WO2021250456A1 - System and method to detect a disease outbreak - Google Patents

Abstract

System and method to detect a disease outbreak is disclosed. The system includes at least one image capturing unit configured to capture one of a video, one or more images of people, one or more processors. The processor includes an image analysis module configured to analyse at least one of one or more symptoms of the disease, 5 one or more facial expressions of the people, one or more behaviours of the people and to generate an analysis result, a disease risk detection module configured to determine a risk factor representative of the disease for each of the people, a disease spread detection module configured to determine a rate of spread of the disease, a remote assistance module configured to generate at least one holographic image of at 10 least one medical assistant to remotely assist the people.

Description

SYSTEM AND METHOD TO DETECT A DISEASE OUTBREAK

This International Application claims priority from a Complete patent application filed in India having Patent Application No. 202021024543, filed on June 11, 2020, and titled “SYSTEM AND METHOD TO DETECT A DISEASE OUTBREAK”.

FIELD OF INVENTION

Embodiments of a present invention relate to detection of disease, and more particularly, to a system and method to detect a disease outbreak.

BACKGROUND

In the recent days, one of the newest diseases by name COVID-19 was discovered which spreads through saliva or any similar bodily mucus. On December 8, 2019, the first new coronavirus case was discovered, and an intensive outbreak incepted in the followed days. Virologicalists and epidemiologists predicted that it would reach a peak in about 90 days and fade away till the end in about 4 months. However, the rate of cases is increasing rapidly everyday across the globe. The number of deaths is also increasing due the disease. However, until now the reliable technique to determine an infected person is through Swab Test, Nasal aspirate, Tracheal aspirate, Sputum Test, and Blood test. All these testing require elaborate testing kits and trained professionals to use the same, which makes the process of testing costly and difficult to deploy in areas having limited resources. Further, such testing kits need to stored in a proper manner to ensure the accuracy of results. In most of the cases of affected people, the symptoms of the disease are also rarely seen, which is a reason for huge outbreak of such disease. Due to such limitations, the detection, prevention and cure of such disease is becoming challenging at a global level. Also, the spread of the disease cannot be controlled instantaneously in such situations through such conventional methods.

Hence there is a need for an improved system and method to detect a disease outbreak to address the aforementioned issues. BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system to detect a disease outbreak is disclosed. The system includes at least one image capturing unit installed in a pre-defined location. The at least one image capturing unit is configured to capture one of a video, one or more images or a combination thereof of people within the pre defined location, at every pre-defined time interval. The system also includes one or more processors operatively coupled to the at least one image capturing unit. The processor includes an image analysis module configured to analyse at least one of one or more symptoms of the disease upon analysing one of the video, the one or more images, or a combination thereof, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof, using the artificial intelligence technique to detect the disease outbreak within the pre-defined location. The image analysis module is also configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people. The processor also includes a disease risk detection module operatively coupled to the image analysis module. The disease risk detection module is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location. The processor also includes a disease spread detection module operatively coupled to the disease risk detection module. The disease spread detection module is configured to determine a rate of spread of the disease from the affected people to the disease-free people of people in real time. The processor also includes a remote assistance module operatively coupled to the disease spread detection module. The remote assistance module is configured to generate at least one holographic image of at least one medical assistant to remotely assist one of the disease affected people, the disease free people, or a combination thereof among the people within the pre-defined location.

In accordance of another embodiment of the disclosure, a method for detecting a disease outbreak is disclosed. The method includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval. The method also includes analysing one of the video, the one or more images or a combination thereof of the people within the pre-defined location using an artificial intelligence technique. The method also includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof, using the artificial intelligence technique for detecting the disease outbreak within the pre defined location. The method also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people. The method also includes determining a risk factor representative of the disease for each of the people within the pre-defined location. The method also includes determining a rate of spread of the disease from the affected people to the disease-free people of people in real time. The method also includes generating at least one holographic image of at least one medical assistant to remotely assist one of the disease-affected people, the disease free people, or a combination thereof among the people within the pre-defined location.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representing a system to detect a disease outbreak in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of an exemplary embodiment of the system in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure; and FIG. 4 is a flow chart representing steps involved in a method for detecting a disease outbreak in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system and method for detecting a disease outbreak. As used herein, the term ‘disease’ is defined as an abnormal condition which affects normal structure functioning of the body which is usually caused by external foreign bodies. In one exemplary embodiment, the disease may be a coronavirus or the like. In such embodiment, the disease is contagious from one from to another through saliva or any such bodily fluids. In the present disclosure, the disease is referred to a contagious disease.

