Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts.

Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20-208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.

Challenges of evaluating and modelling vaccination in emerging infectious diseases.

Outbreaks of emerging pathogens pose unique methodological and practical challenges for the design, implementation, and evaluation of vaccine efficacy trials. Lessons learned from COVID-19 highlight the need for innovative and flexible study design and application to quickly identify promising candidate vaccines. Trial design strategies should be tailored to the dynamics of the specific pathogen, location of the outbreak, and vaccine prototypes, within the regional socioeconomic constraints. Mathematical and statistical models can assist investigators in designing infectious disease clinical trials. We introduce key challenges for planning, evaluating, and modelling vaccine efficacy trials for emerging pathogens.

Evolving Treatment Patterns for Hospitalized COVID-19 Patients in the United States in April 2020-July 2020.

We sought to examine the trend (April-July) in the treatment patterns among hospitalized COVID-19 patients using the Premier Healthcare Database (PHD). In the analysis, we identified 53,264 patients from 302 hospitalsthat continuously provided inpatient data from April 1, 2020 to July 31, 2020 to the PHD, a nationwide, population-based multihospital research database in the US. We used generalized estimating equations (GEE) models to assess changes in the proportion of therapies used during the study period. After adjusting for patient and provider factors, a decline in hydroxychloroquine and an increase in azithromycin and dexamethasone were observed among COVID-19 patients during the 4-month study period.