision making.We will cross-check positive epidemiological tests with socio-economic and climatic factors linked to artificial intelligence.We will provide a solution to improve the reliability of test-related data, by inserting IOT device exam collection machines, encrypting the data and protecting it using Blockchain technology, thus excluding any human interference and also solving the problem of data fraud.
We are inspired by the current situation in which the whole planet finds itself. We understand that it would be a faster solution of containment if the government had the means to obtain information more quickly for decision making.
Our activity plan involves the use of resources that we have identified as important, through research related to the proliferation of epidemiological diseases and which could accelerate the spread of the disease. To generate predictive scenarios for the spread of contagions for government decision making
We will analyze HDI factors of the regions together, with data from NASA on the following factors that we identified through standard researchers with epidemiological disease. They are: Population density, Air humidity and Temperature and results of examinations of infectious pathologies, received through an Open IoT API that connects machines and equipment of epidemiological tests to transmit properly encrypted on the blockchain network.
The project techonology stack: nodejs to build rest apis; python with pandas for data analysis; kubernetes as container orchestrator; docker for application containerization; Hyperledger Fabric as blockchain implementation; IBM cloud as cloud provider ( kubernetes services | blockchain as service ); The main challenging aspect was the nasa data handling from sensors ( satellites ), many datasources available provide this data as is, so the effort to "translate" this data into a suitable format and pattern seems to be the most challenging point. The second one was relative to time for smart contract implementation.
https://youtu.be/scZWsdzn-PQ
https://xd.adobe.com/view/5f8c49d0-eaca-4c1a-67b8-29e3558fd492-29bd/?fullscreen
Consumption of sentinel-hub apis (focused on temperature and humidity) -
https://www.sentinel-hub.com/develop/api/
(Through a backend application)
* Consumption of SEDAC data (aimed at population density)
SEDAC Data Collections (improved dataset)
https://earthdata.nasa.gov/learn/pathfinders/covid-19/environmental-impacts
* Data collected from the government of epedemiologicas diseases in database;
* Data analysis for the composition of a dataset that unifies the following data (through machine learning models):
* Presentation of the probability of regions vulnerable to contagion from COVID-19 through an interactive panel;
https://servicodados.ibge.gov.br/api/docs
https://www.infraestruturameioambiente.sp.gov.br/sistemas/