FIG. 1 is a block diagram representing a system (10) to detect a disease outbreak in accordance with an embodiment of the present disclosure. The system (10) includes at least one image capturing unit (20) installed in a pre-defined location. In one embodiment, the at least one image capturing unit (20) may include a camera, a video camera, an infrared (IR) camera, or the like. In one exemplary embodiment, the pre defined location may include an indoor location or an outdoor location of a pre-defined dimension. The at least one image capturing unit (20) is configured to capture one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval. In such embodiment, the people may be located within the pre-defined location. Also, the people may be at stationary, may be in motion within the pre-defined location. In one specific embodiment, the at least one image capturing (20) unit may be coupled to an internet of things (IoT) platform.

Further, the system includes one or more processors (30) operatively coupled to the at least one image capturing unit (20). The one or more processors (30) include an image analysis module (40). The image analysis module (40) is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location captured by the at least one image capturing unit (20), using an artificial intelligence technique. In one embodiment, the analysis of the one or more image or the video may be compared with a pre set of reference data. In another embodiment, the one or more images or the video may be analysed using an image processing technique. In one exemplary embodiment, an analysis technique may be one of a machine learning technique, a deep learning technique, or the like. As used herein, the term “artificial intelligence” is defined as a study of computer algorithms that improve automatically through experience. Also, the term ‘artificial intelligence’ is defined as a technique demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. In one exemplary embodiment, an autonomous model may be used which may be a self-teaching and self-governing model which may be trained using the deep learning technique. Broadly the autonomous model practices existing data to teach the model to detect patterns and later apply the detected patterns on a real time data to execute predictions. Here the autonomous model is trained using the deep learning technique to recognize facial expressions with respect to symptoms of the detected disease based on historic data. The historic data may be collected based on feeding of millions of pictures, images, videos that contain symptoms of the detected disease, this would be one of the categories of symptoms. Further the autonomous model may start building multiple patterns based on one of a plurality of clusters or a plurality of classifications of the one or more images or the video in real time and may compare the built pattern with the pre-defined to determine the disease using one or more layers of an artificial neural network of the machine learning technique the one or more layers may be distinguished based on the plurality of parameters or a plurality of hyperparameters.

The image analysis module (40) is also configured to analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof, using the artificial intelligence technique to detect the disease outbreak within the pre-defined location. In one embodiment, the artificial intelligence technique may be one of the machine learning technique, the artificial intelligence technique, or the like. In another embodiment, the artificial intelligence technique may be the image processing technique. In such embodiment, the one or more images or the video may be divided into a plurality a sectors, and the plurality of sectors may be compared with a pre defined set of reference data to identify the symptoms associated with the disease. In such embodiment, the facial expression may include restlessness, anxiety, or the like. The image analysis module (40) may also be configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people. In one embodiment, the analysis result may data representing a number of disease-affected people, number of disease -free people among the people within the pre-defined location.

