Managing Safety and Security in the Smart City: Covid-19, Emergencies and Smart Surveillance

The chapter examines the role and potential inherent in surveillance systems in smart cities today. The Covid-19 pandemic and the resultant restrictions to mobility, on the one hand, and the need for strengthened enforcement measures highlighted the already existing weaknesses and contingencies besetting surveillance in smart cities. The chapter makes a case that the adoption of smart city surveillance and infrastructure management systems may contribute to the improvement of safety and security in the smart city as well as to an overall enhancement of the smart city's resilience. The discussion in this chapter focuses on the complex processes of data acquisition, data sharing, and data utilization to explain in which ways they all add to smart surveillance systems that-while aware of individual freedoms and privacy issues-contribute to the process of making a smart city resilient. To showcase the applicability of these findings, a wireless mesh network (WMN) surveillance system is presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

The role of transnational churches in response to Coronavirus (COVID-19) crisis in South Africa

Coronavirus (COVID-19) has caused unprecedented suffering and death among the people of South Africa. The epidemic is associated with great fear experienced by the infected, affected and the general population. This article focuses on the role played by South African transnational churches in response to the COVID-19 crises and measures taken by the government. The article is anchored on Foucault's theory of biopolitics in which he explains the emergence ofnew political strategies implemented to regulate the lives of the species being. Foucault's account as applied to the context of this article serves as an overture to his depiction of panopticism as a system of governance. In South Africa, the haunting memory of COVID-19 and the chaos associated with it has paved the way for 'biopolitics' as a system of constant surveillance to citizens and transnational churches. Stringent lockdown regulations have been implemented in this regard after COVID-19 was declared a national disaster. A qualitative research method and an interpretivist research paradigm were adopted. Data was collected using telephone interviews with 5 transnational churches located in Durban. Key findings show that transnational churches in Durban have adhered to lockdown regulations in multifarious ways. They have continued with the theology of ministry in an attempt to replace the message of fear with the message of hope. Many have recommended their congregants to stay at home and attend church services via radio and online live streaming. It recommends religion be accommodated and coexistence with scientific knowledge systems in fighting the pandemic. Science, biomedical and clinical approach is not enough to explain the behavior and illness of human beings.

Low-Cost Systematic Methodology for Rapidly Constructing a Physiological Monitoring Interface in ICU

During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user interface is very important to the surveillance system;therefore, we used web technology to develop a system that can create an interface based on user needs. When the surveillance scene needs to be changed, the surveillance location can be changed at any time, effectively reducing the costs and time required, so that patients can achieve timely and appropriate goals of treatment. ZigBee was employed to develop a monitoring system for intensive care units (ICUs). Unlike conventional GUIs, the proposed GUI enables the monitoring of various aspects of a patient, and the monitoring interface can be modified according to the user needs. A simulated ICU environment monitoring system was designed to test the effectiveness of the system. The simulated environment and monitoring nodes were set up at positions consistent with the actual clinical environments to measure the time required to switch between the monitoring scenes or targets on the GUI. A novel system that can construct ZigBee-simulated graphical monitoring interfaces on demand was proposed in this study. The locations of the ZigBee monitoring nodes in the user interface can be changed at any time. The time required to deploy the monitoring system developed in this study was 4 min on average, which is much shorter than the time required for conventional methods (131 min). The system can effectively overcome the limitations of the conventional design methods for monitoring interfaces. This system can be used to simultaneously monitor the basic physiological data of numerous patients, enabling nursing professionals to instantly determine patient status and provide appropriate treatments. The proposed monitoring system can be applied to remote medical care after official adoption.

Hospitalization for child maltreatment and other types of injury during the COVID-19 pandemic.

BACKGROUND: The possibility that child maltreatment was misclassified as unintentional injury during the COVID-19 pandemic has not been evaluated. OBJECTIVE: We assessed if child maltreatment hospitalizations changed during the pandemic, and if the change was accompanied by an increase in unintentional injuries. PARTICIPANTS AND SETTING: This study included children aged 0-4 years who were admitted for maltreatment or unintentional injuries between April 2006 and March 2021 in hospitals of Quebec, Canada. METHODS: We used interrupted time series regression to estimate the effect of the pandemic on hospitalization rates for maltreatment, compared with unintentional transport accidents, falls, and mechanical force injuries. We assessed if the change in maltreatment hospitalization was accompanied by an increase in specific types of unintentional injury. RESULTS: Hospitalizations for child maltreatment decreased from 16.3 per 100,000 (95 % CI 9.1-23.4) the year before the pandemic to 13.2 per 100,000 (95 % CI 6.7-19.7) during the first lockdown. Hospitalizations for most types of unintentional injury also decreased, but injuries due to falls involving another person increased from 9.0 to 16.5 per 100,000. Hospitalization rates for maltreatment and unintentional injury remained low during the second lockdown, but mechanical force injuries involving another person increased from 3.8 to 8.1 per 100,000. CONCLUSIONS: Hospitalizations for child maltreatment may have been misclassified as unintentional injuries involving another person during the pandemic. Children admitted for these types of unintentional injuries may benefit from closer assessment to rule out maltreatment.

