1. A system for forecasting and predicting the spread of epidemic diseases, comprising: a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by number of cases reported, number of deaths reported, age group of patients, state/province of report, year of report, month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index; a data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing; a hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising: a plurality of layers including an LSTM layer, a dropout layer, and a dense layer, each configured to process and analyze the preprocessed multivariate dataset; a plurality of gates within the LSTM layer, including a forget gate, an input gate, and an output gate, each gate being configured to selectively forget or retain information within cell states; a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model; an alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios; a performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics and to optimize the model's parameters accordingly.

2. The system as claimed in claim 1, wherein said data processing module further comprises an outlier detection submodule configured to identify and handle anomalies within such multivariate dataset to ensure the integrity of the data.

3. The system as claimed in claim 1, wherein said hybrid deep learning model employs a rectified linear activation function (ReLU) within the dense layer to enhance the model’s ability to handle non-linear relationships in the data.

4. The system as claimed in claim 1, wherein said plurality of gates within the LSTM layer includes a self-attention mechanism configured to dynamically adjust the weights of such gates based on the importance of the input data at each time step.

5. The system as claimed in claim 1, wherein said prediction module is further configured to generate confidence intervals for such forecasts, providing an estimation of the uncertainty associated with the predictions.

6. The system as claimed in claim 1, wherein said alert module includes a communication interface configured to transmit such warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications.

7. The system as claimed in claim 1, wherein said performance evaluation module further comprises a cross-validation submodule configured to partition such multivariate dataset into training and validation sets to ensure the robustness and generalizability of the hybrid deep learning model.

8. The system as claimed in claim 1, wherein said data processing module includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change, to enhance the predictive capability of the hybrid deep learning model.

9. The system as claimed in claim 1, wherein said hybrid deep learning model further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting, to improve the overall accuracy and reliability of the forecasts. SYSTEM FOR FORECASTING AND PREDICTING THE SPREAD OF EPIDEMIC DISEASES Abstract The present disclosure discloses a system for forecasting and predicting the spread of epidemic diseases, comprising a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by various factors. A data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing. A hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising a plurality of layers configured to process and analyze the preprocessed multivariate dataset. A prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model. An alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios. A performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics. Fig. 1 , Claims:Claims :

1. A system for forecasting and predicting the spread of epidemic diseases, comprising: a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by number of cases reported, number of deaths reported, age group of patients, state/province of report, year of report, month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index; a data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing; a hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising: a plurality of layers including an LSTM layer, a dropout layer, and a dense layer, each configured to process and analyze the preprocessed multivariate dataset; a plurality of gates within the LSTM layer, including a forget gate, an input gate, and an output gate, each gate being configured to selectively forget or retain information within cell states; a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model; an alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios; a performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics and to optimize the model's parameters accordingly.

2. The system as claimed in claim 1, wherein said data processing module further comprises an outlier detection submodule configured to identify and handle anomalies within such multivariate dataset to ensure the integrity of the data.

3. The system as claimed in claim 1, wherein said hybrid deep learning model employs a rectified linear activation function (ReLU) within the dense layer to enhance the model’s ability to handle non-linear relationships in the data.

4. The system as claimed in claim 1, wherein said plurality of gates within the LSTM layer includes a self-attention mechanism configured to dynamically adjust the weights of such gates based on the importance of the input data at each time step.

5. The system as claimed in claim 1, wherein said prediction module is further configured to generate confidence intervals for such forecasts, providing an estimation of the uncertainty associated with the predictions.

6. The system as claimed in claim 1, wherein said alert module includes a communication interface configured to transmit such warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications.

7. The system as claimed in claim 1, wherein said performance evaluation module further comprises a cross-validation submodule configured to partition such multivariate dataset into training and validation sets to ensure the robustness and generalizability of the hybrid deep learning model.

8. The system as claimed in claim 1, wherein said data processing module includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change, to enhance the predictive capability of the hybrid deep learning model.

9. The system as claimed in claim 1, wherein said hybrid deep learning model further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting, to improve the overall accuracy and reliability of the forecasts.

