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Arthropod-borne arboviral diseases—including dengue, Zika, and chikungunya—place a substantial burden on the health of populations, globally. Dengue alone is endemic in more than 100 countries and causes more than 96 million symptomatic cases and approximately 40,000 deaths annually. The recent surge in arboviral disease outbreaks, coupled with the World Health Organization’s newly published vector control guidelines, accentuates the imperative to understand the dispersion of disease-carrying mosquitoes across diverse spatial and temporal scales. However, traditional surveillance mechanisms often fall short because of workforce limitations, logistical complexities, jurisdictional boundaries, and budgetary constraints, especially in low- and low-middle-income countries. In this article, we systematically report the design, implementation, and iterative enhancement of a groundbreaking school-based citizen science initiative for augmenting mosquito surveillance in the Solomon Islands. Key reflections encompass the initiative’s role in supporting routine government-led disease vector monitoring, sustainability through integration and fostering participant engagement, and the amalgamation of citizen-collected data with government surveillance activities. The article also discusses the impact of the citizen science initiative with regard to the Solomon Islands’ pursuit of the Sustainable Development Goals. Our findings underscore the potential of citizen science methods to support and extend public health surveillance activities and to serve as a community-engagement-for-behaviour-change tool in resource-constrained contexts.

Africa's dual burden of rising incidence of infectious diseases and increasing prevalence of non-communicable diseases (NCDs), such as cardiovascular diseases and diabetes, demands innovative approaches to disease surveillance, response, and cross-border health management in response to growing economic integration and global connectivity. In this context, we propose a discursive framework for the development and implementation of a multi-disease digital health passport (MDDHP) in Africa. The MDDHP would serve as a secure platform for storing and sharing individual health data, offering a comprehensive solution to track and respond to infectious diseases, facilitate the management of NCDs, and improve healthcare access across borders. Empowering individuals to proactively manage their health and improve overall outcomes is a key aspect of the MDDHP. In the paper, we examine the key elements necessary to effectively implement MDDHP, focusing on minimizing risks, maintaining efficacy, and driving its adoption while also taking into consideration the unique contexts of the continent. The paper is intended to provide an understanding of the key principles involved and contribute to the discussion on the development and successful implementation of MDDHP in Africa. [ABSTRACT FROM AUTHOR]

This paper studies continuing optimal lockdowns (can also be interpreted as quarantines or self-isolation) in the long run if a disease (Covid-19) is endemic and immunity can fail, that is, the disease has SIRS dynamics. We model how disease related mortality affects the optimal choices in a dynamic general equilibrium neoclassical growth framework. An extended welfare function that incorporates loss from mortality is used. In a disease endemic steady state, without this welfare loss even if there is continuing mortality, it is not optimal to impose even a partial lockdown. We characterize how the optimal restriction and equilibrium outcomes vary with the effectiveness of the lockdown, the productivity of working from home, the rate of mortality from the disease, and failure of immunity. We provide the sufficiency conditions for economic models with SIRS dynamics with disease related mortality–a class of models which are non-convex and have endogenous discounting so that no existing results are applicable. [ABSTRACT FROM AUTHOR]

Today, because infectious diseases have a large impact on people, control methods for all infectious diseases are crucial. In this paper, we propose a SIQR model with Crowley-Martin type incidence rate and saturated treatment. We have incorporated precautionary measures taken by the population through the saturated incidence rate. Screening of individuals who have come into contact with infected individuals is modelled. Thus, we explore a four-dimensional mathematical model to study the dynamics of the infectious diseases and their management. Stability and bifurcation analysis ofthe system is done. The Mensitivity analysis of all parameters in relation to the basic reproduction number has been performed and the theoretical results have been validated using numerical simulation. Mathematical results in this paper suggest that both government interventions and individual efforts must be incorporated to achieve better control of emerging infectious diseases in the population. [ABSTRACT FROM AUTHOR]

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays. [ABSTRACT FROM AUTHOR]

This paper uses an augmented gravity model framework to investigate the historical impact of infectious diseases on international tourism and develops an out-of-sample prediction model. Using bilateral tourism flows among 38,184 pairs of countries during the period 1995–2017, I compare the forecasting performance of alternative specifications and estimation methods. These computations confirm the statistical and economic significance of infectious-disease episodes in forecasting international tourism flows. Including infectious diseases in the model brings a significant improvement in forecast accuracy relative to the standard gravity model. The magnitude of these effects, however, is likely to be much greater in the case of COVID-19, which is a highly contagious virus that has spread fast throughout populations across the world. [ABSTRACT FROM AUTHOR]

Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and timeliness of infectious diseases, specific neural networks are used to extract the geography and timeliness of infectious diseases. In the model framework, the features of different periods are extracted by a multi-scale convolution module. At the same time, the propagation effects between regions are simulated by graph convolution and attention mechanisms. We compare the proposed method with the most advanced statistical methods and deep learning models. Meanwhile, we conduct comparative experiments on data sets with different time lengths to observe the prediction performance of the model in the face of different degrees of data collection. We conduct extensive experiments on real-world epidemic-related data sets. The method has strong prediction performance and can be readily used for epidemic prediction. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the ethical foundation for resolving ethical issues arising from the biomedical big data analytics on emerging infectious diseases. To this end, this paper first compares the moral issues arising from the analysis of healthcare big data for emerging infectious diseases with the existing problem areas of biomedical ethics. We will discuss how existing bioethical debates, especially those dealing with the issue of individual autonomy, are inadequate to address the ethical issues of biomedical big data analytics for emerging infectious diseases, even though they have some relevance. Based on these discussions, I will present one approach to addressing the ethical issues of biomedical big data analytics for emerging infectious diseases, centered on L. Floridi’s notion of agents. This will provide an opportunity to expand the existing discussion of bioethics to include information ethics. [ABSTRACT FROM AUTHOR]

Background: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. Results: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. Conclusions: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases. [ABSTRACT FROM AUTHOR]

Since the early days of the coronavirus (COVID-19) outbreak in Wuhan, China, Saudi Arabia started to implement several preventative measures starting with the imposition of travel restrictions to and from China. Due to the rapid spread of COVID-19, and with the first confirmed case in Saudi Arabia in March 2019, more strict measures, such as international travel restriction, and suspension or cancellation of major events, social gatherings, prayers at mosques, and sports competitions, were employed. These non-pharmaceutical interventions aim to reduce the extent of the epidemic due to the implications of international travel and mass gatherings on the increase in the number of new cases locally and globally. Since this ongoing outbreak is the first of its kind in the modern world, the impact of suspending mass gatherings on the outbreak is unknown and difficult to measure. We use a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses shown in the paper provide insights into the effects of global mass gatherings such as Hajj and Umrah on the progression of the COVID-19 pandemic locally and globally. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance poses a significant challenge to public health globally, leading to increased morbidity and mortality. AMR surveillance involves the systematic collection, analysis, and interpretation of data on the occurrence and distribution of AMR in humans, animals, and the environment for action. The West African Health Organization, part of the Economic Community of West African States (ECOWAS), is committed to addressing AMR in the region. This paper examines the status of AMR surveillance in ECOWAS countries using available WHO data from the TrACSS survey and GLASS enrollments. The analysis reveals that while progress has been made, significant challenges remain. Twelve of the fifteen ECOWAS countries are enrolled in GLASS, and ten have developed national action plans (NAPs) for AMR. However, there is a need to ensure all countries fully implement their NAPs, continue reporting to GLASS, and use the data for evidence-based actions and decision making. Surveillance systems for AMR and antimicrobial consumption/use vary across countries with some demonstrating limited capacity. All countries, except Cabo Verde, reported having a reference laboratory for AMR testing. Strengthening laboratory capabilities, data management and use, and multisectoral coordination are crucial for effective AMR surveillance and response. Based on the findings and the regional context, it is essential to prioritize capacity building, data utilization, and the adoption of standardized guidelines for AMR surveillance. Collaboration among ECOWAS countries, the WAHO, and international partners is essential to address AMR comprehensively. Ensuring a consistent supply of essential antimicrobial medications and reagents is vital. [ABSTRACT FROM AUTHOR]

