System and methods for survei l lance of epidemic networks by telemetry of infectious node estimate location
TECHNICAL FIELD
This invention relates to the geographic survei l lance of inf luenza- 1 ike i l lness symptoms as an early warning system for epidemic and pandemic, and specifical ly to a system and methods for the mobi le detection of such symptoms , and the visual ized analysis of the aggregated information of such symptom occurrences to improve operational response.
BACKGROUND ART
The CoVID-19 pandemic, which is sti l l in progress in many countries at the time of writing this disclosure, reveals a vulnerabi l ity of the human population that remains unchecked since a century earl ier with a related infection ( the“Spanish flu” ) . The pandemic is overwhelming the capacity of healthcare institutions to an immense level that prompted quarantines and lockdowns in many countries worldwide. However , this operational response has been economical ly disruptive as most businesses remain closed for an extended period, suspending most of the employment . The transition away from the restrictions to human mobi l ity is fraught with uncertainty because of the inabi l ity to identify where to start l ifting those restrictions . The healthcare system could have averted the epidemic if it had captured the spread of infection by knowing where and when the onset occurred. The transitions from restrictions of human mobi l ity would have also been less uncertain if governments could local ize those areas where such restrictions should remain. Although mass testing can identify which individuals to keep in isolation, the time-consuming process of obtaining test results leads to inaction that cost l ives . Thus , a decision-support tool that can visual ize real -time and accurate information about the infection must be in place.
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SUBSTITUTE SHEETS (RULE 26) Researchers on predictive epidemic models would also benefit from a data col lection, processing and mapping tool that would help them better understand the dynamics of spread across a variety of cultures , economies , and geopol itical jurisdictions , and make proper proj ections of the epidemic curve .
China was the epicenter of the CoVID-19 pandemic, so it was the first nation that had to come up with a solution toward managing the crisis . Indeed, a few recent patent appl ications focused on a monitoring system to constrain the spread of the virus . Patent Appl ication No. CN111063449A discloses an epidemic control system based on“big data;” however , the data source is simi lar to conventional sentinel survei l lance, the rel iabi l ity, and regularity of updates of which can be questionable due to“manipulation and secrecy by governments” (quoted from U.S. Pat . No. 7,840,421) . Also, the prediction of outbreak comes from a mathematical model , simi lar to the method disclosed in U.S. Pat . No. 9,075,909, of which the assumptions of human dynamics , such as travel behavior and contact networks , may not be entirely val id. Patent Appl ication No. CN110926656A discloses a data acquisition method using a wearable temperature sensor with a tracker . However , the body temperature is not sufficient for ILI detection, so it must corroborate with another symptom, such as a cough. Patent Appl ication No. CN110993119A discloses an epidemic prediction method based on human population migration. However , the technique could not capture infections arising from rare symptom events because it rel ies on en masse information. Rare symptom events almost always relate to the epidemic onset ; hence, the disclosed method cannot detect potential outbreaks at its early stages . The three Chinese patent appl ications , although inspired by the ongoing CoVID-19 pandemic, do not employ the mobi le detection of symptoms and visual ized analysis of the epidemic picture.
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SUBSTITUTE SHEETS (RULE 26) The use of sensors for the acquisition of primary ILI symptom information is crucial for the detection of potential outbreaks at the onset. The collection of information through sensors from various sources, in space and time, is also necessary to establish a geographic picture of spreading likelihood. U.S. Pat. No. 10,362,769 discloses a system and method for collecting ILI symptoms, particularly cough sounds, from mammalian animals in confinement; hence, the sensors are fixed in the vicinity of the confined area and analyzing the geographic spread of infection would be irrelevant. In contrast, U.S. Pat. No. 10,275,526 discloses a system of obtaining geographic information of disease. However, the data source is secondary, in the form of text-based broadcast messaging over social media. The method relies on access to“user broadcasting” platforms over the Internet. It is language- dependent because it filters words as inputs in contrast to ILI sounds that are universal regardless of language. Although both prior arts represent viable methods for ILI tracking and outbreak detection, they both lack the combined features required for real-time telemetry of symptoms that provide for visualized analysis to improve operational response.
