Abdullah Al Shafi, Rowzatul Zannat, Abdul Muntakim
et al.
Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance
Shan Shen, Yibin Zhang, Hector Rodriguez Rodriguez
et al.
Graph representation learning is a powerful method to extract features from graph-structured data, such as analog/mixed-signal (AMS) circuits. However, training deep learning models for AMS designs is severely limited by the scarcity of integrated circuit design data. In this work, we present CircuitGPS, a few-shot learning method for parasitic effect prediction in AMS circuits. The circuit netlist is represented as a heterogeneous graph, with the coupling capacitance modeled as a link. CircuitGPS is pre-trained on link prediction and fine-tuned on edge regression. The proposed method starts with a small-hop sampling technique that converts a link or a node into a subgraph. Then, the subgraph embeddings are learned with a hybrid graph Transformer. Additionally, CircuitGPS integrates a low-cost positional encoding that summarizes the positional and structural information of the sampled subgraph. CircuitGPS improves the accuracy of coupling existence by at least 20\% and reduces the MAE of capacitance estimation by at least 0.067 compared to existing methods. Our method demonstrates strong inherent scalability, enabling direct application to diverse AMS circuit designs through zero-shot learning. Furthermore, the ablation studies provide valuable insights into graph models for representation learning.
Yalewayker Gashaw, Zelalem Asmare, Mitkie Tigabie
et al.
Abstract Background Antimicrobial resistance among Enterobacteriaceae poses a significant global threat, particularly in developing countries. Colistin, a critical last-resort treatment for infections caused by carbapenem-resistant and multidrug-resistant strains, is increasingly facing resistance due to inappropriate use of colistin and the spread of plasmid-mediated resistance genes. Despite the significance of this issue, comprehensive and updated data on colistin resistance in Africa is lacking. Thus, the current study was aimed to determine the pooled prevalence of colistin-resistant Enterobacteriaceae in Africa. Methods A systematic search was conducted across PubMed, Scopus, ScienceDirect, and Google Scholar to identify relevant studies. Forty-one studies reporting on the prevalence of colistin resistance in Enterobacteriaceae isolates from clinical specimens in Africa were included in the analysis. Stata 17 software was used to calculate the pooled prevalence of colistin resistance, employing a random-effects model to determine the event rate of resistance. Heterogeneity across studies was assessed using the I2 statistic, and publication bias was evaluated using Egger’s test. Subgroup analyses were performed to address any identified heterogeneity. Results This systematic review analyzed the colistin resistance profile of 9,636 Enterobacteriaceae isolates. The overall pooled prevalence of colistin resistance was 26.74% (95% CI: 16.68–36.80). Subgroup analysis by country revealed significant variability in resistance rates, ranging from 0.5% in Djibouti to 50.95% in South Africa. Species-specific prevalence of colistin resistance was as follows: K. pneumoniae 28.8% (95% CI: 16.64%-41.05%), E. coli 24.5% (95% CI: 11.68%-37.3%), Proteus spp. 50.0% (95% CI: 6.0%-106.03%), and Enterobacter spp. 1.22% (95% CI: -0.5%-3.03%). Analysis based on AST methods revealed significant differences in colistin resistance rates (p = 0.001). The resistance rates varied between 12.60% for the disk diffusion method and 28.0% for the broth microdilution method. Additionally, a subgroup analysis of clinical specimens showed significant variation (p < 0.001) in colistin resistance. Stool specimen isolates had the highest resistance rate at 42.0%, while blood specimen isolates had a much lower resistance rate of 3.58%. Conclusions Colistin resistance in Enterobacteriaceae is notably high in Africa, with significant variation across countries. This underscores the urgent need for effective antimicrobial stewardship, improved surveillance, and the development of new antibiotics.
Abstract Coxsackievirus A6 (CVA6) has emerged as a major pathogen causing hand, foot and mouth disease (HFMD) outbreaks worldwide. The CVA6 epidemic poses a new challenge in HFMD control since there is currently no vaccine available against CVA6 infections. The Vero cell line has been widely used in vaccine production, particularly in the preparation of viral vaccines, including poliovirus vaccines and EV71 vaccines. Unfortunately, most CVA6 strains failed to propagate effectively on Vero cells. The expression level of virus-specific receptors on the cell membrane significantly influences viral infection. Here, a Vero cell line with stable over-expressing of KREMEN1 (KRM1), a crucial receptor for CVA6, was constructed using the lentivirus system. The cloned cell line, called Vero-KRM1_#11, could efficiently support most CVA6 infections. The propagation of CVA6-TW00141 strain on Vero-KRM1_#11 was equal to that on RD cells. After four passages, the virus batch was obtained with a titer of about 107 TCID50/mL. Moreover, the purified CVA6 particles produced from Vero-KRM1_#11 or RD cells both could induce high and comparable levels of IgG and neutralizing antibodies. Importantly, passive transfer of the antisera from CVA6-vaccined mice showed 100% preventive efficacy against CVA6 infection in mice. Therefore, KRM1-expressing cells have the potential to serve as a valuable tool for the development and production of CVA6 or polyvalent HFMD vaccines.