Description:SYSTEM FOR FORECASTING AND PREDICTING THE SPREAD OF EPIDEMIC DISEASES
Field of the Invention
[0001] The present disclosure generally relates to disease forecasting systems. Further, the present disclosure particularly relates to a system for forecasting and predicting the spread of epidemic diseases.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Modern medical services and treatments are attainable globally. Despite this availability, outbreaks continue to occur. In rural and underprivileged areas, these modern amenities remain inaccessible, leaving such areas grappling with basic needs. The 21st century has witnessed epidemic diseases posing significant hazards with high mortality and morbidity rates. Although advanced medical services and treatments are accessible worldwide, outbreaks persist. Rural and underprivileged areas lack access to these amenities and continue to struggle with fundamental requirements.
[0004] Moreover, younger children below the age of 5 years are particularly vulnerable, with 28,000 to 111,500 deaths reported annually. Approximately 99% of these deaths occur in developing countries. Such a scenario highlights the disparity in healthcare availability and the consequent impact on public health. The continued prevalence of epidemic diseases such as COVID-19, dengue, and influenza underscores the limitations of current methods in controlling these outbreaks, especially in resource-limited settings. Further, the control of malaria outbreaks remains a significant challenge in many regions, particularly in developing countries.
[0005] Furthermore, conventional methods of disease prediction and control often rely on reactive measures rather than proactive approaches. Such methods typically involve monitoring disease spread through reported cases and then implementing control measures. However, this approach can result in delays, allowing the disease to spread further before effective interventions are applied. In light of these limitations, there is a pressing need for innovative approaches that can enhance the prediction and control of infectious diseases, particularly in areas with limited healthcare infrastructure.
[0006] Additionally, existing predictive models often fail to account for the complex and dynamic nature of disease transmission. Traditional models may not incorporate various temporal and spatial variables that influence the spread of infectious diseases. Consequently, such models may offer limited accuracy and reliability in forecasting disease outbreaks. The need for more sophisticated models that can integrate a wide range of factors and provide more accurate predictions is evident.
[0007] Moreover, the rapid spread of diseases like COVID-19 has highlighted the importance of timely and accurate disease forecasting. Early prediction of disease outbreaks can enable health authorities to implement preventive measures more effectively, thereby reducing the impact of the disease on the population. However, achieving accurate predictions requires advanced techniques that can process and analyze large volumes of data from diverse sources.
[0008] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for predicting and controlling the spread of infectious diseases.