The usual method of sensing body temperature relies on heat detection devices, such as thermometers or thermographs, as disclosed in Patent Application Nos. CN109378079A, CN108695003A, and TWI570661B, which would be obtrusive if integrated with mobile computing devices. Integration may lead to inaccurate temperature readings as the computing device itself emits heat, which is the reason why devices like smartphones interfere with heat detection. An indirect, but effective, means of determining the body temperature relies on the relationship between body temperature and heartbeats per minute (bpm). The technique of determining fever temperature from heart rate has been known in the medical literature [Karjalainen & Vi i tasalo ( 1986) , https://dx.doi.org/10.1001/archinte.1986.00360180179026; Davies & Maconochie (2009), https://dx.doi.org/10.1136/emj.2008.061598/;
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SUBSTITUTE SHEETS (RULE 26) Jensen et al. (2019), https://pubmed.ncbi.nlm.nih.gov/31536050/; Kirschen et al. (2019), https://dx.doi.Org/10.1016/j.ajem.2019.158355], which report a 1.0-degree centigrade increase in temperature for about 7 to 8 bpm average increase in heart rate. More recent evidence on the applicability of the technique appears in medical studies that utilize wearable heart-rate sensors to estimate the body temperature [Viboud & Santillana (2020), https : //dx.doi .org/10.1016/S2589 - 7500( 19)3024191 ; Zhu et al. (2020), https://dx.doi.org/10.1155/2020/6152041]. Open-source designs for heart-rate sensors that work according to the principles of photoplethysmography also exist. Thus, a mobile solution for detecting fever would involve the use of unobtrusive heart-rate sensors that communicate with a mobile computing device .
Lastly, a method that passively detects the ILI symptom events with minimal operation from the user of a mobile computing device would be beneficial. Many existing technologies require the user to consciously provide information on symptoms, as disclosed in U.S. Pat. No. 10.430,904, A passive method will address non-compliance.
SUMMARY OF INVENTIONS
Embodiments described in this disclosure provide a system and methods for the geographic surveillance and early detection of epidemic or pandemic arising from ILI-causing pathogens.
In one exemplary embodiment, the present disclosure relates to a method for the surveillance of ILI symptoms across a human population comprising: continuously receiving, from a plurality of data-transceiving mobile computing devices, information of the occurrence of ILI symptom events, wherein the information includes the estimate location and time of occurrence, among other attributes, thereof, which each data-transceiving
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SUBSTITUTE SHEETS (RULE 26) mobi le computing device acquires through two or more sensors , wherein the two or more sensors comprise one or more audio sensors and one or more heart - rate sensors ; detecting ILI sounds from the sensed audio information; detecting fever from the sensed heart-rate information; analyzing the col lected information in the GIS database server to determine the potential epicenter and rate of spread of ILI infection; and presenting the analyzed information as a geographic information and early warning map of the "epidemic weather" of a specific human community of any spatial scale; wherein the steps of the method are performed in accordance with a processor- control led server and a memory.
In another embodiment , the present disclosure relates to a system comprising a memory and a processor-control led server computer operatively coupled to the memory and configured to implement the steps of the methods stated in the previous paragraph.
In yet another embodiment , the present disclosure relates to a computer program comprising a non-transitory computer- readable storage medium for storing computer readable program code, which when executed, causes a process-control led server computer to perform the methods stated two paragraphs prior . In another aspect of this embodiment , the present disclosure relates to a computer program product comprising a non-transitory computer readable storage medium for storing computer- readable program code, which when executed, causes a processor-control led mobi le computing device to send information of the occurrence of ILI symptom events through a global computing network to a remote, secure central ized database server .
