Modeling a traffic light warning system for acute respiratory infections.

The high morbidity of acute respiratory infections constitutes a crucial global health burden. In particular, for SARS-CoV-2, non-pharmaceutical intervention geared to enforce social distancing policies, vaccination, and treatments will remain an essential part of public health policies to mitigate and control disease outbreaks. However, the implementation of mitigation measures directed to increase social distancing when the risk of contagion is a complex enterprise because of the impact of NPI on beliefs, political views, economic issues, and, in general, public perception. The way of implementing these mitigation policies studied in this work is the so-called traffic-light monitoring system that attempts to regulate the application of measures that include restrictions on mobility and the size of meetings, among other non-pharmaceutical strategies. Balanced enforcement and relaxation of measures guided through a traffic-light system that considers public risk perception and economic costs may improve the public health benefit of the policies while reducing their cost. We derive a model for the epidemiological traffic-light policies based on the best response for trigger measures driven by the risk perception of people, instantaneous reproduction number, and the prevalence of a hypothetical acute respiratory infection. With numerical experiments, we evaluate and identify the role of appreciation from a hypothetical controller that could opt for protocols aligned with the cost due to the burden of the underlying disease and the economic cost of implementing measures. As the world faces new acute respiratory outbreaks, our results provide a methodology to evaluate and develop traffic light policies resulting from a delicate balance between health benefits and economic implications.

Preprocessing and Artificial Intelligence for Increasing Explainability in Mental Health

This paper shows the added value of using the existing specific domain knowledge to generate new derivated variables to complement a target dataset and the benefits of including these new variables into further data analysis methods. The main contribution of the paper is to propose a methodology to generate these new variables as a part of preprocessing, under a double approach: creating 2nd generation knowledge-driven variables, catching the experts criteria used for reasoning on the field or 3rd generation data-driven indicators, these created by clustering original variables. And Data Mining and Artificial Intelligence techniques like Clustering or Traffic light Panels help to obtain successful results. Some results of the project INSESS-COVID19 are presented, basic descriptive analysis gives simple results that even though they are useful to support basic policy-making, especially in health, a much richer global perspective is acquired after including derivated variables. When 2nd generation variables are available and can be introduced in the method for creating 3rd generation data, added value is obtained from both basic analysis and building new data-driven indicators. © 2023 World Scientific Publishing Company.

Traffic Lights Engineering Academy: A Remote Online Education Solution for Creating K-12 STEM Projects Featuring Microcontrollers

Microcontroller programming is an essential part of K-12 Science, Technology, Engineering, and Mathematics (STEM) education. Experience with microcontroller programming is a gateway to many topics in this discipline, such as electrical engineering and programming. Hands-on experiences using microcontrollers are critical for student engagement and deeper understanding. However, as classes and field trips transitioned online due to the COVID-19 pandemic, educators encountered many difficulties adapting the microcontroller experiments to remote online education. One challenge is that traditional computer software for microcontroller experiments is not easy to set up. In remote education, students cannot be expected to install the software and do the configurations on their own computers at home. The second problem is that it is hard to monitor the students' progress and give feedback in real-time. Even though there are many online collaborative coding platforms, none of them support microcontrollers. In this paper, we introduce a comprehensive solution for remote education featuring microcontrollers. An online education platform was developed that allows the students to program the microcontroller using CircuitPython with no software installation or configuration. It also allows instructors to monitor students' work remotely in real-time. In addition, a microcontroller development board for experiments in which students apply programming knowledge to the function of traffic lights was designed. A CircuitPython module for the development board was also developed, which allowed the students to focus more on the logic of the traffic lights and less on potential hardware issues. This online education solution can also be adapted to meet different needs by designing different development boards for different scenarios, including breadboard experiments to focus on circuits, adopting more powerful microcontrollers for advanced programming, and a variety of other applications for use in differentiated instruction. The proposed traffic lights engineering academy was provided to a local school district and got positive feedback. The experiences and best practices are also discussed in this paper. © American Society for Engineering Education, 2022.