Summary
[0009] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] In an aspect, the present disclosure provides a system for forecasting and predicting the spread of epidemic diseases, comprising a multivariate dataset including temporal variables. Such a dataset includes historical data on epidemic diseases like COVID-19, dengue, influenza, and malaria. The data are categorized by the number of cases reported, number of deaths reported, age group of patients, state/province of report, year of report, month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index. The system includes a data processing module configured to preprocess such a multivariate dataset, involving normalization, handling of missing values, and temporal sequencing. Additionally, the system includes a hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks. Such a model comprises a plurality of layers, including an LSTM layer, a dropout layer, and a dense layer, each configured to process and analyze the preprocessed multivariate dataset. The system further includes a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such a hybrid deep learning model. An alert module issues warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios. A performance evaluation module assesses the accuracy of such a hybrid deep learning model using predetermined metrics and optimizes the model's parameters accordingly.
[00012] In an embodiment, the data processing module further comprises an outlier detection submodule configured to identify and handle anomalies within such a multivariate dataset to ensure the integrity of the data. Such a submodule enables the removal or correction of outliers, thereby improving the overall quality and reliability of the dataset used for forecasting.
[00013] In an embodiment, the hybrid deep learning model employs a rectified linear activation function (ReLU) within the dense layer to enhance the model’s ability to handle non-linear relationships in the data. Such an activation function enables more efficient learning and prediction by addressing the complexity and variability inherent in epidemiological data.
[00014] In an embodiment, the plurality of gates within the LSTM layer includes a self-attention mechanism configured to dynamically adjust the weights of such gates based on the importance of the input data at each time step. Such a mechanism enhances the model’s capacity to focus on relevant features, thereby improving the accuracy of the predictions.
[00015] In an embodiment, the prediction module is further configured to generate confidence intervals for such forecasts, providing an estimation of the uncertainty associated with the predictions. Such intervals offer valuable insights into the reliability and range of the forecasted outcomes, aiding in better decision-making.
[00016] In an embodiment, the alert module includes a communication interface configured to transmit such warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications. Such a communication interface ensures timely dissemination of critical information, enabling prompt and effective responses to emerging threats.
[00017] In an embodiment, the performance evaluation module further comprises a cross-validation submodule configured to partition such a multivariate dataset into training and validation sets to ensure the robustness and generalizability of the hybrid deep learning model. Such a submodule facilitates the assessment of model performance under various scenarios, enhancing its reliability.
[00018] In an embodiment, the data processing module includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change, to enhance the predictive capability of the hybrid deep learning model. Such derived features provide more nuanced insights, contributing to more accurate forecasts.
[00019] In an embodiment, the hybrid deep learning model further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting, to improve the overall accuracy and reliability of the forecasts. Such an ensemble approach leverages the strengths of different models, resulting in more robust predictions.
[00020] Such a system for forecasting and predicting the spread of epidemic diseases provides significant advantages by utilizing advanced data processing and deep learning techniques. Further, such a system enables healthcare providers to proactively manage resources and implement preventive measures based on accurate and timely predictions. Moreover, such a system enhances public health surveillance by integrating comprehensive datasets and sophisticated analytical models.
Brief Description of the Drawings
[00021] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00022] FIG. 1 illustrates a basic block diagram of a system for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure.
[00023] FIG. 2 illustrates a first pseudo code for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure.
[00024] FIG. 3 illustrates a second pseudo code for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure.
[00025] FIG. 4 illustrates a process flow diagram for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure.
Detailed Description
[00026] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00027] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00028] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00029] The system comprises a multivariate dataset including temporal variables. Such dataset comprises historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria. The dataset categorizes data by several parameters: the number of cases reported, the number of deaths reported, the age group of patients, the state or province of report, the year of report, the month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index. The collection and categorization of this data enable comprehensive analysis and understanding of the spread patterns and impacts of epidemic diseases over time and across different regions. The multivariate nature of the dataset allows the system to capture the complexity and interdependencies of various factors influencing disease spread. The temporal variables ensure that the system can track changes over time, providing a dynamic and evolving picture of epidemic trends. By including environmental factors such as temperature and rainfall, the dataset incorporates the potential effects of climate on disease transmission. The population index offers insights into the density and demographics of affected areas, facilitating more targeted and effective interventions. The historical data on different diseases enables comparative analyses and identification of common factors or unique characteristics associated with each disease. This comprehensive dataset forms the foundation for the system's ability to forecast and predict the spread of epidemic diseases, providing a robust input for further processing and analysis.
