Comparative analysis of point-of-care diagnostic techniques for respiratory infectious diseases. Lessons we learned from the COVID-19 pandemic and future consideration on more competent alternatives

Development of a negative pressure hood for isolation and transportation of individual patient with respiratory infectious disease

Viral pathogens are a serious health threat around the world, particularly in resource limited settings, where current sensing approaches are often insufficient and slow, compounding the spread and burden of these pathogens. Here, we describe a label-free, point-of-care approach toward detection of virus particles, based on a microfluidic paper-based analytical device with integrated microwire Au electrodes. The device is initially characterized through capturing of streptavidin modified nanoparticles by biotin-modified microwires. An order of magnitude improvement in detection limits is achieved through use of a microfluidic device over a classical static paper-based device, due to enhanced mass transport and capturing of particles on the modified electrodes. Electrochemical impedance spectroscopy detection of West Nile virus particles was carried out using antibody functionalized Au microwires, achieving a detection limit of 10.2 particles in 50 μL of cell culture media. No increase in signal is found on addition of an excess of a nonspecific target (Sindbis). This detection motif is significantly cheaper (∼$1 per test) and faster (∼30 min) than current methods, while achieving the desired selectivity and sensitivity. This sensing motif represents a general platform for trace detection of a wide range of biological pathogens.

Impact of COVID-19 preventive measures on other infectious and non-infectious respiratory diseases in Pakistan

Editor's evaluation: Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Decision letter: Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

BackgroundInformation from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation.ObjectiveThis study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak.MethodsWe developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation.ResultsThe tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak.ConclusionsAIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

Coming Together for Climate and Health: Proceedings of the Second Annual Clinical Climate Change Meeting, January 24, 2020.

In 1880, Charles Laveran first visualized the protozoan parasite that causes malaria in a smear of blood under a microscope, and from the early decades of the 20th century to date, microscopy has been the mainstay of malaria detection. In laboratories throughout the world, routine diagnosis is carried out by microscopic examination of Giemsa-stained blood films for the intraerythrocytic asexual stages of the Plasmodium parasite that are indicative of active malaria infection. Clinical trials of antimalarial interventions also rely on good-quality slide reading for endpoint determination. In recent years, molecular methods for detecting the DNA of Plasmodium species that infect humans have been incorporated into some trial protocols to increase the sensitivity of detection (polymerase chain reaction [PCR] is typically 1–2 orders of magnitude more sensitive than microscopy), to distinguish genetically distinct parasite lineages, and to answer additional questions, such as the prevalence of mutations associated with drug resistance [1, 2]. A major constraint for such studies is the need to obtain blood samples repeatedly during posttreatment follow-up; these samples are often collected from infants, young children, and pregnant women, who bear the brunt of the malaria disease burden and are therefore the targets of most antimalarial interventions. Blood sampling, with its requirement for trained personnel, an attendant risk of infection and, in some countries, associated taboos can result in poor compliance when repeated testing is needed. In this issue of the Journal, Nwakanma et al. [3] have comprehensively evaluated the potential of saliva and urine samples as alternative sources of parasite DNA from Plasmodium falciparum, the species that causes the majority of malaria deaths worldwide and that predominates in subSaharan Africa. After obtaining blood, saliva, and urine samples from Gambian patients with suspected malaria infections, skilled microscopists carried out standard diagnosis and DNA was extracted from all samples for analysis by 2 methods: an established conventional nested PCR [1] was used to provide a benchmark for diagnostic sensitivity and specificity, and a quantitative PCR (qPCR) was used to estimate the amount of parasite DNA in these biological fluids. The study demonstrated that nested PCR, using DNA extracted from saliva samples, had encouragingly high sensitivity of 73% and specificity of 97%, compared with microscopic examination of peripheral blood samples obtained simultaneously. Moreover, a significant positive correlation was observed between parasite counts established by microscopy and estimates of parasite DNA in saliva by qPCR (r 0.58; P .001). When DNA extracted from urine was used as a template, the sensitivity of nested PCR compared with microscopy of blood was much lower (32%), and the correlation between counts established by microscopy and qPCR results was not significant. This suggests that either insufficient parasite DNA is excreted in the urine for these samples to provide a useful amplification template or that novel approaches to isolation, purification, and concentration are needed for DNA obtained from urine samples. In addition to diagnostic applications, the possibility that DNA from saliva samples could be used to distinguish variant parasite genotypes in samples from patients with malaria was raised by Mharakurwa et al. [4], who attempted to amplify a polymorphic region of the P. falciparum msp2 gene from blood, saliva, and urine samples obtained from individuals in Zambia who tested positive for malaria by microscopy. These authors also found that urine rarely yielded a PCR product, whereas the msp2 genotypes amplified from saliva samples regularly matched those found in blood samples obtained from the same person. The chance of amplifying parasite DNA from saliva samples improved as parasite density in the blood Received 7 January 2009; accepted 7 January 2009; electronically published 7 May 2009. Potential conflicts of interest: none reported. Financial support: European Union FP7 MALACTRES Project (R.H.); United Kingdom Health Protection Agency (C.J.S). Reprints or correspondence: Dr. Colin J. Sutherland, Dept. of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel St., London WC1E 7HT, United Kingdom (colin.sutherland@lshtm.ac.uk). The Journal of Infectious Diseases 2009; 199:1561–3 © 2009 by the Infectious Diseases Society of America. All rights reserved. 0022-1899/2009/19911-0001$15.00 DOI: 10.1086/598857 E D I T O R I A L C O M M E N T A R Y