In this respect , before explaining at least one embodiment of the invention in detai l , it is to be understood that the invention is not l imited in its appl ication to the detai ls of construction and to the arrangements of the
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SUBSTITUTE SHEETS (RULE 26) components set forth in the fol lowing description or i l lustrated in the drawings . The invention is capable of other embodiments and of being practiced and carried out in various ways . Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as l imiting. BRIEF DESCRIPTION OF DRAWINGS FIG. l is a system diagram of components of the invention. FIG. 2 is a block diagram of the data acquisition and transceiving system. FIG. 3 is a flowchart of the sensor data acquisition method. FIG. 4 is a flowchart of the IFI sound detection method. FIG. 5 is a flowchart of the temperature detection method. FIG. 6 is a flowchart of the geoprocessing and mapping method. DESCRIPTION OF EMBODIMENTS This section presents the embodiments of the invention with further detai l on the systems and methods for the geographic survei l lance and early detection of epidemic or pandemic arising from IFI-causing infectious pathogens . The description of the invention appl ies the fol lowing terms and definitions . A ski l led reader should recognize the definitions of each term includes examples of possible alternative definitions and that other components may be incorporated into each definition. “Inf luenza- 1 ike i l lness ( IFI)” is , according to the World Health Organization, having a fever at a temperature not less than 38 degrees
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SUBSTITUTE SHEETS (RULE 26) centigrade and a cough over the last 10 days . It is used as a medical diagnosis of potential influenza infection.
“Data-transceiving mobi le computing device” means an apparatus with the capabi l ity to transmit and receive data, comprising a processor and memory by which it can perform computing tasks . It is sufficiently portable so that its uti l ization is not usual ly confined to one specific location only.
The term“geographic” describes something tangible or abstract on or pertaining to the Earth' s surface, the location of which is known and can be pinpointed on Earth' s map.
The term“survei l lance” means the close and regular observation of something that may appear in many places at the same time or in one place at different times .
The term“epidemic” means a widespread, but not necessari ly global , outbreak of an i l lness across the human population, whi le“pandemic” is an epidemic that is global in scope.
Outbreaks start out in small clusters of infected individuals located in a specific geographic area. The knowledge of the location and time of formation of these clusters remain undetectable by existing sentinel survei l lance systems because infected people usual ly see a doctor only after the symptoms aggravate. Hence, a survei l lance system that senses first -hand information of the ILI symptoms , no matter how mi ld, can provide for an early detection of potential outbreaks . Embodiments of the present invention describe a system and method, which continuously senses the occurrence of coughing, sneezing, and fever using data-transceiving mobi le computing devices . The ubiquity and mobi l ity of these devices al low a network of such devices to cover a lot of ground efficiently in contrast to existing survei l lance
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SUBSTITUTE SHEETS (RULE 26) measures , such as contact tracing and hospi tal -based sentinel methods . The sensors can be bui lt- in or attached to, or stand-alone from the mobi le computing device, and at least include audio and heart-rate sensors . The data from these sensors are combined with the geo- location of and time of receipt by the mobi le computing device as a data vector , which is transmitted through a computing network to a central ized database server . The database server aggregates al l the col lected information, which is accessible by an authorized computing apparatus that executes computer programs that process the data to extract meaningful strategies to contain the infection.
FIG. 1 presents a graphical overview of the system showing two detection events 101 and 102, each triggered by the occurrence of ILI symptoms : cough 111 and sneeze 121 , respectively. In event 101 , the sound of the cough 111 is captured by the audio sensor of a data-transceiving mobi le computing device 112 situated in the vicinity of the source of the cough, e.g. , a distance R 113 , within the reception range of the sensing capabi l ity of device 112. This distance could be determined by the relationship between sound intensity and distance, which is establ ished in the scientific l iterature. The intensity-distance l ink can be encoded as a suitable cal ibration of the audio sensor . A threshold distance comparable to the dimensions of an individual human being is necessary for the definition of the R 113 as a piece-wise function. If the source- receiver distance estimated from the sound detection method 400 is less than the threshold, then the value of R is re-assigned as zero. This case would be interpreted as an instance of sampl ing wherein the capturing device is in contact proximity to the source of the ILI sound. On the other hand, if the distance estimated from the method 400 is such that R > threshold, then the value of R is retained and is taken to be an estimate of the actual distance, such as between the receiving device 112 and the source 111. The device 112 encodes event 101 as a data vector 110 consisting of , at least , the latitude,
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SUBSTITUTE SHEETS (RULE 26) longitude, elevation of the location of the device; the distance R 113 between the device and the cough source; and the time of occurrence of event 101. Data vector 110 is uploaded through a means of wireless communication 114 to a remote database 130.
