Outbreak Of Respiratory Infectious Disease Research Articles

Evaluating the Effectiveness of Public Health Measures During Infectious Disease Outbreaks: A Systematic Review.

Over the previous three decades, the incidence of infectious disease outbreaks has considerably increased and the trend is expected to increase further. Public health measures are essential for controlling and preventing emerging outbreaks of infectious illnesses. This study is aimed at evaluating the effectiveness of public health measures during infectious disease outbreaks by summarizing the outcomes from the available evidence in the literature. A systematic review was carried out through a detailed search strategy using specific keywords applied across different electronic databases, including the Science Direct, PubMed, and EMBASE databases. Studies published between 2015 and 2024 were included with a focus on cohorts, clinical trials, longitudinal studies, case-control, and quasi-experimental studies. Low-quality studies and those published before 2015 along withincorrect findings or measures were excluded. A standardized form was used for data extraction. The quality of included studies and the risk of bias were assessed through relevant techniques. The obtained data was narrative synthesized and findings were organized systematically. The reviewed studies revealed that public health measures are considerably effective against infectious disease outbreaks. The success of various measures such as social isolation, confinement measures, and public education on hygiene against different outbreaks of respiratory infectious diseases has been well-established in the literature. Moreover, the timing of intervention application plays a vital role in their success. The implementation in the early phase of an outbreak is highly effective, as it protects more people from infectionand controls the overall burden of the disease. The systematic review provided valuable insights into the efficiency of public health measures in monitoring outbreaks of infectious illnesses. The main findings suggest that appropriate public health interventions are effective in controlling the incidence of contagious disease outbreaks. Ongoing research strives to investigate measures that are most effective from the perspective of public health against various transmittable diseases to prevent future outbreaks.

Tuberculosis experience in possible respiratory infectious disease outbreaks with lessons learned during the COVID-19 pandemic

The analysis of the lessons learned during the COVID-19 pandemic was carried out and the provision of treatment and preventive services to the population in health care facilities was improved in case of possible emergencies in connection with possible outbreaks of respiratory infectious diseases in the Republic of Tajikistan using the experience of the anti-tuberculosis service.During the COVID-19 pandemic, individual employees of anti-tuberculosis facilities reflected the relationship of coronavirus infection with contacts at work. The relationship between the dynamics of reducing the incidence of tuberculosis among employees of specific medical institutions and the volume of implemented interventions for anti-tuberculosis infection control, which, in combination with other anti-epidemic measures, plays a significant role in reducing the risk of spreading tuberculosis infection and is an essential element of protecting the health of medical workers, has been scientifically proven. The experience of TB facilities in complying with infection control rules and regulations can be used to prepare health systems for various likely outbreaks of respiratory infections.

Analysis on the thermal performance of low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) for space heating

With increasing energy use and outbreaks of respiratory infectious diseases (such as COVID-19) in buildings, there is a growing interest in creating healthy and energy-efficient indoor environments. A novel heating system named low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) is proposed in this study. Thermal performance, indoor air quality, energy and exergy performance were investigated and compared with conventional radiant floor heating (CRFH) and conventional radiant floor heating with mixing ventilation (CRFH + MV). The results indicated that LTR-ISV had a more uniform operative temperature distribution and overall thermal sensation, and air mixing was enhanced without generating additional draft sensation. Compared with CRFH and CRFH + MV, the indoor CO2 concentration in LTR-ISV can be reduced by 1355 ppm and 400 ppm, respectively. Airborne transmission risk can also be reduced by 5.35 times. The coefficient of performance for CRFH, CRFH + MV, and LTR-ISV during working hours was 4.2, 2.5, and 3.4, respectively. The lower value of LTR-ISV was due to the high energy usage of the primary air handing unit. In the non-working hours, LTR-ISV was 0.6 and 1.3 higher compared to CRFH and CRFH + MV, respectively. The exergy efficiency of LTR-ISV, CRFH, and CRFH + MV was 81.77 %, 76.43 %, and 64.71 %, respectively. Therefore, the LTR-ISV system can meet the requirements of high indoor air quality and thermal comfort and provides a reference for the energy-saving use of low-grade energy in space heating.

Wearable bioelectronic masks for wireless detection of respiratory infectious diseases by gaseous media

Respiratory infectious diseases (H1N1, H5N1, COVID-19, etc.) are pandemics that can continually spread in the air through micro-droplets or aerosols. However, the detection of samples in gaseous media is hampered by the requirement for trace amounts and low concentrations. Here, we develop a wearable bioelectronic mask device integrated with ion-gated transistors. Based on the sensitive gating effect of ion gels, our aptamer-functionalized transistors can measure trace-level liquid samples (0.3 μL) and even gaseous media samples at an ultra-low concentration (0.1 fg/mL). The ion-gated transistor with multi-channel analysis can respond to multiple targets simultaneously within as fast as 10 min, especially without sample pretreatment. Integrating a wireless internet of things system enables the wearable mask to achieve real-time and on-site detection of the surrounding air, providing an alert before infection. The wearable bioelectronic masks hold promise to serve as an early warning system to prevent outbreaks of respiratory infectious diseases.