Teodora Gladnishka, Iva Trifonova, Vladislava Ivanova
et al.
Background: Lymphocytic choriomeningitis virus (LHMV) infection is a neglected rodent-borne zoonotic infection but it is found all over the world because of the cosmopolitan distribution of its reservoirs. The diagnostic of this disease is not widely applied that is why it has been underreported. The aim of this study is to investigate infection with LCMV in hospitalized patients in 2015-2022 in Bulgaria and to analyse the data compared to the worldwide data available in this field of research. Materials/methods: A total of 66 serum samples and 25 cerebrospinal fluid (CSF) samples from 73 patients with suspected LCMV infection from different hospitals in Bulgaria were collected. All samples were tested with a commercial enzyme-linked immunosorbent assay (Human LCMV-Ab ELISA, SSBT, China), based on the principle of double-antibody sandwich technique to detect Human LCMV-Antibody. Results: A total of 11/91 (12.09%) positive samples were found in 5 males and 6 females throughout the study period. The positive samples were from patients from the cities: Sofia, Stara Zagora, Montana. A total of 3/25 (12%) positive samples were from CSF samples and 8/66 positive samples (12.12%) were from serum samples. Conclusions: It’s found that this infection occurs in our country and should not be underestimated, due to the possible severe neurological course and the danger of fetal damage in pregnant women. The diagnosis of LCMV infection is based on previous experience, placed in the light of the continuous introduction of new more sensitive and specific approaches.
Continuous-time multistate models are widely used for analyzing interval-censored data on disease progression over time. Sometimes, diseases manifest differently and what appears to be a coherent collection of symptoms is the expression of multiple distinct disease subtypes. To address this complexity, we propose a mixture hidden Markov model, where the observation process encompasses states representing common symptomatic stages across these diseases, and each underlying process corresponds to a distinct disease subtype. Our method models both the overall and the type-specific disease incidence/prevalence accounting for sampling conditions and exactly observed death times. Additionally, it can utilize partially available disease-type information, which offers insights into the pathway through specific hidden states in the disease process, to aid in the estimation. We present both a frequentist and a Bayesian way to obtain the estimates. The finite sample performance is evaluated through simulation studies. We demonstrate our method using the Nun Study and model the development and progression of dementia, encompassing both Alzheimer's disease (AD) and non-AD dementia.
The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, especially for rare diseases. Here, we introduce EyeDiff, a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts and evaluate its applicability in diagnosing common and rare diseases. EyeDiff is trained on eight large-scale datasets using the advanced latent diffusion model, covering 14 ophthalmic image modalities and over 80 ocular diseases, and is adapted to ten multi-country external datasets. The generated images accurately capture essential lesional characteristics, achieving high alignment with text prompts as evaluated by objective metrics and human experts. Furthermore, integrating generated images significantly enhances the accuracy of detecting minority classes and rare eye diseases, surpassing traditional oversampling methods in addressing data imbalance. EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field.
Sudam Surasinghe, Swathi Nachiar Manivannan, Samuel V. Scarpino
et al.
Mathematical modeling has played a central role in understanding how infectious disease transmission manifests in populations. These models have demonstrated the importance of key community-level factors in structuring epidemic risk, and are now routinely used in public health for decision support. One barrier to their broader utility is that the existing canon does not often accommodate social inequalities as distinct formal drivers of variability in transmission dynamics. Given decades of evidence supporting the organizational effects of inequalities in structuring society more generally, and infectious disease risk more specifically, addressing this modeling gap is of critical importance. In this study, we build on previous efforts to integrate social forces into computational epidemiology by introducing a metric, the structural causal influence (SCI). The SCI uses causal analysis to provide a measure of the relative vulnerability of sub-communities within a susceptible population, shaped by differences in characteristics such as access to therapy, exposure to disease, and other determinants driven by social forces. We develop our metric in a simple case and apply it to a context of public health importance: Hepatitis C virus in a population of persons who inject drugs. In addition, we demonstrate the flexibility of the SCI using an agent-based model of an infectious disease. Our use of the SCI reveals that, under specific parameters in a multi-community model, the "less vulnerable" community may achieve a basic reproduction number below one, ensuring disease extinction. However, even minimal transmission between communities can increase this number, leading to sustained epidemics within both communities.
