Do Infrared Thermometers Hold Promise for an Effective Early Warning System for Emerging Respiratory Infectious Diseases?

BACKGROUND: Major respiratory infectious diseases, such as influenza, SARS-CoV, and SARS-CoV-2, have caused historic global pandemics with severe disease and economic burdens. Early warning and timely intervention are key to suppress such outbreaks. OBJECTIVE: We propose a theoretical framework for a community-based early warning (EWS) system that will proactively detect temperature abnormalities in the community based on a collective network of infrared thermometer-enabled smartphone devices. METHODS: We developed a framework for a community-based EWS and demonstrated its operation with a schematic flowchart. We emphasize the potential feasibility of the EWS and potential obstacles. RESULTS: Overall, the framework uses advanced artificial intelligence (AI) technology on cloud computing platforms to identify the probability of an outbreak in a timely manner. It hinges on the detection of geospatial temperature abnormalities in the community based on mass data collection, cloud-based computing and analysis, decision-making, and feedback. The EWS may be feasible for implementation considering its public acceptance, technical practicality, and value for money. However, it is important that the proposed framework work in parallel or in combination with other early warning mechanisms due to a relatively long initial model training process. CONCLUSIONS: The framework, if implemented, may provide an important tool for important decisions for early prevention and control of respiratory diseases for health stakeholders.

Postacute Sequelae of SARS-CoV-2 in University Setting.

Postacute sequelae of SARS-CoV-2 infection, commonly known as long COVID, is estimated to affect 10% to 80% of COVID-19 survivors. We examined the prevalence and predictors of long COVID from a sample of 1,338 COVID-19 cases among university members in Washington, DC, USA, during July 2021‒March 2022. Cases were followed up after 30 days of the initial positive result with confidential electronic surveys including questions about long COVID. The prevalence of long COVID was 36%. Long COVID was more prevalent among those who had underlying conditions, who were not fully vaccinated, who were female, who were former/current smokers, who experienced acute COVID-19 symptoms, who reported higher symptom counts, who sought medical care, or who received antibody treatment. Understanding long COVID among university members is imperative to support persons who have ongoing symptoms and to strengthen existing services or make referrals to other services, such as mental health, exercise programs, or long-term health studies.

Assessment Model for Rapid Suppression of SARS-CoV-2 Transmission under Government Control.

The rapid suppression of SARS-CoV-2 transmission remains a priority for maintaining public health security throughout the world, and the agile adjustment of government prevention and control strategies according to the spread of the epidemic is crucial for controlling the spread of the epidemic. Thus, in this study, a multi-agent modeling approach was developed for constructing an assessment model for the rapid suppression of SARS-CoV-2 transmission under government control. Different from previous mathematical models, this model combines computer technology and geographic information system to abstract human beings in different states into micro-agents with self-control and independent decision-making ability; defines the rules of agent behavior and interaction; and describes the mobility, heterogeneity, contact behavior patterns, and dynamic interactive feedback mechanism of space environment. The real geospatial and social environment in Taiyuan was considered as a case study. In the implemented model, the government agent could adjust the response level and prevention and control policies for major public health emergencies in real time according to the development of the epidemic, and different intervention strategies were provided to improve disease control methods in the simulation experiment. The simulation results demonstrate that the proposed model is widely applicable, and it can not only judge the effectiveness of intervention measures in time but also analyze the virus transmission status in complex urban systems and its change trend under different intervention measures, thereby providing scientific guidance to support urban public health safety.

Analyzing the Impact of COVID-19 Lockdowns on Violent Crime.

Existing research suggests that COVID-19 lockdowns tend to contribute to a decrease in overall urban crime rates. Most studies have compared pre-lockdown and post-lockdown periods to lockdown periods in Western cities. Few have touched on the fine variations during lockdowns. Equally rare are intracity studies conducted in China. This study tested the relationship between violent crime and COVID-19 lockdown policies in ZG City in southern China. The distance from the isolation location to the nearest violent crime site, called "the nearest crime distance", is a key variable in this study. Kernel density mapping and the Wilcoxon signed-rank test are used to compare the pre-lockdown and post-lockdown periods to the lockdown period. Panel logistic regression is used to test the fine variations among different stages during the lockdown. The result found an overall decline in violent crime during the lockdown and a bounce-back post-lockdown. Violent crime moved away from the isolation location during the lockdown. This outward spread continued for the first two months after the lifting of the lockdown, suggesting a lasting effect of the lockdown policy. During the lockdown, weekly changes in COVID-19 risk ratings at the district level in ZG City also affected changes in the nearest crime distance. In particular, an increase in the risk rating increased that distance, and a drop in the risk rating decreased that distance. These findings add new results to the literature and could have policy implications for joint crime and pandemic prevention and control.

