The new COVID‐19 pandemic has challenged policymakers on key issues. Most countries have adopted “lockdown” policies to reduce the spatial spread of COVID‐19, but they have damaged the economic and moral fabric of society. Mathematical modeling in non‐pharmaceutical intervention policy management has proven to be a major weapon in this fight due to the lack of an effective COVID‐19 vaccine. A new hybrid model for COVID‐19 dynamics using both an age‐structured mathematical model based on the SIRD model and spatio‐temporal model in silico is presented, analyzing the data of COVID‐19 in Israel. Using the hybrid model, a method for estimating the reproduction number of an epidemic in real‐time from the data of daily notification of cases is introduced. The results of the proposed model are confirmed by the Israeli Lockdown experience with a mean square error of 0.205 over 2 weeks. The use of mathematical models promises to reduce the uncertainty in the choice of “Lockdown” policies. The unique use of contact details from 2 classes (children and adults), the interaction of populations depending on the time of day, and several physical locations, allow a new look at the differential dynamics of the spread and control of infection.
Pharmaceutical nanoparticles (NPs) carrying molecular payloads are used for medical purposes such as diagnosis and medical treatment. They are designed to modify the pharmacokinetics-pharmacodynamics (PKPD) of their associated payloads, to obtain better clinical results. Currently, the research process of discovering the PKPD properties of new candidates for efficient clinical treatment is complicated and time-consuming. In silico experiments are known to be powerful tools for studying biological and clinical processes and therefore can significantly improve the process of developing new and optimizing current NPs-based drugs. However, the current PKPD models are limited by the number of parameters they can take into consideration and the ability to solve large-scale in vivo settings, thus providing relatively large errors in predicting treatment outcomes. In this study, we present a novel mathematical graph-based model for PKPD of NPs-based drugs. The proposed model is based on a population of NPs performing a directed walk on a graph describing the blood vessels and organs, taking into consideration the interactions between the NPs and their environment. In addition, we define a mechanism to perform different prediction queries on the proposed model to analyze two in vivo experiments with eight different NPs, done on mice, obtaining a fitting of 0.84 +- 0.01 and 0.66 +- 0.01 (mean +- standard deviation), respectively, comparing the in vivo values and the in silico results.
Many researchers have tried to predict the impact of the COVID-19 outbreak on morbidity, in order to help policy-makers find optimal isolation policies. However, despite the development and use of many models and sophisticated tools, these forecasting attempts have largely failed. We present a model that considers the severity of the disease and the heterogeneity of contacts between the population in complex space–time dynamics. Using mathematical and computational methods, the applied tool was developed to analyze and manage the COVID-19 pandemic (from an epidemiological point of view), with a particular focus on population heterogeneity in terms of age, susceptibility, and symptom severity. We show improved strategies to prevent an epidemic outbreak. We evaluated the model in three countries, obtaining an average mean square error of 0.067 over a full month of the basic reproduction number (R 0). The goal of this study is to create a theoretical framework for crisis management that integrates accumulated epidemiological considerations. An applied result is an open-source program for predicting the outcome of an isolation strategy for future researchers and developers who can use and extend our model.
Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.