We aimed to evaluate the relationship between use of sodium-glucose cotransporter-2 inhibitors (SGLT2is) and incidence of various respiratory and infectious diseases and site-specific fractures. Large randomized controlled trials (RCTs) of SGLT2is enrolling more than 400 subjects were included. Outcomes of interest were various serious adverse events regarding to respiratory and infectious disorders and site-specific fractures. Meta-analysis was done using risk ratio (RR) and 95% confidence interval (CI) as effect size. Thirty-two large RCTs were included in this meta-analysis. Use of SGLT2is was significantly associated with the lower incidences of 6 kinds of noninfectious respiratory diseases {e.g., Asthma (RR 0.64, 95% CI 0.43-0.96; P = 0.0299), Chronic obstructive pulmonary disease [COPD] (RR 0.75, 95% CI 0.62-0.91; P = 0.0027), and Respiratory failure (RR 0.78, 95% CI 0.61-0.99; P = 0.0447)} and 4 kinds of infectious respiratory diseases {e.g., Bronchitis (RR 0.61, 95% CI 0.46-0.81; P = 0.0007), and Pneumonia (RR 0.85, 95% CI 0.78-0.93; P = 0.0002)}. Use of SGLT2is was not significantly associated with the incidences of 31 kinds of site-specific fractures (e.g., Hip fracture, Femoral neck fracture, and Spinal fracture; P > 0.05). Our meta-analysis confirmed the benefits of SGLT2is against 6 kinds of noninfectious respiratory diseases (e.g., Asthma, COPD, and Respiratory failure) and 4 kinds of infectious respiratory diseases (e.g., Bronchitis, and Pneumonia). These findings suggest a likelihood that SGLT2is might be used to prevent or treat these respiratory diseases. Moreover, our meta-analysis for the first time revealed no association between use of SGLT2is and incidence of various site-specific fractures.

During the 2020 COVID-19 pandemic, class suspension and school closures, as non-pharmacological interventions, effectively curbed on-campus communicable diseases transmission by minimizing contact. Targeted temporary measures taken for high-risk groups and activities, such as suspending a certain group activity and isolating students with symptoms at home, can significantly reduce transmission without the need for a complete suspension. However, there is currently a lack of in-depth analysis of teacher and student contact behavior. The aim is to assess the risk of communicable respiratory diseases transmission among different populations and activities in primary school. We utilized Ultra-Wideband (UWB) wearable devices - a wireless positioning technology enabling centimeter-level proximity detection to record 143,328 close contacts among 292 teachers and students in a primary school throughout the day. By converting data into a network matrix, we constructed a dynamic contact network for the corresponding analysis and calculated network indicators. We also analyzed the contact patterns and distribution of contagion risk among different groups and activity types of contacts. Network analysis reveals that among the top 10% of individuals in terms of closeness centrality and eigenvector centrality, the proportion of younger students was the highest. Additionally,the duration of single contact was longer among younger students. High-risk contacts predominantly occurred during classes, after-school service, large recess activity and lunch break. The risk of contact was highest among teachers and students of the same grade, with an average contact duration of 57.46 and 56.17min, while inter-grade durations were only 4.15 and 1.17min. Moreover, same-grade teachers and students had higher proportion of medium-high risk and above interactions. Inter-grade contacts primarily occurred among students (38.99% medium-high-risk interactions), with 90.45% concentrated during morning self-study and after-school services. The identification of high-risk contact activities and populations on campus provides important quantitative parameters for targeted intervention particular during the early stage of outbreak.