The Characteristics of Student SARS-CoV-2 Cases on an Urban University Campus: Observational Study.

BACKGROUND: Academic institutions are central hubs for young adults, laden with academic and social interactions and communal living arrangements, heightening the risk of transmission of many communicable diseases, including COVID-19. Shortly after the start of the fall 2020 academic year, institutions of higher learning were identified as hot spots for rises in COVID-19 incidence among young adults. OBJECTIVE: This study aims to identify the characteristics of student SARS-CoV-2 cases, identify the extent to which the student population adhered to preventative strategies, and examine behaviors that would put them at higher risk of contracting or spreading COVID-19. METHODS: This observational study comprises 1175 university students at The George Washington University in Washington, DC, with a confirmed COVID-19 diagnosis between August 3, 2020, and November 30, 2021. Case investigation and contact tracing tools were developed by the Campus COVID-19 Support Team and captured in REDCap (Research Electronic Data Capture). Trained case investigators were notified of a case and attempted to contact all cases within 24 hours of the case receiving their lab result. Associations between case characteristics and number of contacts were examined using Wilcoxon rank sum tests. Knowledge of exposure, behaviors since exposure, student residence status, and fraternity and sorority life affiliation were examined using chi-square tests. RESULTS: Positive student cases reported a median of 3 close contacts, and 84.6% (993/1175) reported at least one symptom with a median of 4 COVID-19 symptoms. Congestion (628/1175, 53.4%), cough (530/1175, 45.1%), and headache (484/1175, 41.2%) were the most frequently reported symptoms. Moreover, 36% (415/1160) reported that they did not know how they were exposed to the virus. Among those aware of contact with a COVID-19 confirmed case, 55.1% (109/198) reported the contact was a close friend or family member, and 25.3% (50/198) reported that it was someone with whom they lived. Athlete (vs nonathlete; P<.001), on-campus (vs off-campus; P<.001), and undergraduate (vs graduate; P=.01) students all reported a significantly higher number of contacts. Students living on campus were more likely to report attending campus events in the 2 days prior to symptom onset or positive test result (P=.004). Students with fraternity or sorority affiliation were more likely to report attending campus events in the 2 days prior to symptom onset or positive test result (P<.001). CONCLUSIONS: COVID-19 cases have not yet stabilized to a predictable state, but this study provides case characteristics and insights for how academic institutions might prepare to mitigate outbreaks on their campuses as the world plans for the transition from pandemic to endemic COVID-19.

COVIDHunter: COVID-19 Pandemic Wave Prediction and Mitigation via Seasonality Aware Modeling.

Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.

COVID-19 containment on a college campus via wastewater-based epidemiology, targeted clinical testing and an intervention.

Wastewater-based epidemiology has potential as an early-warning tool for determining the presence of COVID-19 in a community. The University of Arizona (UArizona) utilized WBE paired with clinical testing as a surveillance tool to monitor the UArizona community for SARS-CoV-2 in near real-time, as students re-entered campus in the fall. Positive detection of virus RNA in wastewater lead to selected clinical testing, identification, and isolation of three infected individuals (one symptomatic and two asymptomatic) that averted potential disease transmission. This case study demonstrated the value of WBE as a tool to efficiently utilize resources for COVID-19 prevention and response. Thus, WBE coupled with targeted clinical testing was further conducted on 13 dorms during the course of the Fall semester (Table 3). In total, 91 wastewater samples resulted in positive detection of SARS-CoV-2 RNA that successfully provided an early-warning for at least a single new reported case of infection (positive clinical test) among the residents living in the dorm. Overall, WBE proved to be an accurate diagnostic for new cases of COVID-19 with an 82.0% positive predictive value and an 88.9% negative predictive value. Increases in positive wastewater samples and clinical tests were noted following holiday-related activities. However, shelter-in-place policies proved to be effective in reducing the number of daily reported positive wastewater and clinical tests. This case study provides evidence for WBE paired with clinical testing and public health interventions to effectively contain potential outbreaks of COVID-19 in defined communities.

Surgical site infections: guidance for elective surgery during the SARS-CoV-2 pandemic - international recommendations and clinical experience.

