Monitoring and predicting viral dynamics in SARS-CoV-2-infected Patients

shaoqing wen; Yi Wang

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.14.20060491 doi: medRxiv preprint curves, that could be because the treatment with lopinavir-ritonavir, or, more likely, because the occurrence of false-negative test results. Moreover, we should pay special attention to those who have negative RT-PCR results but lower predicted Ct values on day 21 after symptom onset, such as patient 2 and 7, which implies they may turn positive again. In this study, we used a mathematical model to monitor and predict the changes of viral loads from different nasal and throat swab of clinical specimens collected from diagnosed patients. We also tested our real time model by using the data from the SARS-CoV-2-infected patients with different severity. By using this personal model, we can predict the viral dynamics of patients, minimize false-negative test results, and screen the patients who are at risk of testing positive again after recovery. The following factors can optimize our model: 1) more data regarding viral loads of the infected patients; 2) detailed information about clinical features and laboratory results;