Tuberculosis (TB) remains a leading cause of infectious disease death. New TB vaccines are currently in late-stage trials and may be available before the end of the decade. Modelling predicts new TB vaccines may reduce global burden but rely on assumptions about vaccine efficacy by TB disease stage and TB natural history, which may be incorrect. We explored the sensitivity of estimates of the impact of new TB vaccines to uncertainties in efficacy by disease stage and natural history. We developed a dynamic compartmental TB model for India, including early TB disease stages (non-infectious disease, infectious asymptomatic disease, and infectious symptomatic disease). Scenarios assumed 50% vaccine efficacy for 10 years and prevented progression to (a) only infectious symptomatic disease, or (b) any infectious disease (infectious asymptomatic disease and infectious symptomatic disease), or (c) any disease (non-infectious disease, infectious asymptomatic disease, and infectious symptomatic disease). We estimated impact on averting disease episodes over 2030-2050, compared to no-new-vaccine introduction. Results suggest, over 3 years, there was little difference in the proportion of cumulative symptomatic disease episodes averted by vaccines preventing only infectious symptomatic disease, any infectious disease, or any disease (1.6%, 2.3%, and 2.3%, respectively). However, over 20 years, compared to vaccines preventing only infectious symptomatic disease, vaccines preventing any infectious disease, or any disease, averted a markedly higher proportion of symptomatic disease episodes (7.3%, 19.4%, and 23.3%, respectively), due to preventing continued transmission from infectious asymptomatic disease. A key limitation with any mathematical modelling study is the uncertainty associated with the inputs, and further data collection is required to better understand the transmissibility, morbidity, and dynamics of asymptomatic disease, to improve modelling estimates and inform wider policy. Our modelling estimates that the population impact of new TB vaccines may depend on efficacy against infectious asymptomatic disease. TB vaccine trials should include analyses of participant sputum samples (collected during or at the end of trials and analysed at the end of trials) to enable better estimates of the potential value of new TB vaccines against infectious asymptomatic disease.

Guidance on the use of respiratory and facial protection equipment

Mortality profile of patients with rheumatoid arthritis in France and its change in 10 years.

Infection risk assessment is very useful in understanding the transmission dynamics of infectious diseases and in predicting the risk of these diseases to the public. Quantitative infection risk assessment can provide quantitative analysis of disease transmission and the effectiveness of infection control measures. The Wells–Riley model has been extensively used for quantitative infection risk assessment of respiratory infectious diseases in indoor premises. Some newer studies have also proposed the use of dose‐response models for such purpose. This study reviews and compares these two approaches to infection risk assessment of respiratory infectious diseases. The Wells–Riley model allows quick assessment and does not require interspecies extrapolation of infectivity. Dose‐response models can consider other disease transmission routes in addition to airborne route and can calculate the infectious source strength of an outbreak in terms of the quantity of the pathogen rather than a hypothetical unit. Spatial distribution of airborne pathogens is one of the most important factors in infection risk assessment of respiratory disease. Respiratory deposition of aerosol induces heterogeneous infectivity of intake pathogens and randomness on the intake dose, which are not being well accounted for in current risk models. Some suggestions for further development of the risk assessment models are proposed.Practical ImplicationsThis review article summarizes the strengths and limitations of the Wells–Riley and the dose‐response models for risk assessment of respiratory diseases. Even with many efforts by various investigators to develop and modify the risk assessment models, some limitations still persist. This review serves as a reference for further development of infection risk assessment models of respiratory diseases. The Wells–Riley model and dose‐response model offer specific advantages. Risk assessors can select the approach that is suitable to their particular conditions to perform risk assessment.

Objective To analyze the epidemiological problems concerning cruiser-related infectious diseases reported since 1973, so as to provide evidences for the diagnosis, prevention and control of diseases. Methods Information retrieval was made by using Pubmed, Medline and China Knowledge Resource Integrated Database (CNKI) to find out papers concerning cruiser-related infectious diseases reported from 1973 to the present, and at the same time, data concerning cruiser-related gastrointestinal infectious diseases published by the Center for Disease Control (CDC) were also sorted out, and finally statistical analyses were made on such related factors as the category of diseases, types of pathogens and the areas of navigation. Results Research results indicated that gastrointestinal infectious diseases and respiratory infectious diseases, the gastrointestinal infectious diseases in particular, were the two main types of cruiser-related infectious diseases. Norovirus, shigella and Escherichia coli were the most commonly detected pathogens in the gastrointestinal infectious disease pathogens, whereas the most pathogenic factor for respiratory diseases were influenza virus, followed by varicella and legionella. In all the cruisers that were affected by infectious diseases, those sailing in tropical waters had the higher incidence rate, which was significantly higher than those sailing in temperate and frigid zones, which accounted for 72.72%, 13.64% and 13.64% respectively. Conclusions Gastrointestinal infectious diseases and respiratory infectious diseases were the main types of cruiser-related infectious diseases, and the distribution of pathogens had their special patterns, which were closely related with the geographic regions of sailing. Knowledge about cruiser-related infectious diseases was of important significance for rapid diagnosis, prevention and control of related diseases. Key words: Cruiser; Infectious diseases; Gastrointestinal infectious diseases; Respiratory infectious diseases; Tropical waters