1. A method for simulating an infectious disease outbreak, comprising: receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results.

2. The method of claim 1, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.

3. The method of claim 1, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.

4. The method of claim 1, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.

5. A system for simulating an infectious disease outbreak, comprising: a processor, memory, and a user interface, wherein the memory stores computer-readable instructions for simulating an infectious disease outbreak, and wherein the processor executes the computer-readable instructions to receive data related to the infectious disease outbreak, generate a simulation model based on the received data, run the simulation model to simulate the infectious disease outbreak, and output simulation results via the user interface.

6. The system of claim 5, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.

7. The system of claim 5, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.

8. The system of claim 5, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak. Infectious Disease Outbreak Simulation Abstract This patent relates to a method and system for simulating an infectious disease outbreak. The method involves receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results. The received data can include disease transmission rates, population demographics, disease symptoms, and geographic location. The simulation model can be an agent-based model, a compartmental model, or a network model. The simulation results can include the number of infected individuals, the number of deaths, and the economic impact of the outbreak. , C , Claims:Claims :

1. A method for simulating an infectious disease outbreak, comprising: receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results.

2. The method of claim 1, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.

3. The method of claim 1, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.

4. The method of claim 1, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.

5. A system for simulating an infectious disease outbreak, comprising: a processor, memory, and a user interface, wherein the memory stores computer-readable instructions for simulating an infectious disease outbreak, and wherein the processor executes the computer-readable instructions to receive data related to the infectious disease outbreak, generate a simulation model based on the received data, run the simulation model to simulate the infectious disease outbreak, and output simulation results via the user interface.