Analytics Saves Lives During the COVID-19 Crisis in Chile

During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time;(2) helped allocate limited intensive care unit (ICU) capacity;(3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases;and (4) implemented a nationwide serology surveillance program that significantly influenced Chile's decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field;the effective communication of information to the population;and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD.

A Surveillance System Controlling Covid-19 in Office Environments

COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office e nvironment a n i dentification system that uses a deep learning model that prioritizes object recognition for the identification o f e mployees w ho w ear a f ace m ask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. L oss o f Smell after the next component. One person can use one disposable card to check the smell of sniffing. E ach d isposable c ard has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence a nd o ffers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person. © 2022 IEEE.

DNN and Model Combined Passive Localization and Social Distancing with Partial Inertial Aiding

At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Role of Animal Health Components in a Biosurveillance System: Concept and Demonstration

Biosurveillance defines the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision making for action at all levels. Animal health surveillance is an important component within biosurveillance systems comprising a continuum of activities from detecting biological threats, to analyzing relevant data, to managing identified threats, and embracing a One Health concept. The animal health community can strengthen biosurveillance by adopting various developments such as increasing the alignment, engagement, and participation of stakeholders in surveillance systems, exploring new data streams, improving integration and analysis of data streams for decision-making, enhancing research and application of social sciences and behavioral methods in animal health surveillance, and performing timely evaluation of surveillance systems. The aim of this paper is to explore components of a biosurveillance system from an animal health perspective and identify opportunities for the animal health surveillance community to enhance biosurveillance. Structural and operational diagrams are presented to demonstrate the required components and relevant data of animal health surveillance as an effective part within a biosurveillance system.

Intelligence Methods to Fight Covid-19 Spread

According to the COVID-19 worldwide sickness, which has wreaked devastation, over than millions of individuals from all over the globe have been afflicted. The COVID-19 virus infected a significant number of people worldwide as a result of both the latency in detecting its existence in the female organism. A.i. (AI) and Computer Vision (ML) may assist in identifying, treatment, and assessing the severity of COVID-besides all the conventional approaches now present. In order to fully understand the role of AI and ML as a crucial tool for COVID-19 and related outbreak detection, forecasting, forecasts, contacts tracking, and therapy formulation, this study aims to offer a comprehensive review of the topic. AI revolutionises diagnostic accuracy in terms of efficiency and precision. This technology holds promise for a self-driving and visible surveillance system that can enable real - time and treat people avoiding spreading the virus to others. Digital Healthcare different applications have also been discovered. This essay investigates how AI may help fight the COVID-19 pandemic. We make an effort to provide an AI-based hospital design. Ai systems (AI) is used in the infrastructure to effectively and quickly carry out health care, assessment, and treatment. © 2022 IEEE.

Surveillance Systems for Monitoring Vaccination Coverage with Vaccines Recommended for Pregnant Women, United States.

Pregnant women* and their infants are at increased risk for serious influenza, pertussis, and COVID-19-related complications, including preterm birth, low-birth weight, and maternal and fetal death. The advisory committee on immunization practices recommends pregnant women receive tetanus-toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) vaccine during pregnancy, and influenza and COVID-19 vaccines before or during pregnancy. Vaccination coverage estimates and factors associated with maternal vaccination are measured by various surveillance systems. The objective of this report is to provide a detailed overview of the following surveillance systems that can be used to assess coverage of vaccines recommended for pregnant women: Internet panel survey, National Health Interview Survey, National Immunization Survey-Adult COVID Module, Behavioral Risk Factor Surveillance System, Pregnancy Risk Assessment Monitoring System, Vaccine Safety Datalink, and MarketScan. Influenza, Tdap, and COVID-19 vaccination coverage estimates vary by data source, and select estimates are presented. Each surveillance system differs in the population of pregnant women, time period, geographic area for which estimates can be obtained, how vaccination status is determined, and data collected regarding vaccine-related knowledge, attitudes, behaviors, and barriers. Thus, multiple systems are useful for a more complete understanding of maternal vaccination. Ongoing surveillance from the various systems to obtain vaccination coverage and information regarding disparities and barriers related to vaccination are needed to guide program and policy improvements.

