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.

Transformation of chronic disease management: Before and after the COVID-19 outbreak.

Adults with chronic diseases often experience a decline in their quality of life along with frequent exacerbations. These diseases can cause anxiety and impose a significant economic burden. Self-management is a crucial aspect of treatment outside of the hospital and can improve quality of life and reduce the financial burden resulting from unexpected hospitalizations. With the COVID-19 pandemic, telehealth has become a vital tool for both medical professionals and patients; many in-person appointments have been canceled due to the pandemic, leading to increased reliance on online resources. This article aimed to discuss various methods of chronic disease management, both traditional self-management and modern telehealth strategies, comparing before and after the COVID-19 outbreak and highlighting challenges that have emerged.

Analysis of serum SARS-CoV-2 antibody IgG tests after booster vaccination of COVID-19 vaccine in 205 individuals

Objective To analyze IgG test results of serum SARS-CoV-2 antibody in people after booster vaccinations against SARS-CoV-2, and to provide a basis for the booster vaccination. Methods There were 314 healthy individuals who had been vaccinated with the COVID-19 vaccine. Depending on their inoculation situation, they were divided into three groups:the booster injection group(1 week to 2 months after booster vaccination)of 205 cases, <180 days after two doses group(<180 days after two doses of COVID-19 vaccine)of 49 cases, and >180 days after two doses group(>180 days after two doses of COVID-19 vaccine)of 60 cases. The positive rate of IgG in serum of the three groups was measured using the colloidal gold method. Results The serum COVID-19 antibody IgG positive rates were 83.9% in the booster injection group, 18.4% in the <180 days after two doses group, and 5.0% in the >180 days after two doses group, with statistically significant difference between any two groups(all P < 0.05). In the booster injection group, the serum COVID-19 antibody IgG positive rate was 85.2% in people who received a booster injection more than a month, while those who received a booster injection less than a month had a positive rate of 75.9%, and there was no significant difference between these two groups(P > 0.05). In the booster injection group, the positive rates of serum COVID-19 antibody IgG were 85.1% in males and 82.4% in females, with no significant difference(P > 0.05). In the booster injection group, people at the age of 18 and 50 had a positive serum COVID-19 antibody IgG rate of 86.0%, while those over 50 had a positive rate of 58.3%, and there was significant difference between them(P < 0.05). Conclusions Compared with two injections of the COVID-19 vaccine, the booster injection can significantly increase the positive rate of the antibody IgG of COVID-19, which results in a stronger immune response. There is a lower IgG positive rate of COVID-19 antibodies in those aged over 50 years following the booster dose of COVID-19 vaccine than in those aged 18- 50 years.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model.

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

A Review of Non-Invasive Drug Delivery through Respiratory Routes.

With rapid and non-invasive characteristics, the respiratory route of administration has drawn significant attention compared with the limitations of conventional routes. Respiratory delivery can bypass the physiological barrier to achieve local and systemic disease treatment. A scientometric analysis and review were used to analyze how respiratory delivery can contribute to local and systemic therapy. The literature data obtained from the Web of Science Core Collection database showed an increasing worldwide tendency toward respiratory delivery from 1998 to 2020. Keywords analysis suggested that nasal and pulmonary drug delivery are the leading research topics in respiratory delivery. Based on the results of scientometric analysis, the research hotspots mainly included therapy for central nervous systems (CNS) disorders (Parkinson's disease, Alzheimer's disease, depression, glioblastoma, and epilepsy), tracheal and bronchial or lung diseases (chronic obstructive pulmonary disease, asthma, acute lung injury or respiratory distress syndrome, lung cancer, and idiopathic pulmonary fibrosis), and systemic diseases (diabetes and COVID-19). The study of advanced preparations contained nano drug delivery systems of the respiratory route, drug delivery barriers investigation (blood-brain barrier, BBB), and chitosan-based biomaterials for respiratory delivery. These results provided researchers with future research directions related to respiratory delivery.

Preliminary Study of the Protectiveness of Vaccination Against the COVID-19 in the Outbreak of VOC Omicron BA.2 - Jilin City, Jilin Province, China, March 3-April 12, 2022.