In event 102, the sound of the sneeze 121 is captured by the audio sensor of the mobi le computing device 122 held by the same person who generated the sneeze. The event 102 is encoded simi larly as a data vector 120 consisting of the latitude, longitude and elevation, and time of occurrence. For this instance, the distance R is zero, and assigned as such, if the source of the ILI symptom is the carrier of the device during the symptom event , or if the source is within a threshold distance ( i . e. , R < threshold) away from the capturing device. The data vector 120 may include body temperature information when the device 122 has a bui lt- in body thermal sensor that could automatical ly estimate the body temperature, as the user holds it or a portion thereof , through the method 500. The data vector 120 is uploaded by wireless communication 123 to the same database 130.
A plural ity of simi lar events as 101 and 102 would undergo the same processes of detection and uploading. In some instances , the devices l ike 112 and 122 need not be held by hand (e.g. , if placed in the pocket or a bag) , or carried by a person (e.g. , if resting on a table) to be capable of detecting any nearby ILI sound. Also, the uploading process 114 and 123 need not be instantaneous , but also possibly a delayed synchronization in the case that the connectivity to the database 130 is non-existent at the time when the devices 112 and 122 captured the ILI symptom events . In yet another possible embodiment , the sensors could be physical ly separate from the data- transceiving mobi le computing device and could be worn by a user or situated in a fixed position within a room. However , such sensors uti l ize wireless network technology to send information to a nearby mobi le computing device
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SUBSTITUTE SHEETS (RULE 26) to complete the synchronization process in the case that the sensors do not have such abi l ity.
The database 130 can refer to a system of col lecting, storing, and retrieving data from a plural ity of sources using wireless means of network communication, such as , but not only, through cloud technology. The database 130 is real -time, thus capable of rapidly processing data on demand; spatial , such that it could store data with multi -dimensional attributes ; and mobi le, as it rel ies on the synchronization with mobi le computing devices . The data vectors l ike 110 and 120 populate the database with the information necessary to form structural representations readi ly avai lable for interpretation. The database 130 can supply data 131 to a computing system 140, which may contain a geographic information system (GIS) software that interacts through an appl ication server . This GIS method 600 can create map visual izations from the stored information and present the raw data points as a scatter plot on top of a geographic map 150. The GIS software may also have the capabi l ity to perform geoprocessing 160, which wi l l manipulate, apply operations onto, and convert the raw datasets into other forms of visual ization of a specific facet or attribute of the geodata, such as , but not l imited to a density plot 170. The output maps , such as 150 and 170, from the geoprocessing 160, can be fed back 132 to the database 130. This feedback al lows the database server to relay the maps to the users ( 115, 124) who demand such data and have authorized access .
The system 200, presented in FIG. 2, acquires data through a set of sensors , at least with an audio sensor 210 and heart-rate sensor 220. These sensors could form part of a sensor module 201 , as an integrated assembly that could be external or bui lt- in to a data-transceiving mobi le computing device consisting at least of the processor 250 and a communication interface 230 that integrates the global navigation satel l ite system receiver (GNSS) 232,
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SUBSTITUTE SHEETS (RULE 26) real -time clock (RTC) 231 , computer memory 240, and a graphical display 260. The communication interface 230 acts as both transmitter and receiver of data, to and from the global computer network (GCN) 270, such as the Internet . In an embodiment wherein the sensor module 201 is an external peripheral apparatus , the said module may be connected to the computing device as a peripheral via the interface 230. In another embodiment wherein the sensor module is bui lt- in, the said module directly l inks to the processor through the interface 230 within the circuit network. In yet another embodiment , the sensor module could be a standalone device that may connect with the mobi le computing device through a wired or wireless network via the communication interface 230. It is also possible that one of either 210 or 220 is external whi le the other is bui lt- in to the mobi le computing device. If external , either 210 or 220 can be in the form of a wearable or implant device. Overal l , the data-transceiving mobi le computing device can take the form of a desktop, laptop, handheld, wearable, or implant apparatus without substantial ly modifying the components and their interactions as a system 200 as described herein.