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

OBJECTIVES: Estimates for COVID-19-related excess mortality for African populations using local data are needed to design and implement effective control policies. METHODS: We applied time-series analysis using data from three health and demographic surveillance systems in The Gambia (Basse, Farafenni, and Keneba) to examine pandemic-related excess mortality during 2020, when the first SARS-CoV-2 wave was observed, compared to the pre-pandemic period (2016-2019). RESULTS: Across the three sites, average mortality during the pre-pandemic period and the total deaths during 2020 were 1512 and 1634, respectively (Basse: 1099 vs 1179, Farafenni: 316 vs 351, Keneba: 98 vs 104). The overall annual crude mortality rates per 100,000 (95% CI) were 589 (559, 619) and 599 (571, 629) for the pre-pandemic and 2020 periods, respectively. The adjusted excess mortality rate was 8.8 (-34.3, 67.6) per 100,000 person-month with the adjusted rate ratio (aRR) = 1.01 (0.94,1.11). The age-stratified analysis showed excess mortality in Basse for infants (aRR = 1.22 [1.04, 1.46]) and in Farafenni for the 65+ years age group (aRR = 1.19 [1, 1.44]). CONCLUSION: We did not find significant excess overall mortality in 2020 in The Gambia. However, some age groups may have been at risk of excess death. Public health response in countries with weak health systems needs to consider vulnerable age groups and the potential for collateral damage.

Monitoring Indonesian online news for COVID-19 event detection using deep learning

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.

Statistics in times of increasing uncertainty

The statistical community mobilised vigorously from the start of the 2020 SARS‐CoV‐2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence‐based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long‐term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast‐moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.

Extending and Strengthening Routine DHIS2 Surveillance Systems for COVID-19 Responses in Sierra Leone, Sri Lanka, and Uganda.

The COVID-19 pandemic challenged countries to protect their populations from this emerging disease. One aspect of that challenge was to rapidly modify national surveillance systems or create new systems that would effectively detect new cases of COVID-19. Fifty-five countries leveraged past investments in District Health Information Software version 2 (DHIS2) to quickly adapt their national public health surveillance systems for COVID-19 case reporting and response activities. We provide background on DHIS2 and describe case studies from Sierra Leone, Sri Lanka, and Uganda to illustrate how the DHIS2 platform, its community of practice, long-term capacity building, and local autonomy enabled countries to establish an effective COVID-19 response. With these case studies, we provide valuable insights and recommendations for strategies that can be used for national electronic disease surveillance platforms to detect new and emerging pathogens and respond to public health emergencies.

K-Zones: a Machine Learning-Based System to Estimate Social Distancing Violations During Pandemic Eras

The outbreak of pandemics adversely influences various aspects of people's lives, including economies, education, careers, and social relations. Therefore, many authorities worldwide resort to imposing social distancing regulations to flatten the curve of new confirmed cases. This paper proposes a Machine Learning-based social distancing violation detection system. Unlike many contributions in the literature that use pairwise distance computation running in quadratic execution time, this paper introduces a novel technique that runs in linear time. The solution is considered a Video Surveillance System, and the experimental results show how the system effectively detects not only social distancing violations but also the severity of those violations. © 2022 IEEE.

Oral Presentation: Adverse Events to COVID-19 Vaccines and Policy Considerations that Inform the Funding of Safety Surveillance in Low- and Middle-Income Countries: An International Journal of Medical Toxicology and Drug Experience

Introduction: As of April 3, 2022, 31 COVID-19 vaccines had received approval for use globally (1). With the fast-tracked development and concurrent introduction of vaccines in all countries, there is a need for equitable safety surveillance to monitor adverse events following immunization (AEFIs) in high-income and low- and middle-income countries (LMICs). Objective: To profile the AEFIs to COVID-19 vaccines, explore the difference in reported AEs between Africa and the rest of the world (RoW), and understand the policy considerations that inform the decision by funding organizations to strengthen safety surveillance systems in LMICs. Methods: We used a convergent mixed methods design involving secondary analysis of 14,671,586 AEFIs to COVID-19 vaccines reported to VigiBasea by countries in the WHO Africa Region and the RoW between December 2020 and March 2022. The qualitative component consisted of 12 in-depth interviews with key policymakers from some donor/funding organizations. Results: With 87,351 out of 14,671,586 total reported AEFIs to COVID-19 vaccines (0.6%) Africa had the second-lowest crude AEFI reporting rate of 180 AEs per 1million administered doses. Reports from females made up 73.6% of reports from African and RoW compared to 24.4% for males. The highest number of reports came from persons 18-44 years. 26.0% of the reports were serious, with death being the reason for seriousness in 5% of the reports. Statistically significant differences in AEFI reporting by gender, age group, and serious AEs were found between Africa and the RoW. AstraZeneca, PfizerBioNtech, and Janssen vaccines were the most frequently associated with AEFIs in terms of the absolute number of AEs for Africa and RoW. Sputnik V contributed the highest percentage of AEs per vaccine for Africa. Headache, pyrexia, injection site pain, dizziness, and chills were the top 5 reported AEs for Africa and RoW. Qualitative findings revealed decisions of many funding organizations to fund safety surveillance in LMICs were influenced by considerations about country priorities, the perceived utility of the evidence generated for local decision making, and the contributions to global health by safety surveillance systems. Conclusion: Countries in Africa reported fewer AEFIs to COVID-19 vaccines in VigiBase relative to the RoW, with statistically significant differences in reporting of key parameters that warrant further investigation. Funding decisions by donor organizations were influenced by country priorities and the perceived value added by data generated from safety surveillance systems in LMICs to local and global decision making.

