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.

Abstract: In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.

Abstract: Mechanisms are provided to perform automatic case intervention detection in infectious disease case reports and for configuring an infectious disease computer model based on the automatic intervention detection. Case report data is received and a time ordered curve of the case report data is generated. One or more inflection points in the time ordered curve are identified. The one or more inflection points in the time ordered curve are correlated with one or more intervention entries specified in time stamped infectious disease intervention data, the one or more intervention entries specifying interventions implemented by authorities to control spread of the infectious disease. One or more model parameters of an infectious disease computer model are configured based on results of correlating the one or more inflection points with the one or more intervention entries.

Abstract: Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

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.

Abstract: 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.

Abstract: 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.

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.

Abstract: A mechanism is provided in a data processing system to implement a model pipeline for predicting changes in disease transmission rate using a spatial temporal epidemiological model. The mechanism receives input data comprising disease case data for a disease and mobility data and prepares the input data to generate a training dataset, a validation dataset, and a test dataset. A feature selection module performs feature selection on the input data to select a first set of features for a binary classification computer model, a second set of features for a three-level classification computer model, and a third set of features for a regression computer model.