BACKGROUND: The COVID-19 pandemic not only had an impact on public life and healthcare facilities in general, but also affected established surgical workflows for elective procedures. The strategy to protect patients and healthcare workers from infection by SARS-CoV-2 in surgical departments has needed step-by-step development. Based on the evaluation of international recommendations and guidelines, as well as personal experiences in a clinical 'hot spot' and in a 450-bed surgical clinic, an adapted surgical site infection (SSI) prevention checklist was needed to develop concise instructions, which described roles and responsibilities of healthcare professionals that could be used for wider guidance in pandemic conditions. METHOD: Publications of COVID-19-related recommendations and guidelines, produced by health authorities and organizations, such as WHO, US-CDC, ECDC, the American College of Surgery and the Robert Koch Institute, were retrieved, assessed and referenced up to 31st January 2020. Additionally, clinical personal experiences in Germany were evaluated and considered. RESULTS: Part 1 of this guidance summarizes the experience of a tertiary care, surgical centre which utilized redundant hospital buildings for immediate spatial separation in a 'hot spot' COVID-19 area. Part 2 outlines the successful screening and isolation strategy in a surgical clinic in a region of Germany with outbreaks in surrounding medical centres. Part 3 provides the synopsis of personal experiences and international recommendations suggested for implementation during the COVID-19 pandemic. CONCLUSION: Understanding of COVID-19, and SARS-CoV-2-related epidemiology, is constantly and rapidly changing, requiring continuous adaptation and re-evaluation of recommendations. Established national and local guidelines for continuation of surgical services and prevention of SSI require ongoing scrutiny and focused implementation. This manuscript presents a core facility checklist to support medical institutions to continue their clinical and surgical work during the COVID-19 pandemic.

A Case Series: Successfully Preventing COVID-19 Outbreak in a Residential Community Setting at a Drug and Alcohol Addiction Treatment Center.

Coronavirus disease 2019 (COVID-19) has reduced the capacity of many addiction treatment centers, limiting access to safe, continual treatment for people with substance use disorders (SUD) in the setting of a pandemic. Here, we describe the COVID-19 screening process of a residential addiction treatment center in rural Connecticut that has had no outbreaks, closures, or reductions in capacity since the pandemic began. Out of 420 patients screened for COVID-19 from 1 February to 1 July, five patients tested positive for COVID-19: four prior to entering its residential community setting, and one after entering the residential community, resulting in no COVID-19 spread to other patients. Patient 1 presented from home and tested positive during screening prior to entry into the community. The primary care provider for patient 2 notified staff of a recent pos-itive COVID-19 test prior to the patient's arrival on-site. Patient 3 had a COVID-19 infection in the weeks prior to arrival and tested positive during initial screening. Patient 4 tested positive af-ter coming from another addiction treatment facility that was shut down due to a COVID-19 outbreak. Patient 5 tested negative for COVID-19 during initial screening, entered the residential community, and later tested positive. It is imperative that in-person support for SUD continues during the pandemic. This case report highlights the importance of implementing a variety of tools in an effective screening process, including polymerase chain reaction screening and daily symptomology and temperature screening, which may help prevent further closures or reductions in capacity of addiction treatment centers during the COVID-19 pandemic or future outbreaks.

Development, validity and reliability study of a compliance scale for the COVID-19 outbreak prevention recommendations

Background/aim: During the intense periods of the COVID-19 pandemic, legal measures were taken for its containment. However, since legal precautions cannot be implemented continuously, hand washing, mask usage and obeying social distance rules are important in combating the pandemic. Complying with these rules is mostly individual decisions. The behavior of individuals has a prominent place in the course of the pandemic. In this study, we aimed to develop a scale which could measure compliance with outbreak measures. Materials and methods: This study was conducted in two stages after evaluation of the content validity of the item pool formed by the research group by experts. For construct validity, the scale subdimensions were determined in 250 people between the ages of 18-70 years at the first stage and the definitive version of the 20-item scale was constructed. In the second stage, exploratory factor analysis was repeated in a group of 484 people, and confirmatory factor analysis was performed. Cronbach's alpha coefficients, Spearman-Brown coefficients, test-retest methods were used to determine reliability. Results: The variance explanation of the scale consisting of 20 items and two subdimensions in the explanatory factor analysis is 63.434% (n = 484). Confirmatory factor analysis resulted in CMIN/DF = 3.540, RMR = 0.043, NFI Delta 1 = 0.928, TLI rho 2 = 0.939, CFI = 0.947, RMSEA = 0.072, SRMR = 0.0368. Cronbach's alpha value of the scale is 0.95; and the Spearman Brown coefficient equal length analysis resulted in 0.928. The temporal consistency of the scale was evaluated with the test-retest method (P = 0.893). The structure, content validity, temporal consistency, item discrimination, and internal consistency were evaluated and found to have acceptable valid, reliable properties. Conclusion: The outbreak prevention recommendation compliance scale is a valid and reliable tool with which compliance with the prevention plans can be evaluated.