ABSTRACT
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naive baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. Author SummaryAs SARS-CoV-2 began to spread throughout the world in early 2020, modelers played a critical role in predicting how the epidemic could take shape. Short-term forecasts of epidemic outcomes (for example, infections, cases, hospitalizations, or deaths) provided useful information to support pandemic planning, resource allocation, and intervention. Yet, infectious disease forecasting is still a nascent science, and the reliability of different types of forecasts is unclear. We retrospectively evaluated COVID-19 case forecasts, which were often unreliable. For example, forecasts did not anticipate the speed of increase in cases in early winter 2020. This analysis provides insights on specific problems that could be addressed in future research to improve forecasts and their use. Identifying the strengths and weaknesses of forecasts is critical to improving forecasting for current and future public health responses.
Subject(s)
COVID-19 , Death , Communicable DiseasesABSTRACT
The outbreak of coronavirus disease 2019 (COVID-19) has adversely affected the public domain causing unprecedented cases and high mortality across the globe. This has brought back the concept of biosafety into the spotlight to solve biosafety problems in developing diagnostics and therapeutics to treat COVID-19. The advances in nanotechnology and material science in combination with medicinal chemistry have provided a new perspective to overcome this crisis. Herein, we discuss the efforts of researchers in the field of material science in developing personal protective equipment (PPE), detection devices, vaccines, drug delivery systems, and medical equipment. Such a synergistic approach of disciplines can strengthen the research to develop biosafety products in solving biosafety problems.
ABSTRACT
With the advancements of robotic technologies, the medical environments are adopting more and more aspects of automation to enhance the services in hospitals. In pandemic situations such as COVID 19, direct contact with patients may result in the spreading of disease. In this paper we are going to discuss the design and development of an automatic guided vehicle for hospital applications, which can be controlled remotely. Here we are going to specify the design and development of an automatic guided vehicle for hospital applications, which can be controlled remotely. HARVi is a line follower robot, powered by a battery that can be charged from solar energy. © 2021 IEEE.