A Mexico's Covid Traffic Light Color Prediction System Based on Mexican News

The COVID-19 pandemic has brought social life to a near standstill as many countries imposed very strict restrictions on movement to halt the spread of the virus. In Mexico, a traffic light system was implemented to indicate the crisis level to inform the society of the restrictions for each of the color stages of the system. The present work is an attempt to predict the traffic light color at the current week, and also perform a prediction for 2, 4, and even 8 weeks ahead by using Mexican news. For this work, we consider two approaches, one based on features extracted directly from the news and the other applying transfer learning. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities

One of the greatest challenges for a traffic control system is to synchronize the flow of vehicles to prevent traffic jams. This issue gets worse when there are priority vehicles, such as ambulances, trying to move through the traffic. Given the current situation, with the COVID-19 pandemic, and the trends of smart cities, in this work, we propose and simulate a traffic control system that prioritizes ambulances within large urban centers, using Fuzzy logic and IoT devices. The simulation of our proposed model was performed on the software Dojot, which is an open platform for IoT modeling. It addressed a real situation, in a path that is usually used by ambulances to get to a reference hospital in the city of Campinas, Brazil. The proposed traffic control system can also be used after the COVID-19 pandemic is over in order to improve traffic flow for other priority vehicles (e.g., firefighters and police) and increase people’s life quality within smart cities. © 2022, Springer Nature Switzerland AG.

Traffic Analysis on Intersection Using PTV Vissim

As India is in its developing stage and the traffic on the other side in India is very heterogeneous or mixed in its nature and the average growth rate of vehicles in India is about 8%. With the increase rate of urbanization in India it will lead to the considerable traffic and travel growth on the roads which will result in vehicular delays, long queues and traffic congestion. So, in this paper with the help of traffic simulation software, i.e. VISSIM, three simulation of an unsignalized intersection {Dadour and Una-Jahu, Nerchowk Rd. (NH-21),H.P} will be analyzed and will compare them on the basis of vehicular delays and long queues. These three simulation will be analyzed on the basis of real world traffic data which is less from the expectations due to the pandemic covid-19, theoretical traffic data (increase in real data by 30%) and theoretical traffic data {with traffic signals as theoretical data follows warrant 1 (Min. Vehicular Volume) shown in IRC:93:1985}. Result showed that with increase in vehicular data there was not so much variation in vehicular delays, whereas there was an increase in long queues or queue stops and whilst third simulation (with traffic lights) is done it shows that it overcomes the queue stops of the intersection. © 2022, Springer Nature Singapore Pte Ltd.

The economics of stop-and-go epidemic control.

We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates. The subsequent relaxation of containment measures allows cases to increase again until another lockdown is imposed and the cycle repeats. The policies followed by several European countries during the Covid-19 pandemic seem to fit this pattern. We show that 'stop-and-go' should lead to lower medical costs than keeping infections at the midpoint between the highs and lows produced by 'stop-and-go'. Increasing the upper and reducing the lower limits of a stop-and-go policy by the same amount would lower the average medical load. But increasing the upper and lowering the lower limit while keeping the geometric average constant would have the opposite effect. We also show that with economic costs proportional to containment, any path that brings infections back to the original level (technically a closed cycle) has the same overall economic cost.

The Traffic Light Approach: Indicators and Algorithms to Identify Covid-19 Epidemic Risk Across Italian Regions.

With the beginning of the autumn-winter season, Italy experienced an increase of SARS-CoV-2 cases, requiring the Government to adopt new restrictive measures. The national surveillance system in place defines 21 key process and performance indicators addressing for each Region/Autonomous Province: (i) the monitoring capacity, (ii) the degree of diagnostic capability, investigation and contact tracing, and (iii) the characteristics of the transmission dynamics as well as the resilience of health services. Overall, the traffic light approach shows a collective effort by the Italian Government to define strategies to both contain the spread of COVID-19 and to minimize the economic and social impact of the epidemic. Nonetheless, on what principles color-labeled risk levels are assigned on a regional level, it remains rather unclear or difficult to track.