Copyright of Revista Latinoamericana de Hipertension is the property of Revista Latinoamericana de Hipertension and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper study was designed to establish solutions for mixed functional fractional integral equations that involve the Riemann-Liouville fractional operator and the Erd'elyi-Kober fractional operator to describe biological population dynamics in Banach space. The results rely on the measure of non-compactness and theoretical concepts from fractional calculus. Darbo's fixed-point theorem for Banach spaces has been utilized. Moreover, the solvability of a specific non-linear integral equation that models the spread of infectious diseases with a seasonally varying periodic contraction rate has been explored by using the Banach contraction principle. Finally, two numerical examples demonstrate the practical application of these findings in the realm of fractional integral equation theory. [ABSTRACT FROM AUTHOR]

Infectious diseases seriously threaten the lives of children. Timely and accurate detection of pathogenic microorganisms and targeted medication are the keys to the diagnosing and treatment of infectious diseases in children. The next-generation metagenomic sequencing technology has attracted great attention in infectious diseases because of its characteristics such as no culture, high throughput, short detection cycle, wide coverage, and a good application prospect. In this paper, we review the studies of metagenomic next-generation sequencing in pediatric infectious diseases and analyze the challenges of its application in pediatric diseases. [ABSTRACT FROM AUTHOR]

Spreading quickly throughout populations, whether animal or human-borne, infectious illnesses provide serious risks and difficulties. Controlling their spread and averting disinformation requires effective risk assessment and epidemic identification. Technology-enabled data analysis on diseases allows for quick solutions to these problems. A Combinational Data Assessment Scheme intended to accelerate disease detection is presented in this paper. The suggested strategy avoids duplicate data replication by sharing data among edge devices. It uses indexed data gathering to improve early detection by using tree classifiers to discern between various kinds of information. Both data similarity and index measurements are considered throughout the data analysis stage to minimize assessment errors. Accurate risk detection and assessment based on information kind and sharing frequency are ensured by comparing non-linear accumulations with accurate shared edge data. The suggested system exhibits high accuracy, low mistakes, and decreased data repetition to improve overall effectiveness in illness detection and risk reduction. [ABSTRACT FROM AUTHOR]

Background Myocarditis is challenging to diagnose due to its varied presentations. Endomyocardial biopsy is the gold standard for diagnosis, but its invasive nature has led to alternative non-invasive modalities, notably cardiovascular magnetic resonance (CMR). Identifying the precise aetiology of myocarditis is crucial for effective treatment, yet extra-cardiac causes are often overlooked. In this paper, we spotlight the underexplored role of CMR in diagnosing extra-cardiac aetiologies, utilizing three insightful cases for illustration. Case summary The first case is a 31-year-old patient with myocarditis secondary to a pyogenic liver abscess, identified through CMR, who improved after abscess drainage. The second case involves a 54-year-old patient with myocarditis attributed to adult T-cell leukaemia–lymphoma, with the loco-regional thickening process identified thanks to CMR. This patient had an unfavourable disease progression due to the underlying malignancy. The third case concerns a 23-year-old patient suffering from myocarditis associated with pneumonia, again illustrated effectively through CMR imaging, who recovered after antibiotic treatment. Discussion These cases underline the overlooked potential of CMR in diagnosing extra-cardiac aetiologies of myocarditis, even though such causes are rare. Despite current guidelines recognizing the importance of identifying the aetiology of myocarditis, they do not explicitly address the role of CMR in diagnosing extra-cardiac aetiologies. This article, therefore, proposes that future guidelines could emphasize the utility of CMR in exploring these causes, potentially leading to more accurate diagnoses and improved patient outcomes. It also advocates for a comprehensive, multidisciplinary approach to myocarditis diagnosis, encouraging vigilance for potential loco-regional causes, and calls for further research in this area. [ABSTRACT FROM AUTHOR]

The rapid development of vaccines is a crucial objective in modern biotechnology and molecular pharmacology. In this context, conducting research to expedite the selection of a potent immunogen is imperative. The candidate vaccine should induce the production of antibodies that can recognize the immunogenic epitopes of the target protein, resembling the ones found in recovered patients. One major challenge in vaccine development is the absence of straightforward and reliable techniques to determine the extent to which the spectrum of antibodies produced after vaccination corresponds to antibodies found after recovery. This paper describes a newly developed method to detect antibodies specific to immunogenic epitopes of the target protein in blood plasma and to compare them with antibody spectra generated post vaccination. Comparing the antibody pool generated in the human body after recovering from an infectious disease with the pool formed through vaccination can become a universal method for screening candidate vaccines. This method will enable the identification of candidate vaccines that can induce the production of antibodies similar to those generated in response to a natural infection. Implementing this approach will facilitate the rapid development of new vaccines, even when faced with a pandemic. [ABSTRACT FROM AUTHOR]