6. The system of claim 5, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.

7. The system of claim 5, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.

8. The system of claim 5, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.

Description:Infectious Disease Outbreak Simulation
Field of the Invention
[0001] The present invention relates generally to computer-implemented methods and systems for simulating infectious disease outbreaks, and more specifically to using various models and data to simulate the spread of infectious diseases and predict the impact on populations, healthcare systems, and economies. The invention also includes the use of user interfaces to display simulation results in an easy-to-understand format and the ability to test and evaluate various control measures to mitigate the outbreak.
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] Infectious diseases pose a significant threat to public health and can have far-reaching consequences on populations, healthcare systems, and economies. Traditional methods for studying infectious disease outbreaks involve collecting and analyzing data after the outbreak has occurred. However, this reactive approach can be slow and often fails to capture the dynamic and complex nature of infectious disease transmission.
[0004] Computer simulations offer a proactive approach to studying infectious disease outbreaks by providing a virtual environment in which researchers can test and evaluate the impact of different scenarios on the spread of the disease. Simulations can incorporate various models and data to simulate the transmission of the disease among populations, predict the impact on healthcare systems and economies, and test the effectiveness of different control measures.
[0005] Current simulation techniques, however, often lack accuracy, speed, and flexibility, and are unable to capture the full complexity of the outbreak. Thus, there is a need for improved computer-implemented methods and systems for simulating infectious disease outbreaks that can provide faster, more accurate, and more comprehensive predictions of the impact of outbreaks and the effectiveness of control measures. The present invention aims to address this need by providing novel methods and systems for simulating infectious disease outbreaks using various models and data, and displaying simulation results in an easy-to-understand format.
[0006] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[0007] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[0008] The present invention relates generally to computer-implemented methods and systems for simulating infectious disease outbreaks, and more specifically to using various models and data to simulate the spread of infectious diseases and predict the impact on populations, healthcare systems, and economies. The invention also includes the use of user interfaces to display simulation results in an easy-to-understand format and the ability to test and evaluate various control measures to mitigate the outbreak.
[0009] This patent outlines a method and system for simulating an infectious disease outbreak, aiming to provide a comprehensive solution for predicting and analyzing the spread of infectious diseases within a population. The simulation process includes receiving data related to the outbreak, generating a simulation model based on the received data, running the model to simulate the outbreak, and outputting the simulation results.
[00010] The received data can comprise various elements, such as disease transmission rates, population demographics, disease symptoms, and geographic location. These elements are crucial in developing an accurate and reliable simulation model that reflects the real-world outbreak dynamics. The simulation model can be based on different approaches, including agent-based models, compartmental models, and network models, allowing for flexibility in modeling the complex dynamics of infectious disease outbreaks.
[00011] The simulation results provide valuable insights into the spread and impact of the outbreak, including the number of infected individuals, the number of deaths, and the economic impact of the outbreak. These results can be instrumental for public health officials, researchers, and policymakers in making informed decisions about intervention strategies and resource allocation during an outbreak.
[00012] The patent also describes a system for simulating an infectious disease outbreak, consisting of a processor, memory, and a user interface. The memory stores computer-readable instructions for simulating the outbreak, which the processor executes. The system receives data related to the outbreak, generates a simulation model based on the received data, runs the model to simulate the outbreak, and outputs the simulation results via the user interface.
[00013] Similar to the method, the system's received data can include disease transmission rates, population demographics, disease symptoms, and geographic location. The simulation model can consist of an agent-based model, a compartmental model, or a network model. The simulation results generated by the system can provide insights into the number of infected individuals, the number of deaths, and the economic impact of the outbreak.
[00014] The patent's method and system for simulating an infectious disease outbreak present a versatile and powerful tool for understanding and managing public health crises. By incorporating various data sources and modeling approaches, the method and system offer an integrated solution for predicting the spread of infectious diseases and assessing the impact of different intervention strategies.
[00015] In conclusion, this patent presents a method and system for simulating an infectious disease outbreak that can significantly contribute to improving public health responses and decision-making during such crises. By combining data collection, simulation modeling, and output analysis, the method and system provide an essential tool for researchers, public health officials, and policymakers to better understand, predict, and mitigate the spread of infectious diseases within a population.
Brief Description of the Drawings
[00016] 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:
[00017] FIG. 1 represents an exemplary architecture of system for data analysis to predict disease outbrake, according to some embodiments of the present disclosure.
[00018] FIG. 2 is a flowchart illustrating a method for simulating an infectious disease outbreak, according to some embodiments of the present disclosure.
Detailed Description
[00019] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00020] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00021] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00022] The present invention relates generally to computer-implemented methods and systems for simulating infectious disease outbreaks, and more specifically to using various models and data to simulate the spread of infectious diseases and predict the impact on populations, healthcare systems, and economies. The invention also includes the use of user interfaces to display simulation results in an easy-to-understand format and the ability to test and evaluate various control measures to mitigate the outbreak.
[00023] A system 100 for simulating an infectious disease outbreak is a computer-implemented tool designed to predict and analyze the spread of infectious diseases within a population. The system is composed of a processor 102, a memory 104, and a user interface 106. It uses data related to the infectious disease outbreak to generate a simulation model that can be executed by the processor. The simulation results are then presented to users through the user interface.
[00024] The first embodiment of the system includes a data collection and integration module, which is responsible for gathering relevant data related to the infectious disease outbreak. This data can include information about the infectious agent (e.g., its transmission mode, incubation period, and infection rate), demographic information of the affected population, geographical data, and data on existing control measures. The data can be collected from various sources, such as public health databases, research publications, and real-time monitoring systems.
[00025] In the second embodiment, the system features a preprocessing and model parameterization module. This module processes the collected data to generate input parameters for the simulation model. Preprocessing may involve data cleaning, normalization, and feature extraction. The model parameterization involves converting the preprocessed data into a format that can be used by the simulation model. This can include defining the initial number of infected individuals, the transmission rates, and the spatial distribution of the population.
[00026] The third embodiment includes the development of the simulation model itself. The model is designed to simulate the spread of the infectious disease within a given population over time. It can be based on various mathematical and computational models, such as agent-based models, compartmental models, or network models. The model can be programmed in different programming languages, such as Python, R, or C++. The model takes the input parameters generated by the preprocessing and model parameterization module to run simulations that capture the complex dynamics of infectious disease outbreaks.
[00027] The fourth embodiment encompasses model calibration and validation. Model calibration is the process of adjusting the model parameters to accurately represent the real-world outbreak. This can be achieved through techniques such as optimization algorithms, sensitivity analysis, or expert opinion. Model validation involves comparing the simulation results to real-world data to determine the model's accuracy and reliability. The system can use various statistical measures, such as the mean squared error or the coefficient of determination, to assess the model's performance.
[00028] In the fifth embodiment, the system incorporates a scenario analysis and policy evaluation module. This module allows users to test different intervention strategies, such as vaccination campaigns, social distancing measures, or travel restrictions, within the simulation model. Users can input various intervention parameters, and the system will run simulations to assess the effectiveness of the proposed interventions in controlling the outbreak. This module can be helpful for public health officials and policymakers in making informed decisions during an infectious disease outbreak.
