Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics.  https://www.selleckchem.com/products/bay-3827.html  We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513188 cases, including ~14302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96( 95% CI 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.
  Multiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses.
 
  Four months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testingh for real-time epidemiologic investigation and public health utility.
 The strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility.Importance There is limited evidence regarding whether the presence of serum antibodies to SARS-CoV-2 is associated with a decreased risk of future infection. Understanding susceptibility to infection and the role of immune memory is important for identifying at-risk populations and could have implications for vaccine deployment. Objective The purpose of this study was to evaluate subsequent evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among individuals who are antibody-positive compared with those who are antibody-negative, using real-world data. Design This was an observational descriptive cohort study. Participants The study utilized a national sample to create cohorts from a de-identified dataset composed of commercial laboratory test results, open and closed medical and pharmacy claims, electronic health records, hospital billing (chargemaster) data, and payer enrollment files from the United States. Patients were indexed as antibody-positive or antibody-negmean of 48 versus 44 years). A fraction (18.4%) of individuals who were initially seropositive converted to seronegative over the follow up period. During the follow-up periods, the ratio (CI) of positive NAAT results among individuals who had a positive antibody test at index versus those with a negative antibody test at index was 2.85 (2.73 - 2.97) at 0-30 days, 0.67 (0.6 - 0.74) at 31-60 days, 0.29 (0.24 - 0.35) at 61-90 days), and 0.10 (0.05 - 0.19) at >90 days. Conclusions Patients who display positive antibody tests are initially more likely to have a positive NAAT, consistent with prolonged RNA shedding, but over time become markedly less likely to have a positive NAAT. This result suggests seropositivity using commercially available assays is associated with protection from infection. The duration of protection is unknown and may wane over time; this parameter will need to be addressed in a study with extended duration of follow up.