Engagement is an important component in the advancement of gene-drive vector control research programs as developers look to transition the technology from the laboratory to the field. As research advances and engagement surrounding this novel technology is put into practice, knowledge can be gained from practical experiences and applications in the field. A relationship-based model (RBM) provides a framework for end-user development of engagement programs and strategies. The model places end users at the center of the engagement decision-making processes rather than as recipients of predetermined strategies, methods, and definitions. Successful RBM application for healthcare delivery has previously been demonstrated, and the University of California Malaria Initiative (UCMI) has applied this model to its gene-drive program in the Democratic Republic of São Tomé and Príncipe. The model emphasizes the importance of local leadership in the planning, development, and implementation of all phases of project engagement. The primary aim of this paper is to translate the model from paper to practice and provide a transparent description, using practical examples, of the UCMI program implementation of RBM at its field site. End-user development of the UCMI engagement program provides a unique approach to the development of ethical, transparent, and effective engagement strategies for malaria control programs. This paper may also serve as a reference and example for projects looking to establish an engagement program model that integrates end-user groups in the decision-making processes surrounding engagement.

One of the major bottlenecks affecting the expansion of the freshwater aquaculture sector in developing countries is the outbreak of diseases. Fish farmers have traditionally relied on antibiotics and synthetic chemicals to control diseases. However, antibiotics and synthetic chemicals are associated with negative effects on the environment and consumers and their excessive use has resulted in antibiotic-resistant pathogens. In addition, the majority of freshwater fish farmers, especially small-scale farmers, have limited access to antibiotics and synthetic chemicals due to lack of resources. Medicinal plants have been reported to be suitable replacements in aquaculture, but their usage in aquaculture is still limited. The aim of this paper is to undertake a meta-analysis on the effect of medicinal plants on innate immune response and disease resistance in commonly farmed freshwater aquaculture fish species, namely Mozambique tilapia (Oreochromis mossambicus), African catfish (Clarias gariepinus), trout (Oncorhynchus mykiss), and cyprinids (Labeo rohita and Cyprinus carpio). The analysis showed that the mean effect size for respiratory burst (− 1.90 (95% CI − 2.40; − 1.40), I2 = 100%, P = 0); lysozyme activity (0.05 (95% CI − 0.38; 0.48), I2 = 97%, P < 0.01); white blood cells (− 0.69 (95% CI − 1.16; − 0.23), I2 = 89%, P < 0.01); and phagocytic activity (− 1.21 (95% CI − 2.08; − 0.35), I2 = 91%, P < 0.01) was significantly different. Mean effect size for survival rates was not significantly different (2.56 (95% CI 0.10; 5.01), I2 = 0%, P = 1). The funnel plots for all parameters were asymmetrical, which indicates possible publication bias or the presence of systematic differences or inconsistencies among studies. Overall, the meta-analysis showed that medicinal plants could enhance immunity and disease resistance in tilapia, African catfish, carp, and trout. These findings reinforce the observation that plant supplements can be used to prevent disease outbreaks in aquaculture. It is therefore recommended that freshwater fish farmers in African regions such as Southern Africa learn to produce their own diets supplemented with plants at their farms in order to prevent diseases in cultured fish. [ABSTRACT FROM AUTHOR]

In 2023, Katalin Karikó and Drew Weissman were awarded the Nobel Prize in Physiology or Medicine for their nucleoside base modifications research that later enabled mRNA vaccine development against COVID-19. This paper briefly reviews these achievements in the context of the development of mRNA technology and its enormous potential for medicine in the prevention of various infectious diseases and cancer treatment, including personalised therapies. It is beyond any doubt that discoveries made by Karikó and Weissman were pivotal in overcoming one of the major hurdles in the practical application of mRNA molecules, i.e., the recognition of exogenous mRNAs by endosomal Toll-like receptors and downstream innate immune response, ultimately leading to the decreased translational activity of delivered mRNA and its degradation. Although the Nobel Prize for Karikó and Weissman is fully justified, it must be stressed that mRNA technology would never unfold its potential for public health without a collective scientific effort encompassing over 40 years of research. [ABSTRACT FROM AUTHOR]