[00030] The system includes a data processing module configured to preprocess the multivariate dataset. The preprocessing involves normalization, handling of missing values, and temporal sequencing. Normalization ensures that the data is on a common scale, which is essential for the effective functioning of machine learning models. Handling missing values involves techniques such as imputation or removal of incomplete records to maintain the integrity of the dataset. Temporal sequencing arranges the data in chronological order, enabling the system to recognize and analyze temporal patterns in the spread of diseases. The preprocessing stage is crucial as it prepares the raw data for analysis by the hybrid deep learning model. By ensuring the data is clean, consistent, and properly sequenced, the data processing module enhances the accuracy and reliability of subsequent predictions. The module addresses common data quality issues that could otherwise distort the analysis, such as inconsistencies in data entry or gaps in reporting. This preprocessing step lays the groundwork for the sophisticated analysis performed by the hybrid deep learning model, ensuring that the input data is of the highest quality and ready for complex processing.
[00031] The system employs a hybrid deep learning model comprising Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks. This model includes a plurality of layers designed to process and analyze the preprocessed multivariate dataset. The layers include an LSTM layer, a dropout layer, and a dense layer. The LSTM layer handles the temporal aspects of the data, capable of retaining important information over long sequences and discarding irrelevant details. The dropout layer helps prevent overfitting by randomly setting a fraction of input units to zero during training, thus improving the model's generalization capabilities. The dense layer serves as the final layer that combines the learned features and outputs the final prediction. Each of these layers plays a critical role in the functioning of the hybrid deep learning model. The LSTM layer's ability to manage temporal dependencies makes it particularly suited for time-series data such as the spread of epidemic diseases. The dropout layer ensures that the model remains robust and does not overfit to the training data, maintaining its performance on unseen data. The dense layer consolidates the information processed by previous layers, producing accurate and meaningful predictions.
[00032] Within the LSTM layer of the hybrid deep learning model, the system comprises a plurality of gates including a forget gate, an input gate, and an output gate. Each gate selectively forgets or retains information within cell states, enabling precise control over the information flow through the LSTM network. The forget gate decides which information to discard from the cell state, while the input gate determines which new information to store. The output gate controls the output based on the cell state. These gates work in unison to manage the flow of information and ensure that only relevant data is kept throughout the processing. This gating mechanism is critical for handling long-term dependencies and mitigating the vanishing gradient problem, which are common challenges in training deep learning models on sequential data. By dynamically adjusting the cell states, the gates enable the LSTM layer to maintain context over long sequences of data, making it particularly effective for time-series forecasting. The precise control over information retention and forgetting provided by these gates enhances the model's ability to learn and generalize from complex and temporally dependent data.
[00033] The system includes a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from the hybrid deep learning model. This module leverages the learned patterns and relationships within the data to predict future trends in disease spread. The forecasts generated by the prediction module provide valuable insights into potential outbreaks, helping authorities and healthcare providers to prepare and respond effectively. The module's ability to accurately predict the spread of diseases is a crucial feature of the system, enabling proactive measures to mitigate the impact of epidemics. By analyzing the processed data, the prediction module identifies trends and anomalies that may indicate the onset of an epidemic. The forecasts consider various factors, including historical trends, environmental conditions, and population dynamics, providing a comprehensive view of potential future scenarios. The accuracy of these predictions is enhanced by the sophisticated processing performed by the hybrid deep learning model, ensuring that the forecasts are based on robust and reliable analyses. The prediction module serves as a critical decision-support tool, enabling timely and informed interventions to control the spread of diseases.
[00034] The system includes an alert module configured to issue warnings and recommendations to healthcare service providers based on the forecasts generated by the prediction module. This module enables necessary arrangements for potential worst-case scenarios, ensuring that healthcare systems are prepared to handle increases in disease cases. The alerts provided by the module include information on the predicted spread, severity, and potential impact of an epidemic, allowing healthcare providers to allocate resources effectively. The recommendations may involve measures such as increasing hospital capacity, stockpiling essential supplies, and implementing public health interventions to control the spread of the disease. The alert module enhances the system's utility by translating the forecasts into actionable insights, facilitating timely and effective responses. The warnings issued by the module help prevent healthcare systems from being overwhelmed during outbreaks, ensuring that adequate care is provided to affected individuals. By providing early warnings and detailed recommendations, the alert module plays a vital role in mitigating the impact of epidemics and protecting public health.
[00035] The system includes a performance evaluation module configured to assess the accuracy of the hybrid deep learning model using