What is already known about this topic?: An outbreak of coronavirus disease 2019 (COVID-19) of Omicron BA.2 emerged in Jilin City since March 3, 2022, which involved in 27,036 cases by April 12. The vaccination program with inactivated COVID-19 vaccines has been implemented since the beginning of 2021. What is added by this report?: The incidences of moderate, severe, and critical cases in the whole population of the group of 0+1 dose were 1.82-, 9.49-, and 3.85-fold higher than those in the group of 2 doses, and 5.03-, 44.47-, and ∞-fold higher than those received 3 doses vaccination. For the population ≥60 years, the incidences of moderate, severe, and critical cases in the group of 0+1 dose were 29.92, 9.62, and 4.27 per 100,000, showing 4.13-, 43.72-, and 4.85-fold higher than 2 doses, as well as 13.28-, 22.37-, and ∞-fold higher than 3 doses. What are the implications for public health practice?: The incidences of each type of COVID-19 in the population who were fully vaccinated or booster vaccinated in Jilin City were significantly lower than those who were unvaccinated and/or partially vaccinated. Booster vaccination with homologous inactivated vaccines induces stronger protectiveness for COVID-19 caused by variant of concern (VOC) Omicron.

Assessment of radiation doses and image quality of multiple low-dose CT exams in COVID-19 clinical management.

Purpose: The Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 from an outbreak of unexplained pneumonia in Wuhan (Hubei, China) that subsequently spread rapidly around the world. Because of the public health emergency, chest CT has been widely used for sensitive detection and diagnosis, monitoring the changes of lesions and also for treatment evaluation. The purpose of this study was to investigate radiation dose and image quality of chest CT scans received by COVID-19 patients and to evaluate the oncogenic risk of multiple chest CT examinations. Methods: A retrospective review of 33 patients with RT-PCR confirmed COVID-19 infection was performed from January 31, 2020 to February 19, 2020. The date of each CT exam and respective radiation dose for each exam was recorded for all patients. Multiple pulmonary CT scans were obtained during diagnosis and treatment procedure. Scan frequency, total scan times, radiation dose, and image quality were determined. Results: Thirty-three patients (15 males and 18 females, age 21-82 years) with confirmed COVID-19 pneumonia underwent a total of 143 chest CT scans. The number of CT scans per patient was 4 ± 1, with a range of 2-6. The time interval between two consecutive chest CT scans was 3 ± 1 days. The average effective dose from a single chest CT scan was 1.21 ± 0.10 mSv, with a range of 1.02-1.44 mSv. The average cumulative effective dose per patient was 5.25 ± 1.52 mSv, with a range of 2.24-7.48 mSv. The maximum cumulative effective dose was 7.48 mSv for six CT examinations during COVID-19 treatment. Based on subjective image quality analysis, the visual scoring of CT findings was 11.23 ± 1.35 points out of 15 points. Conclusions: The frequency, total number and image quality of chest CT scans should be reviewed carefully to guarantee minimally required CT scans during the COVID-19 management.

Assessment of radiation doses and image quality of multiple low-dose CT exams in COVID-19 clinical management

<h4>Purpose</h4> The Corona Virus Disease 2019 (COVID-19) was first reported in December 2019 from an outbreak of unexplained pneumonia in Wuhan (Hubei, China) that subsequently spread rapidly around the world. Because of the public health emergency, chest CT has been widely used for sensitive detection and diagnosis, monitoring the changes of lesions and also for treatment evaluation. The purpose of this study was to investigate radiation dose and image quality of chest CT scans received by COVID-19 patients and to evaluate the oncogenic risk of multiple chest CT examinations. <h4>Methods</h4> A retrospective review of 33 patients with RT-PCR confirmed COVID-19 infection was performed from January 31, 2020 to February 19, 2020. The date of each CT exam and respective radiation dose for each exam was recorded for all patients. Multiple pulmonary CT scans were obtained during diagnosis and treatment procedure. Scan frequency, total scan times, radiation dose, and image quality were determined. <h4>Results</h4> Thirty-three patients (15 males and 18 females, age 21–82 years) with confirmed COVID-19 pneumonia underwent a total of 143 chest CT scans. The number of CT scans per patient was 4 ± 1, with a range of 2–6. The time interval between two consecutive chest CT scans was 3 ± 1 days. The average effective dose from a single chest CT scan was 1.21 ± 0.10 mSv, with a range of 1.02–1.44 mSv. The average cumulative effective dose per patient was 5.25 ± 1.52 mSv, with a range of 2.24–7.48 mSv. The maximum cumulative effective dose was 7.48 mSv for six CT examinations during COVID-19 treatment. Based on subjective image quality analysis, the visual scoring of CT findings was 11.23 ± 1.35 points out of 15 points. <h4>Conclusions</h4> The frequency, total number and image quality of chest CT scans should be reviewed carefully to guarantee minimally required CT scans during the COVID-19 management.

Serum nitrite and nitrate: A potential biomarker for post-covid-19 complications?