Copyright of China Tropical Medicine is the property of China Tropical Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018–2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research. [ABSTRACT FROM AUTHOR]

• The Orchard Sports Injury and Illness Classification System (OSIICS) is 1 of 2 sports injury classification systems recognized by the International Olympic Committee and needs regular update. • It was previously focused on injuries rather than common illness/medical condition presentations in athletes, so the most recent updates have extended medical presentation diagnoses. • Version 15 includes expansion of available diagnoses in the fields of mental health, sports concussion, sports cardiology, sports dermatology, sports ophthalmology, and infectious diseases. • The OSIICS has been previously translated into Italian, Spanish, and Catalan, and further translation into multiple other languages is planned. Sports medicine (injury and illnesses) requires distinct coding systems because the International Classification of Diseases is insufficient for sports medicine coding. The Orchard Sports Injury and Illness Classification System (OSIICS) is one of two sports medicine coding systems recommended by the International Olympic Committee. Regular updates of coding systems are required. For Version 15, updates for mental health conditions in athletes, sports cardiology, concussion sub-types, infectious diseases, and skin and eye conditions were considered particularly important. Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes. Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes. Rugby union protocols on head injury assessment were used to create additional concussion codes. It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility. The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Africa’s dual burden of rising incidence of infectious diseases and increasing prevalence of non-communicable diseases (NCDs), such as cardiovascular diseases and diabetes, demands innovative approaches to disease surveillance, response, and cross-border health management in response to growing economic integration and global connectivity. In this context, we propose a discursive framework for the development and implementation of a multi-disease digital health passport (MDDHP) in Africa. The MDDHP would serve as a secure platform for storing and sharing individual health data, offering a comprehensive solution to track and respond to infectious diseases, facilitate the management of NCDs, and improve healthcare access across borders. Empowering individuals to proactively manage their health and improve overall outcomes is a key aspect of the MDDHP. In the paper, we examine the key elements necessary to effectively implement MDDHP, focusing on minimizing risks, maintaining efficacy, and driving its adoption while also taking into consideration the unique contexts of the continent. The paper is intended to provide an understanding of the key principles involved and contribute to the discussion on the development and successful implementation of MDDHP in Africa.

Climate change can affect the frequency and intensity of infectious diseases worldwide, which may further aggravate disparities in the health and disease burden. Apart from the known risk factors, the concurrent COVID-19 pandemic exposed a range of social vulnerabilities that influenced its local impacts and responses. Even though global policies consider various local vulnerabilities, the literature notes an increasing need to address their intertwined spatial implications. This paper offers a review of studies focusing on infectious diseases to understand regional variations in vulnerability and policy implications. By using the scoping review method it compares the trends of regional vulnerability in literature with the ones observed during COVID-19 along with the recent regional reports addressing issues of climate change and health for their policy implications. It finds that due to the overarching nature of global policies, various aspects of regional vulnerability remain unaddressed, and many important regional observations do not find their way to inform global policies. The paper thus argues that considering variations in regional vulnerability in global policies and developing regional guidelines could contribute to an integrated response and effective management of global hazards concerning climate change and health.

On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives. [ABSTRACT FROM AUTHOR]

Mpox is a viral disease caused by the monkey pox virus (MPXV) of the Poxviridae family. It is similar to smallpox and cowpox. Mpox is not categorized as a sexually transmitted infection, however, most transmission in the recent global outbreak has been attributed to be through men who have sex with men (MSM). Agent-based modelling (ABM) is an effective approach to simulate the spread of Mpox because it enables the detailed representation of heterogeneity among individuals or sub-populations, including variations in behaviour, susceptibility to disease, and contact patterns. This is crucial for accurately modelling diseases in populations with long tailed distributions of characteristics such as sexual activity, that has been seen in the recent Mpox outbreak. Two ABMs are presented in this paper which simulate the spread of Mpox in an artificial population. The parameters for contact formation and dissolution are drawn from the NATSAL-3 dataset (National Survey of Sexual Attitudes and Lifestyles) (Mercer et al. 2013). The presented ABMs are based on the principles of the classic compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model and complexity is further added to break the assumptions of homogeneous mixing and populations. The first ABM is based on an heterogeneous population, consisting of people from two self-identified genders (male and female) and three sexual preferences: homosexual, bi-sexual and heterosexual. The second ABM is a subset of the first ABM, that focuses on the MSM population, which is considered to be at high-risk of Mpox. We report a set of experiments modelling relevant scenarios by number of imported cases, number of super-spreader events, and different vaccination strategies. Our analysis concludes that Mpox infection reaches locally stable disease-free equilibrium in the absence of concurrent partnerships. Our simulations demonstrate that prioritizing vaccines for the MSM population can significantly reduce the effective R value from 1.62 to 0.19, highlighting the potential impact of targeted vaccination strategies in mitigating Mpox transmission. [ABSTRACT FROM AUTHOR]