The sensor module 201 can be activated by manual , semi -automatic, and automatic means . In the manual activation, the user pushes a mechanical part that triggers a circuit to draw the electrical power necessary to operate the devices belonging to the module. In semi -automatic activation, a user triggers software instructions stored in a non-transitory computer- readable medium, which a processor executes , causing the processor to generate control signals that dispense electrical power necessary to operate the devices belonging to the module. In automatic activation, the computing device has the means , through its operating system, to instruct the processor to execute the software instructions , causing it to generate control signals that dispense electrical power necessary to operate the devices belonging to the module .
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SUBSTITUTE SHEETS (RULE 26) The audio sensor 210 can be in a form of an electromechanical transducer , such as a microphone, the output of which could be analog or digital . The signal acquired by this sensor may undergo further conversion, fi ltering, and signal processing through the processor 250. This processed signal can further go through a computer program running on the device’ s operating system, which executes a machine learning algorithm optimized to detect features of the signal that correspond to ILI symptom events of interest , such as a dry cough, sneeze, or throat clearing. The results of the processing may be temporari ly stored to a memory 240, which is readi ly retrievable and removable through the processor 250.
The heart -rate sensor 220 can, in one embodiment of this invention, be in the form of an external device that is attachable to the mobi le computing device. In another embodiment , this sensor wi l l be a standalone device fixed or situated in a separate location, but which can transmit its data to the mobi le computing device through a wired or wireless network via the device’ s communication interface 230. In a third embodiment , the sensor 220 can be bui lt- in to the computing device, as part of the circuit package, and which has access to the external environment through a data acquisition means such as by skin contact with the user of the computing device or indirectly through non- invasive methods , such as through optical means .
The RTC 231 is an internal clock that maintains the measurement of time in a computing device, and therefore typical ly bui lt into the computing device. The GNSS 232 is a device that communicates with a satel l ite navigation system to acquire geolocation information of the device of which it is a part . These components are essential for determining the spat iotemporal index associated with the measurements originating from the sensor module 201.
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SUBSTITUTE SHEETS (RULE 26) The processor 250 aggregates the information originating from the sensor module 201, GNSS 232, and RTC 231, and transmitted to it through a wired, wireless or circuit network via the communication interface 230. The information processed in the processormay be stored in memory 240 for later retrieval and use.The processor operating system may run a computer program that executes an algorithm that will instruct the processor 250 to discard the currently stored data without sending,or send the stored data followed by deletion from memory 240. In the latter case, the processor relays the data in vector format through the communication interface 230,which sends it to the GCN 270, in a secure database.Through the communication interface 230, the mobile computing device could also receive processed information from the said database, that may be temporarily stored into memory 240 and presented graphically through the display 260.
As provided by FIG.3,when the data-transceiving mobile computing device is activated and provided that a software application runs on its operating system, a computer program is executed through its processor 250 to acquire data 301 through the sensor module 201.At the minimum, this sensor module can listen to and record sound 310 and measure the heart rate 320. It can detect the ILI sound 311 from the recording and convert the heart rate to an estimate of the body temperature 321. The data acquisition events 310 and 320 need not happen simultaneously on the same device. The possible implications of the asynchronous data acquisition in generating a map visualization and analysis can be addressed by the GIS data method 600 for locating potential hotspots of infection.