Over the past 50 years, the advancements in medical and health research have radically changed the epidemiology of health conditions in neonates, children, and adolescents; and clinical research has on the whole, moved forward. However, large sections of the pediatric community remain vulnerable and underserved, by clinical research. One reason for this is the fact that most pediatric diseases are also rare diseases (i.e., they fit the EU definition of a rare condition, by affecting no more than 5 in 10,000 individuals), and indeed the majority of conditions under this umbrella heading are in fact much rarer, affecting fewer than 1 in 100,000. Rare pediatric diseases incur particular challenges, both in terms of actually conducting clinical trials but also planning trials (and indeed, stimulating the preclinical research and knowledge generation necessary to embark on clinical trials in the first place). The pediatric regulation and orphan regulation (covering rare diseases) were introduced to address the complexities in research and development of medicines specifically for children and for people living with a rare disease, respectively. The regulations have been reasonably effective, particularly in areas where adult and pediatric diseases overlap, driving the development of more pediatric medicines; however, challenges still remain, often exacerbated by the rarity of the diseases. These include issues around trial planning, the need for more innovative methodologies in smaller populations, significant delays in trial start up and recruitment, recruitment issues (due to small populations and the nature of the conditions), lack of endpoints, and scarce data. This chapter will discuss some of the major challenges in delivering trials in pediatric rare diseases while also assessing current and future solutions to address these.
Abstract Background The aim of this study was to investigate the epidemiological characteristics and antibiotic resistance patterns of Ureaplasma urealyticum (UU) infection among women and children in southwest China. Methods A total of 8,934 specimens, including urogenital swabs and throat swabs were analyzed in this study. All samples were tested using RNA-based Simultaneous Amplification and Testing (SAT) methods. Culture and drug susceptibility tests were performed on UU positive patients. Results Among the 8,934 patients, the overall positive rate for UU was 47.92%, with a higher prevalence observed among women of reproductive age and neonates. The majority of UU positive outpatients were women of reproductive age (88.03%), while the majority of UU positive inpatients were neonates (93.99%). Overall, hospitalization rates due to UU infection were significantly higher in neonates than in women. Further analysis among neonatal inpatients revealed a higher incidence of preterm birth and low birth weight in UU positive inpatients (52.75% and 3.65%, respectively) than in UU negative inpatients (44.64% and 2.89%, respectively), especially in very preterm and extremely preterm neonates. Moreover, the incidence rate of bronchopulmonary dysplasia (BPD) among hospitalized neonatal patients was significantly higher in the UU positive group (6.89%) than in the UU negative group (4.18%). The drug susceptibility tests of UU in the neonatology, gynecology and obstetrics departments exhibited consistent sensitivity patterns to antibiotics, with high sensitivity to tetracyclines and macrolides, and low sensitivity to fluoroquinolones. Notably, UU samples collected from the neonatology department exhibited significantly higher sensitivity to azithromycin and erythromycin (93.8% and 92.9%, respectively) than those collected from the gynecology and obstetrics departments. Conclusions This study enhances our understanding of the current epidemiological characteristics and antibiotic resistance patterns of UU infection among women and children in southwest China. These findings can aid in the development of more effective intervention, prevention and treatment strategies for UU infection.
This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases.
In the world of epidemics, the mathematical modeling of disease co-infection is gaining importance due to its contributions to mathematics and public health. Because the co-infection may have a double burden on families, countries, and the universe, understanding its dynamics is paramount. We study a SEIQR (susceptible-exposed-infectious-quarantined-recovered) deterministic epidemic model with a single host population and multiple strains (-$c$ and -$i$) to account for two competitive diseases with quarantine effects. To model the role of quarantine and isolation efficacy in disease dynamics, we utilize a linear function. Further, we shed light on the standard endemic threshold and determine the conditions for extinction or coexistence with and without forming co-infection. Next, we show the dependence of the criticality based on specific parameters of the different pathogens. We found that the disease-free equilibrium (DFE) of the single-strain model always exists and is globally asymptotically stable (GAS) if $\tilde{\mathcal{R}}_k^q\leq 1$, else, a stable endemic equilibrium. On top of that, the model has forward bifurcation at $\tilde{\mathcal{R}}_k^q = 1$. In the case of a two-strain model, the strain with a large reproduction number outcompetes the one with a smaller reproduction number. Further, if the co-infected quarantine reproduction number is less than one, the infections of already infected individuals will die out, and co-infection will persist in the population otherwise. We note that the quarantine and isolation of exposed and infected individuals will reduce the number of secondary cases below one, consequently reducing the disease complications if the total number of people in the quarantine is at most the critical value.