Furthermore, the processor (30) includes a disease risk detection module (50) operatively coupled to the image analysis module (40). The disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location. In one embodiment, the risk factor may be a percentage determining a change to get the disease for the disease-free people from the disease affected people within the pre-defined location at an instant of time. In one exemplary embodiment, the risk factor may be determined using one of the machine learning technique, the artificial intelligence technique, or the like. In one exemplary embodiment, the at least one image capturing unit (20) may be an IoT device. The one or more images or videos captured by such a device may be installed with the artificial intelligence technique. In such embodiment, a depth-map of face of the people within the location is created using the AI neural network technique based on various data points identified. Furthermore, using the one or more images and the data points, multiple network layers are created in order to identify the plurality of parameters and the plurality of hyper parameters. The plurality of hyper parameters mean the configurable values which can be configured as per the pandemic symptoms occurred, parameters are the learned values that are the weightage realized while comparing the symptoms and self-learning process continues using the real time data and the historic data respectively. In one exemplary embodiment, the risk factor may be based on and is directly proportional to one of one or more real time identified symptoms, the one or more facial expressions, rate of spread of the disease or a combination thereof.

The processor (30) also includes a disease spread detection module (60) operatively coupled to the disease risk detection module (50). The disease spread detection module (60) is configured to determine a rate of spread of the disease from the affected people to the disease-free people of people in real time In one embodiment, the rate of spread of the disease may be determined based on the risk factor. In such embodiment, the rate of spread of the disease may be associated to rate of disease-free people catching the disease within a set interval of time.

The processor (30) also includes a remote assistance module (70) operatively coupled to the disease spread detection module (60). The remote assistance module (70) is configured to generate at least one holographic image of at least one medical assistant to remotely assist one of the disease affected people, the disease free people, or a combination thereof among the people within the pre-defined location. As used herein, the term ‘holographic image’ may be defined as photographic recording of a light field, rather than an image formed by a lens in a holographic medium. In one embodiment, the at least one medical assistant may include a doctor, a nurse, a healthcare assistant, or a combination thereof. In one exemplary embodiment, the assistance by the holographic image is integrated with audio to guide the people within the pre-defined location. In one exemplary embodiment, the healthcare assistant may include a hot, a robot, a humanoid, or the like.

In one exemplary embodiment, the processor (30) may further include an alert generation module operatively coupled to the disease spread detection module (60). The alert generation module may be configured to generate an alert notification for one of the people, the at least one medical assistant, at least one emergency assistant or a combination thereof based on one of a determined risk factor, the rate of spread of the disease of a combination thereof. In one exemplary embodiment, the alert notification may include a text notification, a voice notification, a multimedia notification on a computing device of one of the people, the at least one medical assistant, the at least one emergency assistant or a combination thereof. In another exemplary embodiment, the alert notification may be in a form of SMS, email, push notifications, InApp notifications, social media channels, and the like. In one exemplary embodiment, the computing device may be a portable device or a handheld device such as a tablet, a mobile phone, a laptop or the like.

In one exemplary embodiment, the disease detection module (40) may be configured to detect a stage of the disease upon detecting at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the risk factor or a combination thereof. In such embodiment, the stage may be categorised into high, medium and low categories respectively. In one embodiment, the high risk may be defined when the rate of spread calculated against the people is high which may be greater than about 10%. In another embodiment, the medium risk may be defined when the rate of spread calculated against the people is medium which may be between about 5% to 10%. In yet another embodiment, the low risk may be defined when the rate of spread calculated against the people is high which may be lesser than about

5%.

FIG. 2 is a block diagram representation of an exemplary embodiment of the system (80) in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure. In the health care unit, consider a number of people to be ‘X’, out of which ‘N’ be a number of disease-affected people. Also, the health care unit may include 4 cameras (20) installed within the health care unit in four different locations within the health care unit. The 4 cameras (20) capture a plurality of images of the X people at every instant of time. The captured images of X people are analysed using an artificial intelligence technique by an image analysis module of a processor (30).

Further upon analysing the plurality of images symptoms of the disease is analysed based on one or more facial expressions of the X people, one or more behaviours of the X people using the artificial intelligence technique using the image analysis module (40). Upon analysing the plurality of images, an analysis report is generated by the image analysis module (40).