The recorded sound clip goes through a sound detection method 311,which is executed by a computer program 400 running in the operating system of the mobile computing device’ s processor.The said method detects the presence of an ILI sound within the clip 312. An instance of this method has been
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SUBSTITUTESHEETS (RULE26) reported by [Sun et al . (2015) , http: //dx.doi .org/10.1145/2750858.2805826] . Method 311 converts the recorded sound cl ip, a digital ly sampled time series , into a two-dimensional image known as a sonograph using a spectral transform technique, such as the fast Fourier transform. The converted data then proceed to a computer program that executes a solution generated from a machine learning algorithm that represents the mapping among the features of sonograph that contain information of ILI sound. If the computer program affirms the presence of ILI sound in the sonograph then, the geo- location, time of occurrence of the event , and the type of symptom are appended as components of a data vector 330. Otherwise, the method discards the recorded cl ip 313 , ensuring its compl iance with any data privacy statutes . The data vector is then sent 340 to a remote secure database 130 before deletion 350 from the local memory 240.
The heart-rate data col lected from sensor 220 wi l l be passed as raw input to a computer program running in the mobi le computer’ s operating system. The said computer program would execute an algorithm that can detect any statistical ly significant deviation from the normal heart rhythms of the user . The deviation is then converted into a temperature reading based on the l ink that has been establ ished by evidence in the medical l iterature [Kirschen et al . (2019) , https : //doi .Org/10.1016/j . aj em.2019.158355] . The febri le temperature would be detected from the degree and significance of the deviation. If the computer program affirms the presence of fever in the heart-rate data, the geo- location, time of occurrence, and the estimated body temperature are appended as components of a data vector 330. Otherwise, the method discards the recorded temperature 323 , ensuring its compl iance with any data privacy statutes . The data vector is then sent 340 to a remote secure database 130 before deletion 350 from the local memory 240.
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SUBSTITUTE SHEETS (RULE 26) Upon the completion of at least one branch of 300, the method loops back 302 to the data acquisition.
FIG. 4 provides the steps involved in the sound detection method 400, starting with the sound clip 401 originating from the audio sensor 210. The analog signal goes through digitization at a sampling frequency of 16 kHz 410 proven to optimize the tradeoff between clip quality and memory requirements [Sun et al. (2015), http://dx.doi.org/10.1145/2750858.2805826; Kvapilova et al. (2019), Digit Biomark 3, 166-175, https://dx.doi.org/10.1159/000504666]. The sampled continuous audio is then subdivided into segments 420 with lengths not exceeding 5 seconds, such that the end of one segment overlaps with the start of the next segment. This overlapping ensures that boundary effects do not cause distortion to the subsequent signal processing steps. Every segment goes through signal processing steps, such as, but not limited to Fourier or wavelength transform or bandpass filtering, the result of which converts the time-domain signal into a two-dimensional image known as a sonograph 430 corresponding to the spectral features of the said signal. The sonograph of the segment is the input to a classifier 440, such as, but not limited to, a convolutional neural network, with a proven capability for pattern recognition on spatially structured information. The classifier is a solution that could be produced by a learning algorithm applied to a set of example sonograph of known audio segment samples. The samples may or may not contain the ILI sound, and if they do, the sound's occurrence is any time within the segment. The ILI sounds in the samples could also be at different distances away from the detector. Thus, if ILI sound is in the segment clip, another method could estimate the source distance 450. The execution of method 400 is iterative, as the sound capture takes place continuously during the surveillance .
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SUBSTITUTE SHEETS (RULE 26) As outl ined in FIG. 5, the temperature detection method 500 starts with the raw heart-rate originating from the sensor 220. Software instructions stored in a non-transitory computer- readable medium implement a model of the normal heart rhythms of the person that uses the mobi le computing device. The model can be generated from an initial cal ibration of the sensor by the person. The received heart rate information from the sensor 220 is then compared statistical ly to the normal heart rhythms 510, such as through a standard hypothesis test . The test yields a statistic that can quantify the significance of the deviation 520 from the normal heart rate of the person. A statistical ly significant deviation indicates the presence of febri le temperature, which is estimated from the interpolation of a known relationship between heart rate increase and elevation in body temperature. The execution of method 500 is iterative, as the heart rate readings can take place at multiple moments during the survei l lance.
