SYSTEMS AND METHODS FOR CONTAGIOUS ILLNESS SURVEILLANCE AND OUTBREAK DETECTION

Smart overview of the Invention

The patent application describes innovative systems and methods for monitoring public health by utilizing a network of smart thermometers. These devices collect geolocated user data to predict the spread of contagious illnesses across various population nodes. Early warning signals for outbreaks can be generated, offering a granular level of detail that can aid in timely interventions to control the spread of diseases.

Background Context

Infectious diseases pose significant public health challenges globally, with seasonal influenza costing the U.S. economy $87.1 billion annually. Current systems like the National Notifiable Diseases Surveillance System (NNDSS) and ILI-Net face limitations, such as lagging data and incomplete coverage, which hinder rapid identification of disease hotspots. These systems often miss mild or asymptomatic cases, especially in underserved communities, creating blind spots for decision-makers.

Technical Description

The disclosed systems utilize a vast network of temperature sensing probes, or smart thermometers, to gather real-time user data from diverse geographical areas. This approach allows for the early detection of illness transmission trends, potentially identifying outbreaks before traditional healthcare systems can. The real-time data processing minimizes lag and provides a more comprehensive picture of public health across different regions.

Advantages Over Traditional Systems

Traditional healthcare systems often overlook mildly symptomatic individuals who do not seek medical attention, as well as underserved populations who face barriers to accessing healthcare. The proposed system addresses these gaps by capturing data from these groups, offering insights that conventional methods cannot provide due to delays in data collection and processing. This timely data can be crucial for early intervention efforts.

Forecasting Capabilities

The system also enables long-lead forecasting of influenza-like illnesses (ILI) using real-time data from smart thermometers. For instance, it can generate 12-week forecasts for specific geographic areas based on geo-coded data. This forecasting leverages historical incidence data to develop unique transmission patterns for different regions, allowing for accurate predictions of epidemic intensity driven by local climate and population structures.