Proposed are concepts for generating recommendations of disease-related resources in different geographical regions. In particular, it is proposed to leverage information about the popularity (e.g. frequency, rate or regularity of use) of keywords associated with diseases across different geographic regions. Using such information, a relevancy value (i.e. geography weighting score score) for a disease-related resource with respect to a specific geographic region may be calculated. These relevance values may then be used to generate recommendations for the disease-related resources for the specific geographic region.

An embodiment method of providing a mobility sharing service based on disinfection information includes receiving, at a mobility sharing management server, a mobility sharing service request from a user terminal, requesting, by the mobility sharing management server, disinfection information from a disinfection management server based on location information of the user terminal, receiving, by the mobility sharing management server, the disinfection information from the disinfection management server and calculating a predicted infection degree based on the disinfection information, and determining whether to provide the mobility sharing service to a user corresponding to the user terminal based on the predicted infection degree.

A real estate showing appointment management system manages showing appointments based on a health parameter of a showing contact. The health parameter can be the body temperature. The system includes a server system running a server software application and software application running on a device. The software application configures the thermometer of the device to take body temperature of the showing contact. The thermometer can be an infrared thermometer. The software application determines whether the body temperature meets a threshold relying on the server system or by itself. When the showing contact passes the health check, the software application retrieves a showing directive for the showing contact to proceed with showing the listing of a showing appointment. The showing directive is presented by the device for the showing contact.

A computer system and method causes selection of an advertisement based on the prevalence of a healthcare condition in each of a plurality of geographic areas. The prevalence is calculated by an entity that matches healthcare data with consumer data to determine, in each of the geographic areas, how many individuals have an unidentified healthcare condition. The entity removes information pertaining to specific geographic areas and healthcare condition codes that would permit re-identification of persons coded with those specific codes, so that the privacy of the personal healthcare information is maintained.

Mechanisms are provided to adapt computer modeling of an infectious disease based on noisy data and perform hyperlocal prediction of infectious disease dynamics and risks. Case report data is received and a trained background noise computer model is applied to generate first prediction results predicting infectious disease dynamics. The trained background noise computer model is trained to model infectious disease dynamics assuming that there is no community spread of the infectious disease. A first error measure of the first prediction results is determined and, in response to the first error measure being lower than a threshold value, the first prediction results are selected to output as predicted infectious disease dynamics. In response to the first error measure being equal/greater than the threshold value, second prediction results are selected. The second prediction results are generated by applying a trained infectious disease computer model to the received case report data.

Methods, systems, and computer programs are presented for estimating disease-spreading based on facility and behavioral parameters. One method includes an operation for causing presentation of a user interface (UI) for entering facility parameters. The method further includes an operation for calculating the number of infections at the facility for a predetermined time period. The calculation includes setting values for simulation parameters based on the facility parameters, modeling a contact network for people at the facility, and performing a plurality of simulations to determine the number of infections, the plurality of simulations based on the contact network and the facility simulation parameters. Further, the method includes an operation for causing presentation of the number of infections in the UI.

Methods, systems, and computer programs are presented for estimating disease-spreading based on facility and behavioral parameters. One method includes an operation for causing presentation of a user interface (UI) for entering facility parameters. The method further includes an operation for calculating the number of infections at the facility for a predetermined time period. The calculation includes setting values for simulation parameters based on the facility parameters, modeling a contact network for people at the facility, and performing a plurality of simulations to determine the number of infections, the plurality of simulations based on the contact network and the facility simulation parameters. Further, the method includes an operation for causing presentation of the number of infections in the UI.

Mechanisms are provided for hyperlocal prediction of epidemic dynamics and risks. Regional machine learning training is performed on an infectious disease computer model at least by: receiving first case report data; pre-processing the first case report data to remove noise at least by applying a smoothening algorithm to form first smoothed data; aggregating the first smoothed data into regional data, wherein aggregating the first smoothed data comprises correlating the first smoothed data to a target region corresponding to a population; and training the model using the regional data. The trained model is executed on new second case report data for the target region and automatic monitoring of performance of the model is performed according to a prediction accuracy of the model. In response to the prediction accuracy being below a predetermined threshold, automatic retraining is initiated.

Mechanisms are provided for hyperlocal prediction of epidemic dynamics and risks. Regional machine learning training is performed on an infectious disease computer model at least by: receiving first case report data; pre-processing the first case report data to remove noise at least by applying a smoothening algorithm to form first smoothed data; aggregating the first smoothed data into regional data, wherein aggregating the first smoothed data comprises correlating the first smoothed data to a target region corresponding to a population; and training the model using the regional data. The trained model is executed on new second case report data for the target region and automatic monitoring of performance of the model is performed according to a prediction accuracy of the model. In response to the prediction accuracy being below a predetermined threshold, automatic retraining is initiated.

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.