Nitric oxide (NO) plays an important role in cardiovascular and immune systems. Quantification of blood nitrite and nitrate, two relatively stable metabolites of NO (generally as NOx), has been acknowledged, in part, representing NO bioactivity. Dysregulation of NOx had been reported in SARS-CoV-2 infected populations, but whether patients recovered from COVID-19 disease present with restored NOx is unknown. In this study, serum NO2- and NO3- were quantified and analyzed among 109 recovered adults in comparison to a control group of 166 uninfected adults. Nitrite or nitrate levels were not significantly different among mild-, common-, severe- and critical-type patients. However, these recovered patients had dramatically lower NO2- and NO2-/NO3- than the uninfected group (p < 0.0001), with significantly higher NO3- levels (p = 0.0023) than the uninfected group. Nitrate and nitrite/nitrate were positively and negatively correlated with patient age, respectively, with age 65 being a turning point among recovered patients. These results indicate that low NO2-, low NO2-/NO3- and high NO3- may be potential biomarkers of long-term poor or irreversible outcomes after SARS-CoV-2 infection. It suggests that NO metabolites might serve as a predictor to track the health status of recovered COVID-19 patients, highlighting the need to elucidate the role of NO after SARS-CoV-2 infection.

Clinical characteristics and outcomes in adult patients hospitalized with influenza, respiratory syncytial virus and human metapneumovirus infections.

Objectives: To compare the clinical characteristics and outcomes of patients hospitalized with respiratory syncytial virus (RSV), human metapneumovirus (hMPV), and influenza infections.Methods: This study prospectively enrolled 594 patients hospitalized with influenza-like illness (ILI) and laboratory-confirmed RSV, hMPV, or influenza infections over three consecutive influenza seasons at a tertiary hospital in China.Results: While certain clinical features were of value as predictors of infection type, none exhibited good predictive performance as a means of discriminating between these three infections (area under the receiver-operating characteristic curve < 0.70). After controlling for potential confounding variables, RSV infections in pneumonia patients were found to be associated with a 30-day mortality risk comparable to that of influenza patients [odds ratio (OR) 1.016, 95% confidence interval (CI) 0.267-3.856, p = 0.982], whereas hMPV infection was associated with a reduced risk of mortality (OR 0.144, 95% CI 0.027-0.780, p = 0.025). Among those without pneumonia, the 30-day mortality risk in patients with influenza was comparable to that in patients infected with RSV (OR 1.268, 95% CI 0.172-9.355, p = 0.816) or hMPV (OR 1.128, 95% CI 0.122-10.419, p = 0.916).Conclusion: Disease severity associated with these three types of viral infection was inconsistent when comparing patients with and without pneumonia, highlighting the importance of etiologic testing.

Evaluation of work resumption strategies after COVID-19 reopening in the Chinese city of Shenzhen: a mathematical modeling study.

OBJECTIVES: As China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. STUDY DESIGN: Based on the classic SEITR model and combined with population mobility, a compartmental model was constructed to simulate the transmission of COVID-19 and disease progression in the Shenzhen population. METHODS: Based on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios. RESULTS: Imported cases account for most (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage after work resumption, various resumptions resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption. CONCLUSION: Regardless of the work resumption strategy adopted in Shenzhen, the risk of a resurgence of COVID-19 after its reopening was limited. The strict control of imported cases and extensive use of facial masks play a key role in COVID-19 prevention.

Early characteristics of the COVID-19 outbreak predict the subsequent epidemic scope.

OBJECTIVES: The mostly-resolved first wave of the COVID-19 epidemic in China provided a unique opportunity to investigate how the initial characteristics of the COVID-19 outbreak predict its subsequent magnitude. METHODS: We collected publicly available COVID-19 epidemiological data from 436 Chinese cities from 16th January-15th March 2020. Based on 45 cities that reported >100 confirmed cases, we examined the correlation between early-stage epidemic characteristics and subsequent epidemic magnitude. RESULTS: We identified a transition point from a slow- to a fast-growing phase for COVID-19 at 5.5 (95% CI, 4.6-6.4) days after the first report, and 30 confirmed cases marked a critical threshold for this transition. The average time for the number of confirmed cases to increase from 30 to 100 (time from 30-to-100) was 6.6 (5.3-7.9) days, and the average case-fatality rate in the first 100 confirmed cases (CFR-100) was 0.8% (0.2-1.4%). The subsequent epidemic size per million population was significantly associated with both of these indicators. We predicted a ranking of epidemic size in the cities based on these two indicators and found it highly correlated with the actual classification of epidemic size. CONCLUSIONS: Early epidemic characteristics are important indicators for the size of the entire epidemic.