Social Distance Detector Using Computer Vision and Deep Learning to Combat COVID-19

The study's goal is to create a detector that detects and analyses whether pedestrians or individuals in public gatherings are maintaining social distancing. Drone-shot videos, live webcam feeds, and photographs are all kinds of input for the detector. With no human intervention, Dynamic Detection through live stream provides safety and simplifies monitoring of social distance. The webcam input can be integrated with an external webcam or a drone's camera. Furthermore, the YOLOv4 algorithm is used for the data set for the initial phase ofobject detection, identifying various items in each frame. The recognized objects are narrowed down to humans, and the Euclidian distance between one data point and every other data point is determined The Euclidian distance determines if they are maintaining the minimal distance between them or not by depicting them with a colored border box. Euclidian distance assists in detecting if they are keeping the minimal distance between them or not, as shown by a coloredboundary box, red for unsafe and green for safe, with an indication reflecting the number of people in danger. © 2022 IEEE.

COVID-19 Pandemic vis-à-vis Zamboanga Peninsula Aviation: A Predictive Analysis

The use of time-series analysis to examine aviation data trends through time comes crucial in planning its future. The prophet is an additive model that fits non-linear patterns. It functions best with historical data from various seasons and time series with significant seasonal impacts. This research looked closely into the aviation data in Zamboanga Peninsula, Jolo, and Tawi-Tawi to give a clearer picture of its impact on the sector and forecast passenger and aircraft movement in the coming months to see whether the impact of the opening in the aviation industry can be sustained. The final data comprise 51 data points for flight arrivals and departures and 51 data points for passenger arrivals and departures. Data show the decline in passengers and aircrafts arriving and departing in major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi during the pandemic. However, an increasing trend was observed years after the pandemic hit the region. Findings during the training and testing phase revealed that different models attained varied results;however, there are models which attained a higher degree of accuracy as depicted in the RMSE and R2. This indicates that predicting passenger and aircraft movement using models with higher accuracy is similar to real data thus, it is viable in predicting future values. Forecasting results further show a gradually increasing trend of aircraft and passenger arrivals in the major airports in Zamboanga Peninsula, Jolo, and Tawi-Tawi despite some observed smaller forecasted values. © 2022 IEEE.

Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic

Rapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points – distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable) – have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution. © 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

A Review and Discussion of Wastewater Process Inhibitions Observed in the U.S. as a Result of COVID-19 Countermeasures

This paper provides a summary of case studies from water resource recovery facilities (WRRFs) in the United States that have experienced wastewater process inhibitions as a result of COVID-19 countermeasures. Anecdotal feedback from staff operating impacted WRRFs and preliminary influent toxicity screening data point to quaternary ammonium compounds (QAC) in the influent as the possible cause for the inhibition events. As such, a high-level overview of QACs, and a synopsis of their fate and potential impacts in WRRFs, are summarized in this paper. Empirical evidence from full-scale facilities is presented, demonstrating that high concentrations of disinfectants used during the pandemic caused nitrification inhibition. This paper also highlights the potential of disinfectants to inhibit enhanced biological phosphorus removal (EBPR), a treatment phenomenon not yet reported on in literature to our knowledge. Finally, the authors provide recommendations for best management operational practices to mitigate inhibitory impacts at WRRFs in the future. Copyright © 2021 Water Environment Federation

Polynomial Interpolation and Cubic Spline to Determine Approximate Function of COVID-19 Tweet Dataset

As we know theoretically if we are going to construct a polynomial interpolation function through a mapped base, we create an approximation function. In this study, we try to build an approximation function using all sample data available. The approximation function obtained represents the data whose graph goes through a given set of data points. We determine the value of a function at different points and specific intervals using the interpolation model. The first derivative of the function is obtained to find the growth rate of tweet data. The experimental data is a crawling tweet with the keyword COVID-19. Then we get the amount of data per time duration representing a value of the function at a node. The interpolation includes such as Lagrange, Newton’s divided difference, and cubic spline. In this study, we compared polynomial interpolation with cubic splines to obtain optimal results. With the functional approach obtained, a pattern of tweets related to COVID-19 can be seen from its graph that passes through the given data points. The graph and the estimated values obtained show that the cubic spline is the optimal interpolation as an approximation function. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

Time Series Analysis of Infected COVID-19 Cases in the Zamboanga Peninsula, Philippines using Long Short-Term Memory Neural Networks

Infectious disease outbreaks, such as COVID-19 pandemics, exhibit patterns that can be described by the dynamics of a mathematical model This study seeks to explore the use of LSTM in order to develop models that will capture the non-linear dynamic changes of COVID-19 cases in Zamboanga Peninsula. The study uses 436 data points where the latest timestamp for the dataset is on May 29, 2021 and the oldest is on March 20, 2020. These data are taken from the DOH repositories and revalidated using the data from the DOH Regional Office. The training and testing phase results show that among the different LSTM variants, convLSTM trained using Adam and RMSProp attained the smallest RMSE result of 42.34 and 43.67 and a correlation coefficient of 0.94 0.93, respectively. ConvLSTM, when trained with Adam and RMSProp, produces the best results, as evidenced by the shortest RMSE and highest correlation coefficient. Results revealed that convLSTM appears to be a viable choice for modeling the time series of the COVID 19 infected cases in Zamboanga Peninsula Region in compared with the different variants of LSTM. © 2021 IEEE.