Ahmet Hakan Ünlü, Rahmi Yıldız, Selahattin Aydemir
et al.
Objective: Lymnaea stagnalis known as the great pond snail, is one of the intermediate hosts of Fasciola hepatica, a zoonotic parasite. In this study, it was aimed to determine the larval forms of F. hepatica by polymerase chain reaction (PCR) in L. stagnalis species snails collected from the vicinity of Ağrı province.
Methods: In this study, 150 L. stagnalis snails were collected from the Ağrı province. The freshwater snails brought to the laboratory were dissected, then their soft tissues were examined under a microscope. DNA extraction was performed on the dissected snails. After DNA extraction, PCR was performed using primers targeting the cytochrome c oxidase subunit 1 gene region.
Results: In the microscopic examination, larval forms of F. hepatica could not be detected. However, it was concluded that two (1.3%) L. stagnalis freshwater snails were infected with the larval forms of F. hepatica in the PCR.
Conclusion: It was determined that L. stagnalis served as an intermediate host to F. hepatica in the study area.
Kasey Hickman, Nicolas Forcade, Mandelin Cooper
et al.
Background: In 2016, the CDC released the Core Elements of Outpatient Antibiotic Stewardship, which extended the requirements previously released for hospital facilities and nursing homes to the outpatient setting. Several regulatory agencies focused on outpatient antimicrobial use. However, The Joint Commission and the Ambulatory Surgery Center (ASC) Leapfrog Group excluded ambulatory surgery centers from their medication management standards and questions. Due to the public health and patient safety benefits of implementing an antimicrobial stewardship program (ASP) and increasing regulatory interest in the matter, the Hospital Corporation of America (HCA) Ambulatory Surgery Division formally launched a nationwide ASP for its ambulatory surgery centers in March 2021. Methods: HCA is a large healthcare system with 146 ASCs in 16 states in 2021. The structure of the ASCs are local surgery centers with a medical director, a nurse responsible for infection prevention, and a pharmacist at a regional level. The types of surgeries vary based on location and ASC site. In 2019, a multidisciplinary team formed the corporate planning committee. The program was modeled after the CDC Core Elements and The Joint Commission’s requirements for an ASP. Each ASC was asked to build a local ASP team, led by a local physician and a regionally based pharmacist. In addition, a stewardship goal was established to update all preoperative antibiotic surgical-site infection prophylaxis order sets. The corporate committee provided educational resources, including evidence-based guidelines for appropriate antibiotic selection for surgical-site infections. They collected antibiotic cost per case as a baseline metric to track and analyze. Pediatric, ophthalmic, and gastrointestinal endoscopic procedures were excluded from the program. Results: From January 1, 2020, through December 31, 2021, including only centers that were operational during this period and excluding single specialty endoscopy centers, antibiotic cost per case decreased annually from $2.38 to $1.84 (t = 4.157; P < .005), and the postoperative infection rate also declined from 0.370 to 0.304 (t = 2.079; P = .040). Conclusions: Our findings suggest that implementing a health-system–wide outpatient antibiotic stewardship program in the ambulatory surgery center setting is feasible and may contribute to decreased antibiotic cost per case and improved postoperative surgical site infection rates.
