The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (31.2 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure (BP) and heart rate (HR) associated with 150 ARDS patients admitted to five University of California academic health centers (containing 77,972 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Our deep learning model is able to achieve 0.81 area under the curve (AUC) to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients. Since our proposed model uses only the BP and HR, it would be possible to review data prior to the first reported cases in the U.S. to validate the presence or absence of COVID-19 in our communities prior to January 2020. In addition, by utilizing wearable devices, and monitoring vital signs of subjects in everyday settings it is possible to early-detect COVID-19 without visiting a hospital or a care site.