Furthermore, based on the analysis result, a risk factor for the spread of the disease from the N disease-affected people to the other disease-free people of the X people is determined by a disease risk detection module (50) within the health care unit. On determining the risk factor, rate of spread of disease from the N number of disease- affected people to other disease-free people is determined by a disease spread detection module (60). Further, on determining the above said factors, a holographic image (90) a doctor is generated by a remote assistance module (70) to remotely assist the N disease-affected people and helping the disease-free people to take the required measures from not being affected by the disease when the people are within the health care unit. Consequently, an alert notification is generated by an alert generation module (100) for all the X people within the health care unit depending on the medical condition of each of the X people. The generated notification is transmitted to a computing device of each of corresponding the X people. The notification may include the medical condition of the person, the risk factor of being affected by the disease within the health care unit, the rate of spread of the disease with the health care unit, the precautionary measure to be taken to prevent the disease from being affected, the treatment for the disease affected people and / or measure to avoid spread of the disease from the N number of disease-affected people to other disease-free people within the health care unit.

Furthermore, the camera (20), the processor (30), the image analysis module (40), the disease risk detection module (50), the disease spread detection module (60), the remote assistance module (70) are substantially similar to at least one image capturing module (20), a processor (30), an image analysis module (40), a disease risk detection module (50), a disease spread detection module (60), a remote assistance module (70) of FIG. 1.

FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (120), and memory (130) operatively coupled to the bus (140).

The processor(s) (120), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (130) includes a plurality of modules stored in the form of executable program which instructs the processor (120) to perform the method steps illustrated in FIG. 4. The memory (130) is substantially similar to the system (10) of FIG.l. The memory (130) has the following modules: an image analysis module (40), a disease risk detection module (50), a disease spread detection module (60), a remote assistance module (70).

The image analysis (40) module is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location, analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof and to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people.

The disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location. The disease spread detection module (60) configured to determine a rate of spread of the disease from the affected people to the disease-free people of people. The remote assistance module (70) configured to generate at least one holographic image of at least one medical assistant to remotely assist one of the disease-affected people, the disease-free people, or a combination thereof.

FIG. 4 is a flow chart representing steps involved in a method (150) for detecting a disease outbreak in accordance with an embodiment of the present disclosure. The method (150) includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval in step 160. In one embodiment, capturing one of a video, one or more images comprises capturing one of a video, one or more images using at least one image capturing unit.

Furthermore, the method (150) includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the videos, the one or more images or the combination thereof, using the artificial intelligence technique for detecting the disease outbreak within the pre-defined location in step 180. In one embodiment, analysing one of the video, the one or more images may include analysing one of the video, the one or more images by an image analysis module. In one exemplary embodiment, analysing one of the video, the one or more images may include analysing one of the video, the one or more images using one of a machine learning technique, an artificial intelligence technique or the like. In one embodiment, analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof may include analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof by the image analysis module. In such embodiment, the artificial intelligence technique may include one of the machine learning technique, the artificial intelligence technique or the like.

The method (150) also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in step 190. In one embodiment, generating the analysis result may include generating the analysis result by the image analysis module. In one exemplary embodiment, generating the analysis result may include generating the analysis result including data representing a number of disease-affected people, number of disease - free people among the people within the pre-defined location.

The method (150) also includes determining a risk factor representative of the disease for each of the people within the pre-defined location in step 200. In one embodiment, determining the risk factor may include determining the risk factor by a disease risk detection module. The method (150) also includes determining a rate of spread of the disease from the affected people to the disease-free people of people in step 210. In one embodiment, determining the rate of spread of the disease may include determining the rate of spread of the disease by a disease spread detection module.

Furthermore, the method (150) includes generating at least one holographic image of at least one medical assistant to remotely assist one of the disease affected people, the disease free people, or a combination thereof among the people within the pre-defined location in step 220. In one embodiment, generating the at least one holographic image may include generating the at least one holographic image by a remote assistance module. In one exemplary embodiment, generating the at least one holographic image of the at least one medical assistant may include generating the at least one holographic images of a doctor, a nurse, a healthcare assistant, or a combination thereof.

