Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory (preprint)

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.

Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use (preprint)

BackgroundMultiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. MethodsWe developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. ResultsWithout a vaccine, the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8-4 million infections and 15,000-240,000 deaths across these four states over the next 12 months. In this scenario, introducing a vaccine would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50%, a vaccine that is only 50% effective (weak vaccine) would require coverage of 55-94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32-57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely, a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48-78% or a strong vaccine (100% effective) with coverage of 33-58% would be required to suppress the epidemic. Delaying vaccination rollout for 1-2 months would not substantially alter the epidemic trend if the current interventions are maintained. ConclusionsThe degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.

Effects of New York's Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study (preprint)

Background: New York City (NYC) was the epicenter of the COVID-19 pandemic in the United States. On April 17, 2020, the State of New York implemented an Executive Order that requires all people in New York to wear a face mask or covering in public settings where social distancing cannot be maintained. It is unclear how this Executive Order has affected the spread of COVID-19 in NYC. Methods: A dynamic compartmental model of COVID-19 transmission among NYC residents was developed to assess the effect of the Executive Order on face mask use on infections and deaths due to COVID-19 in NYC. Data on daily and cumulative COVID-19 infections and deaths were obtained from the NYC Department of Health and Mental Hygiene. Results: The Executive Order on face mask use is estimated to avert 99,517 (95% CIs: 72,723-126,312) COVID-19 infections and 7,978 (5,692-10,265) deaths in NYC. If the Executive Order was implemented one week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593-141,356) and 9,017 (6,446-11,589), respectively. If the Executive Order was implemented two weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373-162,824) and 10,515 (7,540-13,489), respectively. Conclusions: New York's Executive Order on face mask use is projected to have significantly reduced the spread of COVID-19 in NYC. Implementing the Executive Order at an earlier date would avert even more COVID-19 infections and deaths.