[00029] The sixth embodiment focuses on the user interface and visualization components of the system. The user interface can be a graphical user interface (GUI) or a command-line interface (CLI), allowing users to interact with the system and input relevant data. The visualization component can generate graphical representations of the simulation results, such as charts, maps, or animations. These visualizations can help users better understand the simulation outcomes and make informed decisions.
[00030] The seventh embodiment involves implementing the system on a cloud-based platform, enabling users to access the system remotely and run simulations on demand. This implementation can improve scalability, reduce computational requirements for end-users, and facilitate collaboration between researchers and decision-makers. Additionally, the system can provide an Application Programming Interface (API) that allows other applications and services to integrate with the system. This API can enable the development of custom tools and applications, further enhancing the system's capabilities and usefulness for users.
[00031] The eighth embodiment includes real-time data integration and monitoring capabilities. The system can be connected to external data sources, such as social media feeds, news sources, or public health databases, to continuously update the simulation model with real-time information. This feature can help the system adapt to changing conditions and provide more accurate and timely predictions of infectious disease outbreaks.
[00032] In the ninth embodiment, the system incorporates machine learning and artificial intelligence techniques to improve the accuracy and efficiency of the simulation model. These techniques can be used in various aspects of the system, such as data preprocessing, model calibration, or scenario analysis. For example, machine learning algorithms can be used to identify patterns in the data or to optimize model parameters. Artificial intelligence techniques can be employed to automate the selection of appropriate intervention strategies or to predict the potential impact of new infectious agents.
[00033] In summary, the system for simulating infectious disease outbreaks is a powerful and versatile tool for understanding and managing public health crises. By combining data collection, preprocessing, simulation modeling, calibration, validation, scenario analysis, and visualization, the system provides an integrated solution for predicting and analyzing the spread of infectious diseases within a population. Additionally, with the implementation of cloud-based services, real-time data integration, and advanced machine learning and artificial intelligence techniques, the system offers a robust and cutting-edge platform for researchers, public health officials, and policymakers to make informed decisions in the face of an infectious disease outbreak.
[00034] In an embodiment, the system is designed to simulate an infectious disease outbreak using a variety of input data. This includes at least one of disease transmission rates, which provide information on how easily the disease is spread between individuals; population demographics, which help to determine the size and composition of the at-risk population; disease symptoms, which provide information on how the disease manifests and can help to identify cases; and geographic location, which can affect the spread of the disease and the availability of resources for response.
[00035] In an embodiment, the simulation model used in the system can be one of several types, including an agent-based model, a compartmental model, or a network model. An agent-based model simulates the behavior of individual agents, such as people, and how they interact with one another in the spread of the disease. A compartmental model divides the population into compartments based on their disease status, such as susceptible, infected, or recovered. A network model considers the structure of the population and how people are connected to one another, such as through social networks or transportation systems.
[00036] In an embodiment, the simulation results generated by the system of can include a variety of outputs, including the number of infected individuals, the number of deaths, and the economic impact of the outbreak. These results can help decision-makers and public health officials to better understand the potential impacts of an infectious disease outbreak and to plan and respond accordingly. By using a variety of input data and simulation models, the system can provide valuable insights into the potential outcomes of different scenarios, helping to inform effective public health interventions and policies.
[00037] An exemplary use case for the system for simulating an infectious disease outbreak could be to assess the impact of a new strain of influenza on a large city. Public health officials can input data related to the outbreak, such as transmission rates, population demographics, and disease symptoms, into the system to generate a simulation model. The simulation can be run to simulate the spread of the disease and its potential impact on the population. The system can output simulation results via the user interface, which can include the number of infected individuals, the number of deaths, and the economic impact of the outbreak.
[00038] Based on the simulation results, public health officials can make informed decisions about interventions to mitigate the impact of the outbreak. For example, the simulation may show that implementing social distancing measures and mass vaccination campaigns could significantly reduce the number of infected individuals and deaths. The simulation results can also help officials allocate resources more effectively, such as hospital beds, medical supplies, and personnel.
[00039] Furthermore, the system can be used to test different scenarios and interventions, such as the effectiveness of school closures, travel restrictions, or increased testing capacity. The results of these simulations can inform policy decisions and guide public health officials in developing effective strategies to mitigate the impact of an infectious disease outbreak.
[00040] In summary, the system for simulating an infectious disease outbreak can provide valuable insights into the potential impact of an outbreak and inform effective public health interventions and policies. It can be a valuable tool for public health officials and decision-makers in assessing the risks and developing strategies to prevent or mitigate the spread of an infectious disease.
[00041] Simulating an infectious disease outbreak is an essential tool in public health to understand the spread and impact of a disease in a population. A method 200 for simulating an infectious disease outbreak typically involves the following steps: The step 202 is to collect data related to the outbreak, such as the number of reported cases, the demographic characteristics of the affected population, the geographic distribution of the outbreak, and any other relevant information. At step 204, once the data is collected, a simulation model is created based on the data. The model is typically designed to represent the key features of the disease, such as the rate of transmission, the incubation period, the severity of symptoms, and the duration of illness. At step 206, once the simulation model is created, it is used to simulate the spread of the disease. The simulation typically involves running a large number of iterations to model different scenarios and to account for the inherent variability in disease transmission and population behavior. The simulation may also incorporate interventions such as vaccination, quarantine, or social distancing measures. At step 208, analyze and interpret the simulation results. The output may include a range of outcomes, such as the total number of cases, the number of hospitalizations, the mortality rate, and the economic impact of the outbreak. The results can be used to inform public health policy decisions and to guide the allocation of resources.
[00042] In an embodiment, the method involves using a simulation model to predict the impact of an outbreak of a disease. The simulation model is provided with data that includes at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.
[00043] The simulation model used in this method can be any one of an agent-based model, a compartmental model, or a network model. An agent-based model is a type of simulation model that represents individual agents and their interactions with each other and the environment. A compartmental model divides the population into compartments based on their disease status, such as susceptible, infected, and recovered. A network model represents the interactions between individuals in a network.
[00044] The simulation results generated by the model can include the number of infected individuals, the number of deaths, and the economic impact of the outbreak. These results can be used to guide decision-making in managing the outbreak, such as implementing control measures or allocating resources.
[00045]
[00046] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00047] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00048] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00049] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00050] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00051] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A method for simulating an infectious disease outbreak, comprising: receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results.
2. The method of claim 1, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.
3. The method of claim 1, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.
4. The method of claim 1, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.
5. A system for simulating an infectious disease outbreak, comprising: a processor, memory, and a user interface, wherein the memory stores computer-readable instructions for simulating an infectious disease outbreak, and wherein the processor executes the computer-readable instructions to receive data related to the infectious disease outbreak, generate a simulation model based on the received data, run the simulation model to simulate the infectious disease outbreak, and output simulation results via the user interface.
6. The system of claim 5, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.
7. The system of claim 5, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.
8. The system of claim 5, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.

