Markov Chain Model for Infectious Disease Transmission Estimation
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Solution Overview
Problem
Conventional compartmental models for infectious disease transmission, such as the SIR model, are inadequate in representing complex phenomena like COVID-19 transmission, as they fail to account for vaccinated, hospitalized, and latent states, and assume constant infection transmission rates, which are not applicable to diseases like COVID-19 that vary with contact and distancing policies.
Innovation Solution
A data-based infectious disease model using a Markov chain with multiple states, including vaccinated and hospitalized states, to estimate a risk measurement index and the number of hidden infectious persons, employing discrete-time Markov chain modeling, backward and forward reasoning, and extraction of an inverse scale coefficient to calculate a reproduction factor for predicting hidden infectious persons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional compartmental models (SIR, SEIR) are used for infectious disease transmission, then the model structure is simple and easy to implement, but the model cannot accurately represent complex phenomena such as vaccinated states, hospitalized states, and latent states
Solution Approach 1:
The patent segments the population into multiple distinct compartments including susceptible, exposed, latent, infectious, vaccinated, hospitalized, and recovered states. This segmentation allows the model to capture complex disease transmission dynamics while maintaining a structured approach to modeling each state transition separately.
Solution Approach 2:
The patent implements dynamic transition rates between compartments that change over time based on vaccination campaigns, hospitalization policies, and disease progression. This allows the model to adapt to changing conditions rather than assuming fixed transition parameters.
2Adaptability or versatility
If constant infection transmission rate is assumed in conventional models, then the model is mathematically simple, but it cannot capture varying transmission rates affected by distancing policies and contact patterns
Solution Approach 1:
The patent employs time-dependent transmission rates that vary based on policy interventions, contact patterns, and population behavior. This dynamic approach allows the model to capture the evolving nature of disease transmission while maintaining mathematical tractability through structured functional forms.
Solution Approach 2:
The patent allows transmission rate parameters to change over time and across different population groups based on vaccination status, hospitalization policies, and behavioral changes. This enables the model to reflect real-world variations in transmission dynamics without requiring complete reparameterization.
3Loss of information
If conventional SIR model is used, then the model provides confirmed case data, but it cannot provide information on hidden infectious persons and exposed states
Solution Approach 1:
The patent introduces additional compartments for exposed and latent individuals who are not yet confirmed cases but can transmit the disease. This segmentation captures the hidden transmission chain and provides information on pre-symptomatic and asymptomatic spread that conventional models miss.
Solution Approach 2:
The patent uses the exposed and latent compartments as intermediary states between susceptible and confirmed infectious individuals. These intermediary compartments represent the hidden transmission pathway and allow the model to track and estimate undetected cases through mathematical relationships with observed data.
Data Source
AI summary
A system for estimating for infectious disease transmission includes: a parameter receiving unit that constructs a discrete-time Markov chain model indicating a state and a state transition probability, and receives a parameter indicating status information according to the infectious disease transmission at a time point after t days have elapsed from a start of infection spread; a calculation unit that calculates the number of hidden infectious states through backward reasoning, and calculates the number of hidden infectious states through forward reasoning using the received parameter; an extraction unit that extracts an inverse scale coefficient using the calculated number of infection states and calculates a reproduction factor using the extracted inverse scale coefficient; and a prediction unit that updates infectious disease status information using the calculated reproduction factor, and predicts the number of hidden infectious persons using the updated infectious disease status information.