公开(公告)号：US12062456B2

公开(公告)日：2024-08-13

申请号：US17332356

申请日：2021-05-27

Applicant: Merative US L.P.

Inventor： Vishrawas Gopalakrishnan ,  Ajay Ashok Deshpande ,  Sayali Navalekar ,  James H. Kaufman ,  Simone Bianco ,  Kun Hu ,  Xuan Liu ,  Jacob Ora Miller ,  Raman Srinivasan ,  Pan Ding

IPC: G16H50/80 ,  G06F40/40 ,  G16H15/00 ,  G16H50/20

CPC classification number: G16H50/80 ,  G06F40/40 ,  G16H15/00 ,  G16H50/20

Abstract: Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

公开(公告)号：US11948694B2

公开(公告)日：2024-04-02

申请号：US17318027

申请日：2021-05-12

Applicant: Merative US L.P.

Inventor： Vishrawas Gopalakrishnan ,  Sayali Navalekar ,  James H. Kaufman ,  Simone Bianco ,  Kun Hu ,  Ajay Ashok Deshpande ,  Sarah Kefayati ,  Ujwal Reddy Moramganti ,  George Sirbu ,  Xuan Liu ,  Raman Srinivasan ,  Pan Ding

IPC: G06Q30/00 ,  G06N20/00 ,  G16H50/50 ,  G16H50/80

CPC classification number: G16H50/80 ,  G06N20/00 ,  G16H50/50

Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.