Disease Prediction Using Crowdsourced Environmental Data
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Solution Overview
Problem
Current disease outbreak models, such as SEIR models, are unable to predict the initial onset of diseases and can only infer the spread of an existing disease, making them ineffective in preventing outbreaks before they occur.
Innovation Solution
A method utilizing crowdsourced reports of environmental conditions to infer input parameters for disease outbreak models, allowing for the prediction of potential outbreaks and implementation of corrective actions before they happen, involving the receipt and filtering of reports, correlation with historical data, and application to disease models to trigger warnings and mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SEIR disease outbreak models are used, then the spread of disease among population can be predicted with reasonable accuracy, but the initial onset of disease cannot be predicted
Solution Approach 1:
The system performs preliminary actions by collecting environmental data (temperature, humidity, rainfall) and analyzing it with machine learning models to predict disease outbreaks before they occur. This allows public health officials to take preventive measures in advance, such as deploying mosquito control teams or issuing public warnings, rather than waiting for case reports after outbreaks have started.
Solution Approach 2:
The patent introduces environmental factors (temperature, humidity, rainfall, vegetation) as intermediary variables that mediate between actual disease conditions and model predictions. These environmental parameters serve as proxies that can be measured and analyzed to infer disease risk before clinical cases appear, enabling earlier detection and prevention.
2Loss of information
If disease outbreak models rely on information regarding infected people and their location, then geographic spread can be inferred, but the models require disease onset to have already happened
Solution Approach 1:
The system implements feedback by continuously monitoring environmental conditions and comparing predicted outbreak risks with actual outbreak data. The machine learning models are trained on historical environmental data and outbreak records, creating a feedback loop where past outcomes improve future predictions. This allows the system to reliably predict outbreaks based on environmental patterns that precede disease onset.
Solution Approach 2:
The patent replaces the traditional mechanical system of disease surveillance (which waits for case reports and contact tracing) with an environmental monitoring system using sensors, satellite imagery, and machine learning algorithms. This substitution enables prediction based on environmental precursors rather than requiring actual disease cases to be reported first.
Data Source
AI summary
Embodiments of the invention provide techniques which utilize crowdsourced reports of environmental conditions to predict and/or prevent disease outbreaks. In one aspect, a method comprises receiving one or more crowdsourced reports about one or more environmental conditions; inferring one or more input parameters for at least one disease outbreak model based at least in part on the one or more crowdsourced reports; applying the at least one disease outbreak model to at least the one or more inferred parameters to predict one or more characteristics of at least one potential disease outbreak associated with the reported one or more environmental conditions; and, based at least in part on the predicted one or more characteristics, implementing one or more corrective actions to mitigate the at least one potential disease outbreak.