Prediction of SARS-CoV-2 transmission dynamics based on population-level cycle threshold values: A Machine learning and mechanistic modeling study

BackgroundPolymerase chain reaction (PCR) cycle threshold (Ct) values can be used to estimate the viral burden of Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2) and predict population-level epidemic trends. We investigated the use of machine learning (ML) and epidemic transmission modeling based on Ct value distribution for SARS-CoV-2 incidence prediction during an Omicron-predominant period. MethodsUsing simulated data, we developed a ML model to predict the reproductive number based on Ct value distribution, and validated it on out-of-sample province-level data. We also developed an epidemiological model and fitted it to province-level data to accurately predict incidence. ResultsBased on simulated data, the ML model predicted the reproductive number with highest performance on out-of-sample province-level data. The epidemiological model was validated on outbreak data, and fitted to province-level data, and accurately predicted incidence. Conclusions These modeling approaches can complement traditional surveillance, especially when diagnostic testing practices change over time. The models can be tailored to different epidemiological settings and used in real time to guide public health interventions. FundingThis work was supported by funding from Genome BC, Michael Smith Foundation for Health Research and British Columbia Centre for Disease Control Foundation to C.A.H. This work was also funded by the Public Health Agency of Canada COVID-19 Immunity Task Force COVID-19 Hot Spots Competition Grant (2021-HQ-000120) to M.G.R.