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.

Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern. We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics. We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite. We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly. We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.

Reflections on the Global Pandemic of COVID-19 and the Response Strategies

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to ravage the world, posing great challenges to the public health system and threatening the health and survival of humans. The outbreak of COVID-19 leaves us with much to reflect on and calls on our attention to respiratory infectious diseases, especially new outbreaks of respiratory infectious diseases that are zoonotic. The importance of identifying viral pathogens should be highlighted because it is related to the prevention and treatment strategy of childhood pneumonia. Due to the highly infectious nature of COVID-19, the rapid mutation of the virus, and the lack of specific drugs, vaccines are currently the most effective means of prevention, and a global immune barrier must be established as soon as possible.

Incidence and Antibiotic Susceptibility Profile of Pasteurella Maltocida Isolates Isolated from Goats in Savar Area of Bangladesh

In Bangladesh, goat farming is substantially hampered due to outbreaks of respiratory infectious diseases like pneumonia causing morbidity, mortality and economic losses in Black Bengal goats. Among the infectious agents, Pasteurella maltocida is more frequently associated with the outbreak of acute pneumonia and death of goats. Hence, the occurrence of P. multocida in the goat population should be regularly investigated to effectively control the disease. Furthermore, antibiotic resistance/sensitivity profiling of P. maltocida also needs to be regularly updated for designing or updating of efficient treatment strategy. In this investigation, 150 nasal swab samples from goats were collected from Savar region of Bangladesh and on the basis of colony, staining and biochemical characteristics P. multocida were isolated in 25 samples. Disc diffusion assay was used to determine the antimicrobial susceptibility of 20 isolates against 9 different antimicrobial agents. This study revealed that the rate of P. multocida isolated from goats in Savar area was about 16.67%. The incidence of P. multocida was higher in goats affected with acute pneumonia than apparently healthy goats. P. multocida was fully resistant to penicillin and amoxicillin whereas showed high sensitivity towards ciprofloxacin followed by streptomycin and neomycin. This study suggests that ciprofloxacin, streptomycin and neomycin are potent anti-Pasteurella multocida drugs.

The feasibility of outpatient data used for respiratory syndromic surveillance in general hospital

To find out the data sources of respiratory syndromes and their components from the outpatients of general hospitals and to describe the time distribution and mutual relations of different respiratory syndromes. Feasibility of respiratory syndromes used for early warning surveillance on respiratory infectious disease was also under research. Retrospective investigation on Hospital Information System (HIS) was implemented in a general hospital in Guangzhou, 2005, and data of outpatients was collected and classified into different syndromes. The respiratory syndromes with its time distribution similar to influenza like illness (ILI), were selected, and cross-correlation analyses were conducted to investigate the feasibility of respiratory syndromes for early warning surveillance on respiratory infection diseases (influenza as an example). Primary sub-classification of respiratory syndromes in outpatient department would include upper respiratory infection (URI) (51.20%), trachitis/bronchitis (18.80%), asthma (17.52%), etc. Pulmonary infection accounted for only 2.26%. Time distributions of URI, trachitis/bronchitis, pulmonary infection, cough and asthma in outpatient department, X-ray tests and pneumonia/acute respiratory distress syndromes (ARDSs) in outpatient X-ray room were similar, with two peaks observed. Cross-correlation functions were calculated with the data sets of 1(st) - 28(th) week. The most significant correlation was detected between the time series of outpatient pulmonary infections and ILIs moved 4 weeks backward (r = 0.739, P < 0.01), and that was detected between URIs and ILIs moved 5 weeks backward (r = 0.714, P < 0.01). Correlation between X-ray tests, pneumonia/ARDSs in outpatient X-ray room and ILIs was the strongest when ILIs time series moved 1 week backward (r = 0.858, P < 0.001;r = 0.821, P < 0.001). Outpatient data from HIS system in general hospital could be applied to syndromic surveillance on respiratory diseases. For early warning epidemics or outbreaks of influenza or other respiratory infectious diseases, data of outpatient pulmonary infection appeared to be the most feasible for its specificity and timeliness, followed by URI and cough. X-ray tests and pneumonia/ARDSs in outpatient X-ray findings were important supplementary to verify the respiratory disease epidemics or outbreaks for its good specificity, but with no advantage for early warning.