Abstract: Methods for forecasting case counts for a future date in one or more geographic areas of persons infected by a disease is disclosed. The presence of the disease in a biological sample is testable by a polymerase chain reaction (PCR) test. A load of one or more pathogens associated with the disease correlates with a PCR cycle which indicates presence of the one or more pathogens, and is referred to as a threshold cycle (Ct). Data relevant to forecasting the case counts including Ct data and other data is received. The Ct data comprises Ct values from PCR tests of biological samples from persons within the one or more geographic areas. Arrays of feature data for processing by a trained machine learning model are generated, comprising Ct features and other features obtained from the data. A forecasted number of infected persons are generated by processing the arrays using machine learning.