based on the evaluation results, ensuring continuous improvement in prediction accuracy. The performance evaluation involves metrics such as mean absolute error, root mean squared error, and precision-recall metrics, providing a comprehensive assessment of the model's performance. By continuously monitoring and evaluating the model's predictions, the performance evaluation module identifies areas for improvement and adjusts the model's parameters accordingly. This iterative process of evaluation and optimization enhances the model's ability to make accurate and reliable forecasts. The performance evaluation module ensures that the system remains effective over time, adapting to new data and changing conditions. By maintaining high standards of accuracy, the module contributes to the overall reliability and utility of the system in predicting the spread of epidemic diseases. The continuous optimization process facilitated by the performance evaluation module ensures that the system stays current with the latest data and advancements in modeling techniques, providing the best possible forecasts to support public health efforts.
[00036] In an embodiment, the system includes a data processing module that further comprises an outlier detection submodule configured to identify and handle anomalies within the multivariate dataset to ensure the integrity of the data. Such an outlier detection submodule uses statistical and machine learning techniques to detect data points that deviate significantly from the expected patterns. By identifying these anomalies, the submodule can either remove or correct them, depending on the nature of the outlier. The handling of outliers is critical to maintain the accuracy of the data analysis, as anomalies can skew the results and lead to incorrect predictions. The outlier detection submodule operates in conjunction with the normalization and missing value handling processes to provide a clean and reliable dataset for further processing. This integration ensures that the data processing module delivers high-quality data to the hybrid deep learning model, thereby enhancing the overall performance and reliability of the system in forecasting and predicting the spread of epidemic diseases.
[00037] In an embodiment, the hybrid deep learning model of the system employs a rectified linear activation function (ReLU) within the dense layer. Such a ReLU function enhances the model’s ability to handle non-linear relationships in the data by allowing the model to learn more complex patterns and interactions within the dataset. The ReLU function operates by outputting the input directly if it is positive; otherwise, it outputs zero. This property of the ReLU function helps in mitigating the vanishing gradient problem, which is common in deep learning networks, thus improving the training efficiency and convergence speed. The use of the ReLU activation function in the dense layer contributes to the overall flexibility and adaptability of the hybrid deep learning model, enabling it to better capture and represent the underlying dynamics of the multivariate dataset. This enhancement leads to more accurate and reliable forecasts of epidemic disease spread, ultimately aiding in effective public health planning and response.
[00038] In an embodiment, the plurality of gates within the LSTM layer of the hybrid deep learning model includes a self-attention mechanism. Such a self-attention mechanism dynamically adjusts the weights of the gates based on the importance of the input data at each time step. This mechanism allows the model to focus on the most relevant parts of the input sequence, enhancing its ability to learn long-term dependencies and improve the accuracy of the predictions. By assigning different weights to different inputs, the self-attention mechanism provides a more nuanced and context-aware analysis of the data, which is particularly beneficial for complex and time-varying datasets like those used in forecasting epidemic disease spread. The integration of the self-attention mechanism within the LSTM layer contributes to the robustness and effectiveness of the hybrid deep learning model, enabling it to generate more accurate and meaningful predictions based on the processed multivariate dataset.
[00039] In an embodiment, the prediction module of the system is further configured to generate confidence intervals for the forecasts. Such confidence intervals provide an estimation of the uncertainty associated with the predictions, offering a range within which the true value is expected to lie with a certain probability. The inclusion of confidence intervals enhances the usefulness of the forecasts by providing healthcare service providers with a better understanding of the potential variability and reliability of the predictions. This information is crucial for risk assessment and decision-making, as it allows stakeholders to prepare for a range of possible scenarios rather than relying on a single point estimate. The ability to quantify and communicate uncertainty in the forecasts makes the prediction module a more valuable tool for managing and mitigating the impact of epidemic diseases.
[00040] In an embodiment, the alert module of the system includes a communication interface configured to transmit warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications. Such a communication interface ensures that critical information reaches the intended recipients promptly and effectively, enabling timely responses to potential epidemic threats. By leveraging multiple communication channels, the alert module maximizes the likelihood that healthcare providers will receive and act on the alerts, regardless of their preferred mode of communication. The integration of the communication interface enhances the system's overall responsiveness and utility, providing a reliable means of disseminating important information and recommendations to those who need it most.
[00041] In an embodiment, the performance evaluation module of the system further comprises a cross-validation submodule configured to partition the multivariate dataset into training and validation sets. Such a cross-validation submodule ensures the robustness and generalizability of the hybrid deep learning model by assessing its performance on different subsets of the data. Cross-validation involves repeatedly splitting the dataset into training and validation sets, training the model on the training set, and evaluating it on the validation set. This process helps identify overfitting and underfitting issues, allowing for better tuning of the model’s parameters and improving its overall performance. The use of cross-validation provides a more reliable measure of the model's accuracy and generalizability, ensuring that it performs well not only on the training data but also on unseen data. This robustness is crucial for the system's ability to generate accurate and dependable forecasts of epidemic disease spread.
[00042] In an embodiment, the data processing module of the system includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change. Such a feature extraction submodule enhances the predictive capability of the hybrid deep learning model by providing more informative and relevant inputs. By calculating moving averages, the submodule smooths out short-term fluctuations and highlights longer-term trends, which are important for accurate forecasting. The rate of change captures the speed at which certain variables are changing over time, providing insights into the dynamics of the epidemic spread. These additional features enrich the dataset, allowing the hybrid deep learning model to learn more effectively and make more accurate predictions. The feature extraction submodule plays a vital role in transforming the raw data into a more useful and actionable format, thereby improving the overall performance of the system in predicting the spread of epidemic diseases.
[00043] In an embodiment, the hybrid deep learning model of the system further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting. Such an ensemble learning component improves the overall accuracy and reliability of the forecasts by leveraging the strengths of different models. Each model in the ensemble contributes to the final prediction, and the ensemble learning component integrates these contributions in a way that minimizes errors and maximizes predictive performance. The use of multiple models helps to mitigate the biases and limitations of any single model, resulting in more robust and reliable forecasts. The ensemble learning approach enhances the system's ability to capture complex patterns and relationships within the data, providing more accurate and dependable predictions of epidemic disease spread. This integration of diverse modeling techniques makes the hybrid deep learning model a powerful tool for forecasting and managing epidemic diseases.
