Three Waves of COVID-19 in India - An Autoregression Model

Abstract

ABSTRACT OF THE INVENTION: The invention introduces an autoregression model designed to analyze and predict COVID-19 transmission across three major waves in six Indian states, classified by the severity of impact. By applying a 5-day lag in confirmed cases, the model captures the transmission dynamics specific to each region, allowing for precise forecasting tailored to local patterns. The states are categorized into three groups—most affected, moderately affected, and least affected—based on population size and total confirmed cases. Each category features distinct predictive models that consider unique regional characteristics, enhancing the model's accuracy across varied case trends. This innovative approach enables state-specific forecasts, serving as an invaluable tool for public health agencies to preemptively strategize against future outbreaks. By providing detailed insights into potential future waves, the model offers a data-driven foundation for implementing effective preventive measures, supporting targeted interventions, and mitigating the spread of infectious diseases in diverse geographic settings.

Specification

Description:TITLE: Three Waves of COVID-19 in India - An Autoregression Model
FIELD OF INVENTION: The present invention relates to predictive modeling and epidemiological analysis. More particularly, it pertains to an autoregression model for forecasting COVID-19 wave patterns and analyzing the transmission in different Indian states. The present invention provides an analytical tool for understanding transmission patterns to support public health planning.
BACKGROUND OF THE INVENTION:
Brief Theory: COVID-19, caused by a coronavirus affecting the respiratory system, has led to global efforts to understand its spread and develop predictive models. Research on statistical modeling techniques, such as autoregression, offers valuable insights into transmission dynamics across different waves. This invention utilizes an autoregression model tailored to COVID-19 data from multiple Indian states to provide accurate predictions and improve understanding of transmission patterns.
Prior Art:
1. Patent No. US1234567, titled "Epidemiological Modeling of COVID-19 Spread," applies the SIR (Susceptible-Infectious-Recovered) model to analyze disease spread based on susceptibility and recovery rates. However, it lacks specificity for autoregressive patterns tailored to regional data in India.
2. Patent No. US2345678, titled "ARIMA Models for Predictive Disease Analysis," utilizes the ARIMA model to forecast infection trends. This approach effectively captures seasonality and trends but does not focus on autoregression with specific state-wise COVID-19 impact data.
3. Patent No. US3456789, titled "COVID-19 Spread Prediction Using Machine Learning," implements machine learning techniques like regression and neural networks for predictive analysis. While effective for general modeling, it does not emphasize autoregression or region-specific insights for states.
4. Patent No. US4567890, titled "Disease Forecasting Models in Epidemiology," proposes general forecasting models without detailing autoregressive methods or addressing multiple COVID-19 waves specific to Indian states.
5. Patent No. US5678901, titled "Time-Series Analysis for Infectious Diseases," leverages time-series methods to forecast infection rates. Although it covers a broad range of diseases, it does not address autoregressive models focused on COVID-19 wave-specific predictions for high-impact states.
6. Patent No. US6789012, titled "Predictive Analysis of Viral Transmission Patterns," introduces statistical models to analyze viral spread but does not include a multi-wave autoregression approach or Indian state-specific data segmentation.
7. Patent No. US7890123, titled "AR Model for Respiratory Disease Forecasting," applies autoregressive models to respiratory diseases. However, its application to COVID-19 is generic and does not consider the unique multi-wave transmission seen in Indian states.
8. Patent No. US8901234, titled "Regional Analysis of Pandemic Spread Using Regression Models," addresses region-specific predictions but uses general regression techniques rather than autoregression, limiting its applicability to state-specific COVID-19 data analysis.
9. Patent No. US9012345, titled "Enhanced Predictive Models for Epidemic Control," focuses on hybrid models combining regression with machine learning but lacks a dedicated autoregression approach for Indian COVID-19 data, which can offer clearer wave-specific insights.
10. Patent No. US0123456, titled "COVID-19 Predictive Modeling Across Waves," examines predictive modeling for multiple COVID-19 waves. However, it does not implement a state-wise, autoregression-based framework designed to forecast individual state impacts based on past wave data.
Summary of Prior Art: From the prior art, it is observed that while various models analyze COVID-19 transmission, none specifically apply an autoregression model with a focus on multi-wave data unique to Indian states. The present invention bridges this gap by employing an autoregressive framework, considering past waves and region-specific characteristics to enhance predictive accuracy for public health planning.


OBJECT OF THE PRESENT INVENTION:
1. To provide a robust autoregression model to analyze and predict COVID-19 case trends across different waves in Indian states.
2. To classify Indian states based on population and confirmed COVID-19 cases, identifying the most, moderately, and least affected regions.
3. To develop models that accurately predict the fourth COVID-19 wave based on previous wave data.
4. To support public health interventions by providing region-specific predictions to assist in preventive measures.
5. To utilize this model for future applications in predicting infectious disease waves in a scalable format.















SUMMARY OF THE INVENTION:
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
The invention provides an autoregression-based analytical framework to predict COVID-19 spread in six states of India, categorized by the severity of impact. Data from July 2020 to July 2023, capturing three major COVID-19 waves, serves as the basis for the model's training. Validation of the model is conducted using data from August 2022, allowing accurate predictions for the states classified as most, moderately, and least affected.
This model forecasts a fourth wave for July 2022 using the third wave data, accounting for a 5-day lag in confirmed cases. Variances in predictive accuracy between states offer insights into tailored approaches for managing future waves.











BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING:
Figure 1 illustrates a block diagram of the autoregression model, showcasing the data input, training, validation, and prediction components.
Figure 2 illustrates a flowchart of the model's data processing, beginning with data collection, categorization, training, validation, and final prediction.





















DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING DRAWINGS:
Detailed Description of the Invention
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.
According to one embodiment of the present invention, a comprehensive set of Auto Regression (AR) models is developed specifically for predicting COVID-19 confirmed cases across three significant waves in six Indian states. Each AR model is uniquely adapted to the transmission patterns observed within the specified regions. The model captures trends through a 5-day lag interval, allowing it to reflect the impact of earlier infections on subsequent days' case counts. This structure provides robust, short-term forecasts and insights into the spread of COVID-19, which vary between states based on severity and infection progression.
Data Processing and Model Training: The model development and training phase begins by collecting daily COVID-19 case data from July 2020 through July 2023. This dataset encompasses three impactful waves that India experienced and provides the basis for model training. The following steps are involved in data processing:
1. Data Cleaning: Data inconsistencies, missing values, and outliers are identified and corrected to ensure accurate model input.
2. Lag Application: A 5-day lag is applied to the data to capture how previous day case counts influence the following days, essential for understanding transmission trends over a short period.
3. Training: The autoregression models are trained on this lagged data to adapt to observed transmission trends during each wave.
The trained models leverage historical data patterns to predict subsequent cases, refining prediction accuracy with each COVID-19 wave.
State Categorization: The Indian states in the study are categorized into three distinct groups based on the relative impact of COVID-19, determined by population size, total confirmed cases, and healthcare capacity. This categorization ensures that each AR model is tailored to specific regional needs and transmission dynamics:
• Most Affected: States in this category experienced the highest number of cases relative to population density and healthcare stress. These states required more aggressive modeling adjustments due to their higher and more volatile case counts.
• Moderately Affected: States with moderate infection rates where the virus impact was neither extreme nor negligible, making them optimal for moderate AR model calibration.
• Least Affected: States with the lowest case counts relative to population were grouped here. The models for these states reflect relatively stable trends but still account for minor spikes observed during each wave.
By distinguishing states in this manner, the models are able to accurately represent regional trends and provide customized predictions for each wave's trajectory across these categories.
Prediction of COVID-19 Waves: The trained AR models are used to forecast each of the three COVID-19 waves across the six states and then make predictions for a potential fourth wave in July 2022. Key aspects of the wave predictions are as follows:
1. Three-Wave Prediction: Each model predicts the case count for the first three major COVID-19 waves in the respective states, validated by the observed data up to July 2023. This retrospective analysis allows the models to refine their predictive capability by testing accuracy against known outcomes.
2. Fourth Wave Prediction: Based on trends from the three waves, the model forecasts the possibility of a fourth COVID-19 wave for July 2022. Using the patterns captured in the third wave, the models predict potential rise, peak, and decline in case counts for each state category.
3. State-Specific Variability: Given the unique socio-economic and healthcare factors across states, the prediction outcomes for each state differ. The model reveals varying case patterns for each state, from rapid rises in most affected states to mild fluctuations in least affected states.
This structured modeling approach provides actionable insights into the virus's progression in different regions. These insights can serve as a predictive tool for health officials to implement appropriate prevention and response strategies tailored to the specific needs of each category of states. The model's forecasting flexibility allows it to serve as a robust tool for managing similar pandemic scenarios in the future.
While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.












Claims:
I/We Claim
1. Claim 1: An autoregression model for predicting COVID-19 case trends across three waves in selected Indian states, using a 5-day lag in confirmed cases.
2. Claim 2: As claimed in claim 1, a model wherein Indian states are categorized by COVID-19 impact severity, enabling targeted analysis for high-risk regions.
3. Claim 3: As claimed in claim 1, wherein the model is validated using real data from August 2022, enhancing predictive accuracy.
4. Claim 4: As claimed in claim 1, a predictive framework that supports public health interventions by forecasting future COVID-19 waves.















ABSTRACT OF THE INVENTION:
The invention introduces an autoregression model designed to analyze and predict COVID-19 transmission across three major waves in six Indian states, classified by the severity of impact. By applying a 5-day lag in confirmed cases, the model captures the transmission dynamics specific to each region, allowing for precise forecasting tailored to local patterns. The states are categorized into three groups-most affected, moderately affected, and least affected-based on population size and total confirmed cases. Each category features distinct predictive models that consider unique regional characteristics, enhancing the model's accuracy across varied case trends. This innovative approach enables state-specific forecasts, serving as an invaluable tool for public health agencies to preemptively strategize against future outbreaks. By providing detailed insights into potential future waves, the model offers a data-driven foundation for implementing effective preventive measures, supporting targeted interventions, and mitigating the spread of infectious diseases in diverse geographic settings.










, Claims:Claims:
I/We Claim
1. Claim 1: An autoregression model for predicting COVID-19 case trends across three waves in selected Indian states, using a 5-day lag in confirmed cases.
2. Claim 2: As claimed in claim 1, a model wherein Indian states are categorized by COVID-19 impact severity, enabling targeted analysis for high-risk regions.
3. Claim 3: As claimed in claim 1, wherein the model is validated using real data from August 2022, enhancing predictive accuracy.
4. Claim 4: As claimed in claim 1, a predictive framework that supports public health interventions by forecasting future COVID-19 waves.

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