ABSTRACT
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and intervention measures during an ongoing outbreak. However, reliably inferring the dynamics of ongoing outbreaks by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases: In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.
Subject(s)
COVID-19ABSTRACT
Introduction Consistent guidelines on respiratory protection for healthcare professionals combined with improved global supply chains are critical to protect staff and patients from COVID-19. We summarized and compared the guidelines published by national and international societies/organizations on facemasks and respirators to prevent COVID-19 in healthcare settings. Methods From the 1st January to the 2nd April 2020, guidelines published in four countries (France, Germany, United States, United Kingdom), and two international organizations (US and European Center for Diseases Control, and World Health Organization) were reviewed to analyze the mask and respirators recommended as PPE for the care of patients during the COVID-19 outbreak. Guidelines were eligible for analysis if they (1) included specific guidelines, (2) were written for HCP protection, (3) targeting healthcare settings. The strategy recommended for optimizing supplies and overcoming shortages was collected. Observations The guidelines publication process on respiratory protections varied greatly across countries. Some referred to a unique guide whereas others saw the issue of multiple recommendations by various societies and organization. In term of chronology, most guidelines were published in March with either downgraded (US and European CDC), relatively stable (WHO, Germany, and UK), or a mixing of high and low level equipment (France). The recommendation of respirators was universally recommended for aerosol generating procedures (AGP) across countries, although the type of respirators and what constituted an AGP was variable. Some guidance maintained the use of N95/99 for all contact with confirmed COVID-19 cases (i.e. Germany) whereas others, recommended a surgical mask (i.e. WHO, UK, France). The strategies to overcome shortage of respiratory protection equipment were based on minimizing the need and rationalizing the use, but also prolonging their use, reusing them after cleaning/sterilization, or using cloth masks. Conclusions Stable and consistent guidelines inside and across countries, clearly detailing the respiratory protection type, and the circumstances in which they need to be used may prevent the confusion among frontline staff, and avoid shortage.