Global COVID-19 Mortality Forecasting

COVID-19 forecasting models are currently very influential, as they are used as a key input into policy decisions at the local, national, and international level. However, it can be difficult for policymakers to know which model to use, as hundreds have been produced, and they can differ widely in results and methodology. There is a need to bring together models from different groups, and also provide information about how well each model has performed historically, to help inform which should be considered in the future. is a free, online visualization tool that incorporates forecasts of COVID-19 mortality from major, global models. It provides forecasts months into the future, and for all countries and US states. Users from across the globe can compare what each set of forecasts are saying about COVID-19 in their country. They can also dig deeper and look at how well each model has done in the past, to inform their confidence in a given prediction. Our code and data are all open source and publicly available to promote cooperation and transparency.

The team is a group of 3 graduate/medical students at UCLA. We are seeking community feedback in order to refine our visualization. We hope this tool will be useful in guiding global policy guidelines and COVID-19 mitigation strategies.

Understanding COVID-19 Risk Perception and Behaviors

Effective prevention of COVID-19’s spread is strongly predicated on adherence of the population to recommendations of health professionals. It is therefore critical that we identify the factors that contribute to an individual’s health decision-making with respect to adherence to the recommendations. We are developing two projects to help inform health policy and strategies around COVID-19:

  • National survey. We are creating a cross-sectional survey in collaboration with the Nationscape survey study (which is collecting 500,000 surveys through weekly interviews of Americans from July 2019 through December 2020) to identify and develop potential strategies to enhance compliance. Our survey will cover risk perception, factors influencing risk perception (e.g., primary sources of information, education; risk behaviors, factors influencing risk behaviors (e.g., demographics, health insurance), preexisting health beliefs and psychosocial factors (e.g., social isolation, anxiety, worry). The questions will help assess: 1) COVID-related behaviors (e.g., protective behaviors and high-risk behaviors); and 2) health preventive behaviors (e.g., increasing health maintenance). Results from this large study will be widely disseminated and will form the basis of a comparative effectiveness study for different preventative health interventions.
  • Cancer screening patients. The WISDOM Study involves an enrolled cohort of >25,000 women and already includes assessment around breast cancer risk, perception, and anxiety. We are proposing to extend this assessment with similar COVID-19 specific question in similar areas, gaging psychosocial measures of anxiety and depression within this group. Results from the cohort will be further contrasted with the results of the national survey, ad we will use these results to identify educational gaps and propose practical strategies to encourage preventative behaviors.

Remote Monitoring of COVID-19 Symptoms

With increased COVID-19 testing and the detection of more positive cases (COVID+), many “at-risk” individuals who appear asymptomatic or only with mild symptoms are being told to self-quarantine at home. Unfortunately, there are situations where a sudden worsening of the disease occurs and symptoms progress quickly, usually within 24-48 hours. This rapid health decline often goes unreported until too late. More proactive surveillance of these COVID+ individuals could provide an alert to healthcare providers to initiate earlier intervention and save lives.

Our project adapts our current mHealth framework to the immediate needs of the COVID-19 crisis. Unlike other mHealth efforts that are using apps for contact tracing, self-reporting of symptoms, or new sensors for detecting disease onset, we focus on using mHealth to enable (safe) remote monitoring of known COVID+ patients. We originally created the Sensing for At-Risk Patients (SARP) platform to automatically collect information on older, frail patients with different conditions. SARP uses a smartwatch and different sensors to capture this information, automatically and securely sending the data to UCLA Health for review by healthcare staff. We are extending SARP by adding several FDA-approved devices (thermometer, pulse oximeter, respiratory monitor) to augment its present capabilities. SARP will be used to gather objective observations for at-home COVID+ cases, with the data reviewed by healthcare providers to assess patient health.

Capturing this data will further elucidate the myriad presentation of symptoms in COVID+ patients in a natural environment, helping establish a better understanding of this disease and the evolving pandemic. As more insight is gained about these patients and the presentation of the disease, we will explore the application of machine learning methods on this data to detect early patterns of worsening symptoms, leading to potential clinical decision support tools for physicians.