Case Study | CAM: Modeling the Spread of Covid-19 Infection
Regional Government of Madrid
The Government of the Community of Madrid is the body in charge of directing the regional policy. It is responsible for the executive and administrative functions, as well as the exercise of regulatory authority in matters within its competences. Each community has a health department responsible for key areas such as healthcare planning, public health, and management of health services that have the primary aim of ensuring proximity of services for users.
In the current early phase of the COVID-19 outbreak, Madrid needed to prepare to face the escalation of cases, with a particular focus on the readiness of healthcare services. Containing and mitigating the spread and infection rate of the virus has been the first priority of Madrid public health authorities to distribute the number of infections over time and, if possible, reduce the incidence of the disease it causes.
A Holistic View of the Evolution of Pandemic Data in Spain.
Although new data on COVID-19 are available daily, information about the biological and epidemiological characteristics of COVID-19 remain limited, and uncertainty remains around nearly all parameter values.
For example, estimates of case fatality ratios must account for numerous biases, including high numbers of asymptomatic cases, under-reporting of symptomatic cases, under-reporting of COVID-19 associated deaths, and the delay between case reporting and death reporting. There is also likely regional variability in testing practices, reported incidence.
The machine learning perspective on modeling infectious disease spread involves consideration of these large number of modeling parameters detailing the spread of and recovery from the disease and additional different compartments corresponding to demographic and social factors.
Solution on AWS
Accurate forecasting of the number of COVID-19 cases is becoming the backbone to facilitate the use of the available resources in hospitals and improve management strategies to optimally manage infected patients. And for that purpose, Keepler has developed an interactive dashboard in Quicksight, since a data-driven approach is crucial to understand the evolution of the pandemic and the use of machine learning models is essential to develop regional-scale models for forecasting and assessing the course of the pandemic.
Machine learning models also can help evaluate the potential effects of different community mitigation strategies (e.g., social distancing) and simulated the future trajectory of the epidemic under different scenarios and project the impact and the implications for healthcare capacity and policy interventions.
The solution follows the AWS Well-Architected Framework best practices. A serverless solution has been implemented in order to optimize costs and unnecessary infrastructure maintenance.
AWS services used were as follows
AWS S3: store the information.
AWS CloudWatch: trigger ETL, prediction execution and logs.
AWS Batch: preprocessing and cleaning data.
AWS Glue: construct Data Catalog.
AWS Athena: query SQL to analyze data in S3.
AWS QuickSight: build visualizations.
AWS SageMaker: create and deploy machine learning models.
Benefits for the client
Holistic View
A holistic view of the evolution data of the pandemic and the different incidence of the virus among spanish regions.
Interactive Dashboard
An interactive dashboard to visualize and better understand the key factors of the pandemic and its implications.
SageMaker Model Monitoring
SageMaker model monitoring as a early warning system to detect changes in data distributions as forecasts are strongly influenced by the reliability of the data.
Operational Cost Reduction
Solution fully implemented through managed services with an operating cost is reduced.
Keepler is a full-stack analytics services company specialized in the design, construction, deployment and operation of advanced public cloud analytics custom-made solutions. We bring to the market the Data Product concept, which is a fully automated, public cloud services-based, tailored software that adds advanced analytics, data engineering, massive data processing, and monitoring features. In addition, we help our customers transition to using public cloud services securely and improve data governance to make the organization more data-centric.
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