The Institute for Disease Modeling produced software called Epidemiological Modeling (EMOD).  This modeling software predicts the propagation of the disease in terms of the depletion of healthcare resources and equipment available. 

Hospital Resource Predictions

Producing models of the virus spread based on hierarchy, individual characteristics, and divisions within the population. Such models can be seen on online simulators produced by the Washington Post, the CDC website, and Johns Hopkins University.

Social Pattern Analysis

This can be done via contact tracing as people adapt to the "new normal".  This allows  clinical personnel to recognize infection hotspots and notify personnel of of possible new cases. This method is used to slow down and the pandemic and plauteau the curve.

Digital Monitoring

University of California, Santa Cruz

Machine Learning models can be used to better understand the proteins and complexes that are incvolved with the novel Coronavirus. This includes producing predictions of pandemic regarding hospitalizations, patient outcomes, and simulations.

Machine Learning Analysis 

Allam, M., Cai, S., Ganesh, S., Venkatesan, M., Doodhwala, S., Song, Z., . . . Coskun, A. F. (2020). COVID-19 Diagnostics, Tools, and Prevention. Diagnostics, 10(6), 409. doi:10.3390/diagnostics10060409

https://www.mdpi.com/2075-4418/10/6/409

COVID-19 Diagnostics, Tools, and Prevention

Machine Learning models can be used to understand the protein complexes involved with infection and what treatments/drugs produce a given relationship.

Protein structure prediction

Unlike pathogenic laboratory testing which produces a significant amount of false-negatives while still being a time-consuming method, Machine Learning models  can display radio-logical and image patterns via CT chest scans. 3D CT volume can detect the virus's transmission by using a lung-region 3D CT scan which is segmented and fed into a 3D deep neural network to predict the infection rate.

Large-Scale Analysis of CT Scans: 

Machine Learning models can provide healthcare systems with a forecast of patient outcomes, risk of hospitalization, and probability of complications ranging from respiratory distress to organ failure. The model takes in blood samples and CT scan datasets to develop machine learning models of high risk patients. 

At high speed, these tools (logistic and FbProphet models) also observe trends in the accumulated epidemiological data with which they predict how the future epidemic curve will look like. It's determined that by end of October, approximately 14.1 million people will be infected, in a cumulative aspect. 


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