[00044] FIG. 1 illustrates a basic block diagram of a system for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure. The system first examines the role of machine learning in predicting epidemic diseases. Subsequently, various machine learning models for epidemic disease prediction are studied. The system then involves data acquisition and preprocessing, followed by the study and implementation of transfer learning and ensemble modelling for predicting epidemic diseases. The system calculates performance in terms of accuracy, precision, RMSE, MSE, and R2. Design and implementation of a hybrid model consisting of different layers are performed next. Hyper-parameter tuning of all implemented models for optimizing the results is carried out. The performance of the hybrid model is calculated. The system finally involves a comparison between baseline models and proposed models, leading to the conclusion. Said system provides a comprehensive framework for the prediction of epidemic diseases using machine learning techniques, enhancing the accuracy and reliability of epidemic disease forecasting, thereby aiding in better preparedness and response to potential outbreaks. The systematic approach depicted in FIG. 1 ensures an organized and thorough methodology for developing and assessing predictive models for epidemic diseases.
[00045] FIG. 2 illustrates a first pseudo code for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure. The pseudo code begins with the input of loading the dataset for preprocessing. The output includes positive COVID-19 cases and deaths over a specified number of days. The dataset is normalized into values ranging from 0 to 1. The sequential network is initialized. The number of RNN blocks and input activation functions are set. The training window size is selected. For a specified number of epochs and batch sizes, the network is trained iteratively. Predictions are then run. The loss function, MSE, MAE, MAPE, and RMSE are calculated to evaluate the performance of the model. Said pseudo code provides a structured approach for preparing and training the network, as well as for evaluating its performance in predicting the spread of epidemic diseases. Normalizing the dataset ensures consistent input values, while the iterative training process enables the network to learn patterns within the data. Evaluating the model using multiple metrics such as MSE, MAE, MAPE, and RMSE provides a comprehensive assessment of the model's accuracy and reliability in forecasting epidemic trends.
[00046] FIG. 3 illustrates a second pseudo code for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure. The pseudo code begins with initializing the sequential model. The output includes positive COVID-19 cases and deaths over a specified number of days. The first LSTM layer is added with 45 units, return sequences set to true, and the input shape defined. Dropout regularization is selected at 0.2. The second LSTM layer is added with 65 units, return sequences set to true, and dropout regularization selected at 0.2. The third LSTM layer is added with 85 units, return sequences set to true, and dropout regularization selected at 0.2. The fourth LSTM layer is added with 128 units, and dropout regularization selected at 0.2. The output layer is then added with 1 dense unit. The loss function or optimization strategy is calculated and set. The optimizer is selected as Adam. The desired number of passes over the data (epochs) is set. Training and testing epochs are configured, with a batch size of 64 and verbosity set to 1. Finally, the results are returned. Said pseudo code provides a comprehensive structure for setting up an LSTM network with dropout regularization, optimizing the model, and forecasting epidemic disease trends accurately.
[00047] FIG. 4 illustrates a process flow diagram for forecasting and predicting the spread of epidemic diseases, in accordance with the embodiments of the present disclosure. The process begins by extracting a batch of tasks. For each batch of tasks, the dataset is divided into training data (D_train) and testing data (D_test). The training data is then used to train a sequence of models including Fully Connected (FC), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). The training involves calculating the gradient, minimizing loss, and obtaining optimal parameters. The results from the training are cross-validated using a 10-fold cross-validation process. The testing data is then evaluated with the trained models to ensure the accuracy and reliability of the predictions. The gradient calculation, loss minimization, and parameter optimization steps are repeated for each model to refine the performance. Finally, the optimal parameters are used to predict the spread of epidemic diseases. This process enables a structured approach to model training and evaluation, ensuring comprehensive analysis and reliable forecasting of epidemic disease trends using advanced machine learning techniques.
[00048] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00049] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00050] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00051] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00052] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A system for forecasting and predicting the spread of epidemic diseases, comprising:
a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by number of cases reported, number of deaths reported, age group of patients, state/province of report, year of report, month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index;
a data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing;
a hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising:
a plurality of layers including an LSTM layer, a dropout layer, and a dense layer, each configured to process and analyze the preprocessed multivariate dataset;
a plurality of gates within the LSTM layer, including a forget gate, an input gate, and an output gate, each gate being configured to selectively forget or retain information within cell states;
a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model;
an alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios;
a performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics and to optimize the model's parameters accordingly.
2. The system as claimed in claim 1, wherein said data processing module further comprises an outlier detection submodule configured to identify and handle anomalies within such multivariate dataset to ensure the integrity of the data.
3. The system as claimed in claim 1, wherein said hybrid deep learning model employs a rectified linear activation function (ReLU) within the dense layer to enhance the model’s ability to handle non-linear relationships in the data.
4. The system as claimed in claim 1, wherein said plurality of gates within the LSTM layer includes a self-attention mechanism configured to dynamically adjust the weights of such gates based on the importance of the input data at each time step.
5. The system as claimed in claim 1, wherein said prediction module is further configured to generate confidence intervals for such forecasts, providing an estimation of the uncertainty associated with the predictions.
6. The system as claimed in claim 1, wherein said alert module includes a communication interface configured to transmit such warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications.
7. The system as claimed in claim 1, wherein said performance evaluation module further comprises a cross-validation submodule configured to partition such multivariate dataset into training and validation sets to ensure the robustness and generalizability of the hybrid deep learning model.
8. The system as claimed in claim 1, wherein said data processing module includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change, to enhance the predictive capability of the hybrid deep learning model.
9. The system as claimed in claim 1, wherein said hybrid deep learning model further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting, to improve the overall accuracy and reliability of the forecasts.

