Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19

Some huge scope outside impact pandemics has risen in the course of the most recent two decades, including human, natural life, and plant plagues. Authorities face strategy issues that are reliant on deficient information and require sickness gauges. In this manner, there is an earnest need to create models that empower us to outline all accessible information to estimate and screen an advancing pandemic in an ideal way. This chapter targets assessing different models and proposing an early-cautioning AI approach that can conjecture potential flare-ups of ailments. For gauge COVID-19 episodes, the SEIR model, molecule channel calculation and an assortment of pandemic-related datasets are utilized to investigate different models and strategies. In this chapter, various intermediaries have been clarified for the pandemic season prompting comparative conduct of the powerful multiplication number. We found that a solid relationship exists among conferences and analyzed datasets, particularly when considering time based models. Singular parameters gave like distinctive episode seasons esteems, in this way offering an open door for future flare-ups to utilize such data. © 2021 Scrivener Publishing LLC.

Photoelectric and diffusion charging measurements of fine particulate air pollution along the main roads of the city of Madrid from 1999 to 2021

In this work, the evolution of the fine particulate air pollution (of size below one μm) produced by the vehicles when driving along several roads of Madrid is studied. Measurements were taken with portable near real-time sensors of Diffusion Charging (DC) and Photoelectric Charging (PC) while driving along the roads. The obtained measurement profiles basically consist of spikes when measuring in the exhaust plumes of preceding vehicles and a background level of mixed aged exhaust that forms when high traffic intensity exist. The DC sensor measures air concentration of the particles Total Active Surface (TAS) and the PC sensor was calibrated to measure the air concentration of Particle bound Polycyclic Aromatic Hydrocarbons (PPAH). The amount of adsorbed PAH per active surface (the PC/DC ratio) is a measure of particles toxicity. Both sensors are sensible to ultra-fine particles of size below 0.1 μm. The measured median values of DC and PC, for the years 1999, 2001, 2005, 2006, 2016, 2017, 2020 and 2021, are plotted as well as their median PC/DC ratio. Examples of measurement profiles are also shown including measurements during COVID-19 driving restrictions. During these restrictions, we could conclude that our measured particulate air pollution of fine and ultrafine particles is caused by "polluting-vehicles" still coexisting in the vehicle fleet of Madrid, which do not fulfill the latest Euro standard because they are too old or have no/malfunctioning catalytic converter and/or diesel particle filter (DPF). The changes of the measured median of the DC and PC values are discussed based on already known results of implemented vehicle technologies for reducing emissions, the evolution of the vehicle fleet fulfilling the increasingly demanding Euro standards, the traffic count, the PC and DC working principles, the evolution of the exhaust emission when exiting the pipe, and on the sulfur content reductions in diesel. The main factors that allowed the large reduction of the median values of both DC and PC (from 1167 ± 57 mm2/m3 and 990 ± 54 ng/m3 in 1999 to 263 ± 14 mm2/m3 and 124 ± 7 ng/m3 in 2021 respectively) as well as the changes in the PC/DC ratio was, according to our findings, the diesel sulfur content reduction and the implementation of the Diesel Oxidation Catalysis (DOC) and the DPF. • Decrease in the total active surface of fine particles in the air of the city of Madrid since 1999. • Decrease in PAH adsorbed on fine particles in the air along the roads of the city of Madrid since 1999. • Evolution of on-road fine and ultrafine exhaust particle emissions in Madrid since 1999. • Drastic localized increase of air suspended fine particles caused by specific polluting vehicles. • Changes in the on-road fine and ultrafine exhaust particles toxicity in Madrid since 1999. [ FROM AUTHOR] Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.

An Improved Localization of Mobile Robotic System Based on AMCL Algorithm

In the Mobile Robotics domain, the ability of robots to locate themselves is one of the most important events. By locating, mobile robots can obtain information about the environment and continuously track their position and direction. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is applied most often in robot localization, a two-dimensional environment probabilistic localization system to improve the problems such as high computational complexity and hijacking of mobile robots that exist in the traditional MCL method. The proposed method is based on 2D laser information, range finder information, and AMCL to accomplish the localization task. Furthermore, an optimized AMCL algorithm is proposed to increase the accuracy of localization in terrain that is easy to fail to locate, have a chance to locate successfully when a localization error occurs, and apply the optimized AMCL to the mobile robot system. From the experimental results, we know that the improved AMCL algorithm can enhance the positioning accuracy of the robot effectively, which has better practicality than the original AMCL.

A Versatile Filter Test System to Assess Removal Efficiency for Viruses in Aerosols

Mitigation measures to reduce indoor transmission of SARS-CoV-2 and other pathogenic microorganisms are urgently needed to combat the current pandemic and to prevent future airborne epidemics or pandemics. Very efficient exhaust filters for nanoparticles down to sizes of only a few nanometers have been available for many years;they are used, for example, in diesel and, more recently, gasoline vehicles to reduce emissions. The size of soot particles emitted by combustion engines, i.e., primary particles and aggregates, includes those of viruses. Therefore, such particle filters should also efficiently remove viruses. This study aimed to design a filter test system with a controlled airflow allowing to aerosolize particles at the aerosol inlet and collect samples before and after the particle filter. As an example, results obtained for the NanoCleaner (R), a filter designed to clean cabin air in vehicles, are presented. Validation with soot particles produced with a CAST soot generator revealed a filter efficiency higher than 99.5%. To assess the relevance of the test filter system to measure efficiency for viral particles removal, MS2 bacteriophages, also called Escherichia virus MS2, were used as virus surrogate and aerosolized into the filter test system with the commercially available Eraser nebulizer. Filter efficiencies of more than 99% for MS2 bacteriophages were achieved using the NanoCleaner (R) in the filter test system. Experiments with ceramic wall-flow filters showed similar results. To enlighten the versatility of the filter test system, a typical aircraft cabin air filter was also characterized. The measurements confirmed the high filter efficiency, and in addition, we show a decrease of bacteriophage's survival on the filter material over 48 h post-exposure. In conclusion, we have established a versatile system that is modular to test any filter system for the efficiency of eliminating MS2 bacteriophages as virus surrogates from air.

Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.

State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user's needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.