Infectious and parasitic diseases, Public aspects of medicine
Abstract Background Schistosomiasis is a serious but neglected parasitic disease in humans that may lead to liver fibrosis and death. Activated hepatic stellate cells (HSCs) are the principal effectors that promote the accumulation of extracellular matrix (ECM) proteins during hepatic fibrosis. Aberrant microRNA-29 expression is involved in the development of fibrotic diseases. However, less is known about the role of miR-29 in Schistosoma japonicum (S. japonicum)-induced hepatic fibrosis. Methods The levels of microRNA-29a-3p (miR-29a-3p) and Roundabout homolog 1 (Robo1) were examined in liver tissues during S. japonicum infection. The possible involvement of the miR-29a-3p-Robo1 signaling pathway was determined. We used MIR29A conditional knock-in mice and mice injected with an miR-29a-3p agomir to investigate the role of miR-29a-3p in schistosomiasis-induced hepatic fibrosis. The functional contributions of miR-29a-3p-Robo1 signaling in liver fibrosis and HSC activation were investigated using primary mouse HSCs and the human HSC cell line LX-2. Results MiR-29a-3p was downregulated in humans and mice with schistosome-induced fibrosis, and Robo1 was upregulated in liver tissues. The miR-29a-3p targeted Robo1 and negatively regulated its expression. Additionally, the expression level of miR-29a-3p in schistosomiasis patients was highly correlated with the portal vein and spleen thickness diameter, which represent the severity of fibrosis. Furthermore, we demonstrated that efficient and sustained elevation of miR-29a-3p reversed schistosome-induced hepatic fibrosis. Notably, we showed that miR-29a-3p targeted Robo1 in HSCs to prevent the activation of HSCs during infection. Conclusions Our results provide experimental and clinical evidence that the miR-29a-3p-Robo1 signaling pathway in HSCs plays an important role in the development of hepatic fibrosis. Therefore, our study highlights the potential of miR-29a-3p as a therapeutic intervention for schistosomiasis and other fibrotic diseases. Graphical Abstract
Tomotaka Miura, Hirotsugu Fukuda, Hiroshi Kawada
et al.
Abstract Background Given the widespread prevalence of the coronavirus disease 2019 (COVID-19), oral and neck examinations tend to be avoided in patients with suspected or confirmed COVID-19. This might delay the diagnosis of conditions such as Lemierre’s syndrome, which involves symptoms resembling COVID-19-related throat manifestations. Case presentation A 24-year-old man without any underlying conditions was diagnosed with COVID-19 7 days before presentation. He was admitted to another hospital 1 day before presentation with severe COVID-19 and suspected bacterial pneumonia; accordingly, he was started on treatment with remdesivir and meropenem. Owing to bacteremic complications, the patient was transferred to our hospital for intensive care. On the sixth day, the patient experienced hemoptysis; further, a computed tomography (CT) scan revealed new pulmonary artery pseudoaneurysms. Successful embolization was performed to achieve hemostasis. In blood cultures conducted at the previous hospital, Fusobacterium nucleatum was isolated, suggesting a cervical origin of the infection. A neck CT scan confirmed a peritonsillar abscess and left internal jugular vein thrombus; accordingly, he was diagnosed with Lemierre’s syndrome. The treatment was switched to ampicillin/sulbactam, based on the drug susceptibility results. After 6 weeks of treatment, the patient completely recovered without complications. Conclusion This case highlights the significance of thorough oral and neck examinations in patients with suspected or diagnosed COVID-19 for the detection of throat and neck symptoms caused by other conditions.
Vladimir Milanov, Nikolay Yanev, Natalia Gabrovska
et al.
Isoniazid (H; INH) is an important first-line drug for the treatment of active tuberculosis (TB) and latent TB infection because of its potent early bactericidal activity against Мycobacterium tuberculosis. Currently, TB resistant to INH, alone or in combination with other drugs, is the most common type of drug-resistant TB. Epidemiology of INH-resistant TB, the molecular mechanisms of drug resistance, current methods for diagnosis and therapeutic regimens of this TB form are presented. Studies in the last years have shown that resistance to INH reduces the probability of treatment success and increases the risk of acquiring resistance to other important first-line drugs. Based on the most recent meta-analyses, the last WHO recommendations for treatment of INH-resistant TB are to include rifampicin (RIF), ethambutol, pyrazinamide and levofloxacin for 6 months, and not to add streptomycin or other injectable agents to the drug regimen. The guideline emphasizes the importance of excluding resistance to RIF before starting the regimen for INH-resistant TB because of the risk for development of multidrug-resistant TB during the treatment course. The WHO recommendations are based on observational studies, not randomized controlled trials, and are thus conditional and based on low certainty in the estimates of effect. Therefore, further work is needed to optimize the treatment and control of INH-resistant TB.
Vaccination is an effective way to prevent and control the occurrence and epidemic of infectious diseases. However, many factors influence whether the residents decide to get vaccinated or not, such as the efficacy and side effects while individuals hope to obtain immunity through vaccination. In this paper, the public attitude toward vaccination is investigated, especially how it is influenced by the public estimation of vaccines efficacy and reliance on their neighbors' vaccination behavior. We find that improving people's trust in the vaccination greatly benefits increasing the vaccination rate and accelerating the vaccination process. Counterintuitively, if the individual's attitude towards vaccination is more reliant on his neighbors' vaccination behavior, more individuals will get vaccinated, and the vaccination process will speed up. Besides, individuals are more willing to get vaccinated if they have more neighbors.