The execution of method 600, presented in FIG. 6, is iterative at a predetermined update frequency. The update steps comprise the geoprocessing 160, which can be operated by software instructions residing in a non- transitory computer- readable storage medium. The execution of the software instructions by a processor causes the processor to apply both spatial and attribute data functions 610. The data functions include, but are not l imited to the fol lowing: map registration; map proj ections ; conflation; classification; verification; and retrieval . The overal l aim of 610 is to prepare the raw geodata extracted from the GIS database of ILI sounds and body temperature estimates for data analysis . The integrated data analysis 620 consists of operations such as but not l imited to the fol lowing: determining the spatial coincidence of temperature and ILI sound data; density functions 170; neighborhood functions ; and predicting unknown values using the known values at neighboring locations . Data-analyt ic methods may supplement the complete data analysis , or a part thereof , such as , but not
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SUBSTITUTE SHEETS (RULE 26) l imited to, machine learning, mathematical model ing, and regression, which could fi lter false readings such as ILI sounds due to asthma, or feverish temperatures due to strenuous physical exertion. The overal l aim of 620 is to examine the spat iotemporal relationships among the ILI sound and temperature data towards meaningful early warnings for a potential epidemic.
The epidemic weather forecast 630 uti l izes spat iotemporal autocorrelation and cross-correlation techniques to denote regions on the map 640 that are potential hotspots of infection 641 and del ineate potential physical paths of transmission 642. The map visual ization 640 can present the results of the epidemic weather forecast using color or shade variations , such as in, but not l imited to, the form of a choropleth. For instance, a statistical measure known as Moran’ s index may offer a quantitative description of the spat iotemporal clustering of data points , which could indicate the early stages of an infection hotspot . Particularly, a set of retrieved geodata with a t ime-of-occurrence attribute fal l ing within a specified period, the spatial positions of which are in proximity to one another , might result in a Moran’ s index value that is positive and sufficiently close to 1.0, with statistical significance. This index value could confirm that the retrieved geodata set may represent a local transmission of an infective pathogen that is causing the ILI symptoms . The period length could also provide information on the rate of infection; particularly, an estimate of the infection’ s basic reproduction number , R0 (“R-nought” ) , is a useful indicator of the pathogen’ s abi l ity to spread and cause a pandemic. Higher R0 values relative to one ( 1) imply higher potency of the infection to spread in the human population. For example, the R0 of the SARS-CoV2 virus , which caused the CoVID-19 pandemic, was estimated at 6.47 during the early stages of the outbreak [Tang et al . (2020) , J . Cl in. Med. 9(2) , 462, https : //doi .org/10.3390/j cm9020462] . In comparison, the RO of the seasonal influenza virus is only between 0.9 and 2.1.
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SUBSTITUTE SHEETS (RULE 26) Other forms of analyses on the geodata acquired through the system and methods comprising this invention can be easi ly accompl ished by anyone ski l led in the art when desired. The visual izations resulting from 640 are fed back to the database 130 in a form that may be efficiently retrieved by end users , including the sources of the data 115, 124, through their respective data-transceiving mobi le computing devices . It is also possible to access the map through any computer terminal that is connected to the Internet . The visual information wi l l be straightforward to interpret , thus , helping the community to act on quickly to curtai l the spread of a potential pandemic, such as , by practicing social -di stancing and quarantine measures , and wearing of face masks , where appl icable. The government wi l l also be able to manage the spread of infection at its early stages without needing to lock down an entire province or country; hence, averting the economic disruption that an uncontrol led spread would have caused, such as what happened during the CoVID-19 pandemic.
Embodiments of the present invention may be a system, a method, and/or a computer program product at any possible technical detai l level of integration. The computer program product may include a computer- readable storage medium (or media) having computer- readable program instructions thereon for causing a processor to carry out aspects of the present invention.
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SUBSTITUTE SHEETS (RULE 26)