COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves.

This article presents a novel mathematical model to describe the spread of an infectious disease in the presence of social and health events: it uses 15 compartments, 7 convolution integrals and 4 types of infected individuals, asymptomatic, mild, moderate and severe. A unique feature of this work is that the convolutions and the compartments have been selected to maximize the number of independent input parameters, leading to a 56-parameter model where only one had to evolve over time. The results show that 1) the proposed mathematical model is flexible and robust enough to describe the complex dynamic of the pandemic during the first three waves of the COVID-19 spread in the region of Madrid (Spain) and 2) the proposed model allows us to calculate the number of asymptomatic individuals and the number of persons who presented antibodies during the first waves. The study shows that the following results are compatible with the reported data: close to 28% of the infected individuals were asymptomatic during the three waves, close to 29% of asymptomatic individuals were detected during the subsequent waves and close to 26% of the Madrid population had antibodies at the end of the third wave. This calculated number of persons with antibodies is in great agreement with four direct measurements obtained from an independent sero-epidemiological research. In addition, six calculated curves (total number of confirmed cases, asymptomatic who are confirmed as positive, hospital admissions and discharges and intensive care units admissions) show good agreement with data from an epidemiological surveillance database.

Developing modular training components to support home hospital digital solutions: Results of a Delphi panel.

BACKGROUND: Home hospitalization (HH) has demonstrated to be a cost-effective alternative with respect ti traditional hospitalization. Digital technologies, such as remote monitoring, have the potential to contribute to its expansion. Tailored educational content is a need to ensure patient safety during the whole admission. PURPOSE: The objective of this study was to systematically obtain consensus on patients with HH using training in the digital monitoring system. The goal of this work was to develop an adaptable modular and personalized training program for patients to support quality and safety care for HH. METHODS: The methodological approach for developing the proposed training content followed a modified Delphi technique with a multidisciplinary group of experts with significant knowledge of health informatics and HH protocols in Spain. The study comprised two rounds of training material description and gathering were completed. In Round 1, the experts received 58 predefined items obtained from the literature review and protocol selection. 20 items were rejected for different reasons and 25 new items were proposed. In Round 2, the experts selected the final items to build on the training content for every type of user and illness. RESULTS: A total of 21 experts completed rounds 1 and 2. The consensus was reached at the end of Round 2 with the inclusion of 53 items to build the training material. This included 17 treatment procedures, 4 diagnosis procedures, 22 additional support content, and 10 content features that describe how to build and deliver customized training content. CONCLUSIONS: Participants agreed on the type of content, its structure, and delivery methods to build modular training materials that support patients when they are hospitalized at home with the help of digital monitoring tools. This information can be used to create HH training programs that support new HH protocols and provide a standard for evaluating the quality of existing educational materials and programs.