SYSTEM FOR FORECASTING AND PREDICTING THE SPREAD OF EPIDEMIC DISEASES
Abstract
The present disclosure discloses a system for forecasting and predicting the spread of epidemic diseases, comprising a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by various factors. A data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing. A hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising a plurality of layers configured to process and analyze the preprocessed multivariate dataset. A prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model. An alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios. A performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics.
Fig. 1 , Claims:Claims
I/We Claim:
1. A system for forecasting and predicting the spread of epidemic diseases, comprising:
a multivariate dataset including temporal variables, such dataset comprising historical data on epidemic diseases including COVID-19, dengue, influenza, and malaria, such data being categorized by number of cases reported, number of deaths reported, age group of patients, state/province of report, year of report, month of report, average temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and population index;
a data processing module configured to preprocess such multivariate dataset, including normalization, handling of missing values, and temporal sequencing;
a hybrid deep learning model employing Adaptive-Gradient Long Short-Term Memory (AGLSTM) networks, such model comprising:
a plurality of layers including an LSTM layer, a dropout layer, and a dense layer, each configured to process and analyze the preprocessed multivariate dataset;
a plurality of gates within the LSTM layer, including a forget gate, an input gate, and an output gate, each gate being configured to selectively forget or retain information within cell states;
a prediction module configured to generate forecasts on the spread of epidemic diseases based on the processed data from such hybrid deep learning model;
an alert module configured to issue warnings and recommendations to healthcare service providers based on such forecasts to enable necessary arrangements for potential worst-case scenarios;
a performance evaluation module configured to assess the accuracy of such hybrid deep learning model using predetermined metrics and to optimize the model's parameters accordingly.
2. The system as claimed in claim 1, wherein said data processing module further comprises an outlier detection submodule configured to identify and handle anomalies within such multivariate dataset to ensure the integrity of the data.
3. The system as claimed in claim 1, wherein said hybrid deep learning model employs a rectified linear activation function (ReLU) within the dense layer to enhance the model’s ability to handle non-linear relationships in the data.
4. The system as claimed in claim 1, wherein said plurality of gates within the LSTM layer includes a self-attention mechanism configured to dynamically adjust the weights of such gates based on the importance of the input data at each time step.
5. The system as claimed in claim 1, wherein said prediction module is further configured to generate confidence intervals for such forecasts, providing an estimation of the uncertainty associated with the predictions.
6. The system as claimed in claim 1, wherein said alert module includes a communication interface configured to transmit such warnings and recommendations to healthcare service providers through multiple channels, including email, SMS, and push notifications.
7. The system as claimed in claim 1, wherein said performance evaluation module further comprises a cross-validation submodule configured to partition such multivariate dataset into training and validation sets to ensure the robustness and generalizability of the hybrid deep learning model.
8. The system as claimed in claim 1, wherein said data processing module includes a feature extraction submodule configured to derive additional features from the raw data, such as moving averages and rate of change, to enhance the predictive capability of the hybrid deep learning model.
9. The system as claimed in claim 1, wherein said hybrid deep learning model further comprises an ensemble learning component configured to combine the predictions of multiple models, including LASSO, Ada-Boost, and Light Gradient Boosting, to improve the overall accuracy and reliability of the forecasts.