In one exemplary embodiment, the method (150) may further include generating an alert notification for one of the people, the at least one medical assistant, at least one emergency assistant or a combination thereof based on one of a determined risk factor, the rate of spread of the disease of a combination thereof. In such embodiment, generating the alert notification may include generating a text notification, a voice notification, a multimedia notification on a computing device of one of the people, the at least one medical assistant, the at least one emergency assistant or a combination thereof.

Various embodiments of the present disclosure enable the system and method to detect a disease outbreak enables the system to determine the disease-affected people in real time. The system also measures the risk of spread of the disease among healthier people and also the rate of spread of such deadly diseases. In addition, the system can generate a real time guidance and precautionary measures for people within a particular geographical sector. Also, upon determining the conditions in real time, the system also intimates the people within the particular geographical sector and the required authorities regarding the disease outbreak in real time, thereby making the system more efficient and reliable as the system uses machine learning or artificial intelligence techniques to function in real time. Further, the system can be implemented in areas like customer communication, health care, city administration, police, robotic process automation, water supply, electricity, fintech, smart speakers and the like.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

/ WE CLAIM:

1. A system (10) to detect a disease outbreak comprising: at least one image capturing unit (20) installed in a pre-defined location, and configured to capture one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval; one or more processors (30) operatively coupled to the at least one image capturing unit (20), wherein the one or more processors (30) comprises: a disease detection module (40) configured to: analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique to detect the disease within the pre defined location; and generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in real time; a disease risk detection module (50) operatively coupled to the disease detection module (40), and configured to determine a risk factor corresponding to the disease for each of the people within the pre-defined location in real time; a disease spread detection module (60) operatively coupled to the disease risk detection module (50), and configured to determine contagiosity of the disease from the affected people to the disease-free people of people in real time; and a remote assistance module (70) operatively coupled to the disease spread detection module (60), and configured to generate at least one holographic image of at least one medical assistant to remotely assist one of the disease affected people, the disease free people, or a combination thereof among the people within the pre-defined location;

2. The system (10) as claimed in claim 1, wherein the at least one medical assistant comprises a doctor, a nurse, a healthcare assistant, or a combination thereof.

3. The system (10) as claimed in claim 1, wherein the one or more processors (30) comprises an alert generation module operatively coupled to the disease spread detection module (60), and configured to generate an alert notification for one of the people, the at least one medical assistant, at least one emergency assistant or a combination thereof based on one of a determined risk factor, the rate of spread of the disease or a combination thereof.

4. The system (10) as claimed in claim 3, wherein the alert notification comprises a text notification, a voice notification, a multimedia notification on a computing device of one of the people, the at least one medical assistant, the at least one emergency assistant or a combination thereof.

5. The system (10) as claimed in claim 1, wherein the disease detection module (40) is configured to detect a stage of the disease upon detecting at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the risk factor or a combination thereof.

6. A method (150) for detecting a disease outbreak comprising: capturing, by at least one image capturing unit, one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval; (160) analysing, by the image analysis module, at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique for detecting the disease outbreak within the pre-defined location; (180) generating, by the image analysis module, an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people; (190) determining, by a disease risk detection module, a risk factor representative of the disease for each of the people within the pre-defined location; (200) determining, by a disease spread detection module, a rate of spread of the disease from the affected people to the disease-free people of people; and (210) generating, by a remote assistance module, at least one holographic image of at least one medical assistant to remotely assist one of the disease affected people, the disease free people, or a combination thereof among the people within the pre-defined location. (220)

7. The method (150) as claimed in claim 6, wherein generating the at least one holographic image of the at least one medical assistant comprises generating the at least one holographic images of a doctor, a nurse, a healthcare assistant, or a combination thereof.

8. The method (150) as claimed in claim 6, comprising generating, by an alert generation module, an alert notification for one of the people, the at least one medical assistant, at least one emergency assistant or a combination thereof based on one of a determined risk factor, the rate of spread of the disease of a combination thereof.

9. The method (150) as claimed in claim 8, wherein generating the alert notification comprises generating a text notification, a voice notification, a multimedia notification on a computing device of one of the people, the at least one medical assistant, the at least one emergency assistant or a combination thereof.