Mobile Arthropod Tracking App with Real-Time Risk Assessment
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Solution Overview
Problem
Current methods for tracking disease-carrying arthropods like ticks are inadequate, relying on historical data, season, and weather estimates, which are inefficient and inaccurate, making it difficult to determine the risk of infection in specific areas, especially due to the migratory nature of these arthropods and the lack of real-time, reliable information dissemination.
Innovation Solution
A mobile tracking application that aggregates data on arthropod sightings, bites, and associated diseases, using a database, API, and mobile application to provide real-time location tracking, risk assessment, and notification systems, incorporating augmented reality, AI image recognition, and crowd-sourced data to create a prevalence indicator for specific geo-locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods relying on historical data, season, and weather estimates are used to track disease-carrying arthropods, then the system is simple to operate, but the measurement precision and reliability of risk assessment are inadequate
Solution Approach 1:
The mobile application serves multiple functions: it tracks arthropod sightings, records bite incidents, aggregates data from multiple sources, provides real-time location tracking, generates prevalence indicators, and delivers notifications. This multi-functional approach consolidates what would otherwise require separate systems into a single platform, achieving high measurement precision without proportionally increasing device complexity
Solution Approach 2:
The system enables crowd-sourced data collection where users actively participate by reporting arthropod sightings and bites through the mobile application. This self-service mechanism automatically generates tracking data without requiring manual intervention for data collection, thereby improving measurement precision while keeping the system simple for users to operate
2Reliability
If real-time tracking of migratory arthropods is implemented, then the reliability of risk information is improved, but the loss of time for data aggregation and processing increases
Solution Approach 1:
The system continuously aggregates data from multiple sources including arthropod sightings, bite reports, and environmental factors. This continuous data collection and processing ensures that prevalence indicators are always current and reliable, while the automated nature of the process minimizes time loss by eliminating manual intervention gaps
Solution Approach 2:
The system performs preliminary data aggregation and processing in the background continuously, so that when users request risk information, the data is already prepared and available. This preliminary action ensures reliability of information while minimizing the perceived time loss for users, as the heavy processing occurs before the information is needed
3Quantity of substance
If crowd-sourced data collection through mobile application is used, then the quantity of arthropod location data increases, but the device complexity for data management increases
Solution Approach 1:
The mobile application acts as an intermediary between users and the central server. It collects data from users in a simplified format, automatically processes and validates it, then transmits to the server. This intermediary role manages the complexity of handling large volumes of crowd-sourced data while keeping the user interface simple and the data management process automated
Data Source
AI summary
The present invention comprises the capture and display of arthropod, human and arthropod-based metadata, which is capable of tracking and displaying the metadata, which is time and location-based, in order to show migration paths of arthropods and/or the diseases they have the potential to carry. This real-time view can help predict future arthropod and disease based on various scenarios such as, but not limited to: increased exposure based on the following: a user's geo-location, date and/or time of year, carrier type, etc. These variables can then assist with the education, awareness and potential prevention of disease.