Measurement and Processing of Thermographic Data of Passing Persons for Epidemiological Purposes.

Non-contact temperature measurement of persons during an epidemic is the most preferred measurement option because of the safety of personnel and minimal possibility of spreading infection. The use of infrared (IR) sensors to monitor building entrances for infected persons has seen a major boom between 2020 and 2022 due to the COVID-19 epidemic, but with questionable results. This article does not deal with the precise determination of the temperature of an individual person but focuses on the possibility of using infrared cameras for monitoring the health of the population. The aim is to use large amounts of infrared data from many locations to provide information to epidemiologists so they can have better information about potential outbreaks. This paper focuses on the long-term monitoring of the temperature of passing persons inside public buildings and the search for the most appropriate tools for this purpose and is intended as the first step towards creating a useful tool for epidemiologists. As a classical approach, the identification of persons based on their characteristic temperature values over time throughout the day is used. These results are compared with the results of a method using artificial intelligence (AI) to evaluate temperature from simultaneously acquired infrared images. The advantages and disadvantages of both methods are discussed.

City-wide wastewater genomic surveillance through the successive emergence of SARS-CoV-2 Alpha and Delta variants.

Genomic surveillance of SARS-CoV-2 has provided a critical evidence base for public health decisions throughout the pandemic. Sequencing data from clinical cases has helped to understand disease transmission and the spread of novel variants. Genomic wastewater surveillance can offer important, complementary information by providing frequency estimates of all variants circulating in a population without sampling biases. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Furthermore, wastewater and clinical sequencing match well in the estimated timing of new variant rises and the first detection of a new variant in a given area may occur in either clinical or wastewater samples. The study's main limitation was sample quality when infection prevalence was low in spring 2021, resulting in a lower resolution of the rise of the Delta variant compared to the rise of the Alpha variant in the previous winter. The correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. However, discrepancies in the first detection of the Alpha variant between the two approaches highlight that wastewater monitoring can also capture missing information, possibly resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city.

Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Wearing face masks is one of the direct measures that can help tackling the spread of the new coronavirus. In this paper we presented an architecture for face mask wearing detection using pre-trained deep learning models for computer vision and implementation of them on embedded hardware platforms. Three object detection models were fine-tuned and optimized to run on 4 different hardware platforms. The fine tuning and optimization of the models resulted in significant reduction of the inference time, thus making the use of this technology in IoT based security systems for real-time automatic monitoring of face masks wearing realisable. © 2021 Crown.

Discussions on social media about digital contact tracing for COVID-19: Challenges for public health from Colombia

Introduction. Pandemics are not a new phenomenon for humanity. COVID-19 pandemic brings unprecedented concerns that merit a careful reading, especially regarding digital contact tracing. Methods. A digital ethnography was carried out between March to September of 2020 to characterize the mobile applications created in Colombia for COVID-19 digital contact tracing. Emphasis was put on CoronApp, supported by the national government, leading to an analysis of citizens' discussions about these interventions on social networks. Results. Sixteen technologies for COVID-19 were identified, among mobile tracing applications and internet applications for labor, social, and mobility monitoring. The CoronApp application had more than 10 million downloads and it was the most used in the country. Users concerns were frequent, especially apropos the lack of guarantees of confidentiality in the handling of data, invasion of privacy, risk of commercialization, the power of state, the role of digital citizens' rights, as well as the lack of clarity on the epidemiological policy behind the apps. Discussion. The COVID-19 pandemic is the first global event to be fought digitally. That means a world of possibilities for public health but social and civic challenges about the governance of health and life, as well. © 2021 Pontificia Universidad Javeriana. All rights reserved.

Digital Citizen Science for Responding to COVID-19 Crisis: Experiences from Iran.

The Coronavirus Disease 2019 (COVID-19) pandemic has so far been the most severe global public health emergency in this century. Generally, citizen science can provide a complement to authoritative scientific practices for responding to this highly complex biological threat and its adverse consequences. Several citizen science projects have been designed and operationalized for responding to COVID-19 in Iran since the infection began. However, these projects have mostly been overlooked in the existing literature on citizen science. This research sheds light on the most significant online citizen science projects to respond to the COVID-19 crisis in Iran. Furthermore, it highlights some of the opportunities and challenges associated with the strengths and weaknesses of these projects. Moreover, this study captures and discusses some considerable insights and lessons learned from the failures and successes of these projects and provides solutions to overcome some recognized challenges and weaknesses of these projects. The outcomes of this synthesis provide potentially helpful directions for current and future citizen science projects-particularly those aiming to respond to biological disasters such as the COVID-19 pandemic.

Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions.

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed-i. e., racialized categories-to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.