Infectious Disease Outbreak Simulation
Abstract
This patent relates to a method and system for simulating an infectious disease outbreak. The method involves receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results. The received data can include disease transmission rates, population demographics, disease symptoms, and geographic location. The simulation model can be an agent-based model, a compartmental model, or a network model. The simulation results can include the number of infected individuals, the number of deaths, and the economic impact of the outbreak. , C , Claims:Claims
I/We Claim:
1. A method for simulating an infectious disease outbreak, comprising: receiving data related to the infectious disease outbreak, generating a simulation model based on the received data, running the simulation model to simulate the infectious disease outbreak, and outputting simulation results.
2. The method of claim 1, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.
3. The method of claim 1, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.
4. The method of claim 1, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.
5. A system for simulating an infectious disease outbreak, comprising: a processor, memory, and a user interface, wherein the memory stores computer-readable instructions for simulating an infectious disease outbreak, and wherein the processor executes the computer-readable instructions to receive data related to the infectious disease outbreak, generate a simulation model based on the received data, run the simulation model to simulate the infectious disease outbreak, and output simulation results via the user interface.
6. The system of claim 5, wherein the received data comprises at least one of disease transmission rates, population demographics, disease symptoms, and geographic location.
7. The system of claim 5, wherein the simulation model comprises at least one of an agent-based model, a compartmental model, and a network model.
8. The system of claim 5, wherein the simulation results comprise at least one of the number of infected individuals, the number of deaths, and the economic impact of the outbreak.