Smart Safety Surveillance of Vaccines in Mass Immunization Campaigns: Eritrea's Evolving Experience in a Nutshell: An International Journal of Medical Toxicology and Drug Experience

Introduction: Monitoring safety of vaccines during mass immunization campaigns is challenging. Immunization programs usually focus on vaccination coverages and seldom invest time and effort in identifying and managing immunization-related safety issues. The safety surveillance initiatives taken by the Eritrean Pharmacovigilance Centre (EPC) following its integration into the Expanded Program on Immunization (EPI) in late 2016 involved tactics that evolved with the deployment of newer vaccines over the years. Currently, Eritreas experience collectively enabled the country to operate in a higher level of proactivity, implementing strategies that go in line with principles of the latest concept of Smart Safety Surveillance (3S). Objective: This report shares Eritreas experience in monitoring adverse events following immunization (AEFI) during mass campaigns in 3S approach, presenting a lesson that can be learned for COVID-19 vaccine deployment. Methods: Tools, principles, AEFI-line lists, integration progresses, practices, and strategies used in Eritrea for AEFI surveillance during immunization campaigns were reviewed. The evolution of the AEFI surveillance system in Eritrea was tracked to observe the changes that the EPC introduced in every growth stage of its system. Moreover, search was made on VigiBase to demonstrate trends of AEFIs reporting overtime. Results: In the kickoff, the EPC started small in harnessing its integration with the EPI. In the first two years following integration, the center received only a few tens of AEFI reports through an entirely passive approach of spontaneous reporting. In 2018, the center opted for a more ambitious plan and implemented an active surveillance of newly introduced measles and rubella vaccine. Although it collected 916 AEFIs in a few days, serious cases warranting investigation were left uninvestigated. In 2019, in another highly ambitious turn, the EPC optimized its system to a more proactive approach that employs principles of 3S. Through the 3S approach, the EPC implemented strategies that enabled prevention and management of AEFIs more efficiently. Following investigation, several serious cases that would otherwise have compromised the immunization program were found to be coincidental. Of the more than 3600 AEFIs received following meningococcal-A vaccination campaign, several serious cases were successfully investigated and timely communicated. The reports represent about 80% of all the globally reported cases to the vaccine in VigiBase. Conclusion: 3S approach was found to be effective in monitoring and mitigating immunization campaign-related problems in Eritrea. Considering Eritreas strategies might be helpful for countries having similar setting in establishing a robust vaccine safety surveillance system during COVID-19 vaccine deployment.

GLOBAL NEWS

Imtiaz S, Nafeh F, Russell C, Ali F, Elton-Marshall T, Rehm J. The impact of the novel coronavirus disease (COVID-19) pandemic on drug overdose-related deaths in the United States and Canada: a systematic review of observational studies and analysis of public health surveillance data. Subst Abuse Treat Prev Policy. 2021;16(1):87. https://doi.org/10.1186/s13011-021-00423-5 Monitoring PopulationLevel Physical Activity in Adolescents and Adults Namibia Nashandi et al. validated a selfreport questionnaire by comparing it to an accurate device-based method to assess moderate to vigorous physical activity (MVPA) among adolescent girls (n = 52) and women (n = 51) in Namibia. BMC Public Health. 2021;21(1):1750. https://doi.org/10.1186/s12889-021-1 1765-x Automated Syndromic Surveillance in Communities Taiwan Chan et al. describe a surveillance system to serve as a sentinel for infectious disease outbreaks in Taipei City, Taiwan, using data from primary care clinics and community hospitals and incorporating spatiotemporal information.