In this opinion paper, the author reflects on a transformative experience attending an antimicrobial stewardship workshop that shifted their perception of the looming crisis of antimicrobial resistance. The author emphasises the critical need for greater awareness and education at all levels of society, from healthcare professionals to the general population, to truly comprehend the severity of this issue. Drawing parallels between antimicrobial resistance and more recognised threats such as cancer, the author advocates for a multidisciplinary approach and heightened emphasis on education to foster responsible antimicrobial use. They propose practical strategies, including integrating stewardship principles into medical education and advocating for preventive measures such as vaccination. Ultimately, this paper urges individuals to embrace their roles as stewards of antimicrobials and to seize the opportunity to effect change for the betterment of humanity. Contribution: This opinion paper emphasises the urgent need for greater awareness and education on antimicrobial stewardship. It underscores the importance of multidisciplinary collaboration and proposes practical strategies, particularly in healthcare education. These insights align with the journal’s scope of advancing healthcare education, policy, and addressing global health challenges regarding infectious diseases.

The global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases.

In spatial infectious disease models, it is typical to assume that only the distance between susceptible and infectious individuals is important for modelling, but not the actual spatial locations of the individuals. Recently introduced geographically-dependent individual level models (GD-ILMs) can be used to also consider the effect of spatial locations of individuals and the distance between susceptible and infectious individuals for determining the risk of infection. In these models, it is assumed that the covariates used to predict the occurrence of disease are measured accurately. However, there are many applications in which covariates are prone to measurement error. For instance, to study risk factors for influenza, people with low socio-economic status (SES) are known to be more at risk compared to the rest of the population. However, SES is prone to measurement error. In this paper, we propose a GD-ILM which accounts for measurement error in both individual-level and area-level covariates. A Monte Carlo expectation conditional maximisation algorithm is used for inference. We use models fitted to data to predict areas with high average infectivity rates. We evaluate the performance of the proposed approach through simulation studies and by a real-data application on influenza data in Manitoba, Canada. [ABSTRACT FROM AUTHOR]

The recent outbreak of novel coronavirus has highlighted the need for a benefit‐cost framework to guide unconventional public health interventions aimed at reducing close contact between infected and susceptible individuals. In this paper, we propose an optimal control problem for an infectious disease model, wherein the social planner can control the transmission rate by implementing or lifting lockdown measures. The objective is to minimize total costs, which comprise infection costs, as well as fixed and variable costs associated with lockdown measures. We establish conditions concerning model primitives that guarantee the existence of a straightforward optimal policy. The policy specifies two switching points (a,b)$$ \left(a,b\right) $$, whereby the social planner institutes a lockdown when the percentage of infected individuals exceeds b$$ b $$, and reopens the economy when the percentage of infected individuals drops below a$$ a $$. We subsequently extend the model to cases where the social planner may implement multiple lockdown levels. Finally, numerical studies are conducted to gain additional insights into the value of these controls. [ABSTRACT FROM AUTHOR]

BackgroundDespite the elevated risks of infection transmission, people in prisons frequently encounter significant barriers in accessing essential healthcare services in many countries. The present scoping review aimed to evaluate the state of availability and model of delivery of vaccination services within correctional facilities across the globe.MethodsFollowing the methodological framework for scoping reviews and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews criteria, we conducted a systematic search across four peer-reviewed literature databases (Medline via PubMed, Web of Science, the Cochrane Library, Science Direct, and EBSCO), as well as 14 sources of grey literature. Two researchers meticulously examined the identified papers independently to extract pertinent data published between 2012 and 2022. The quality of the selected publications was assessed using established quality assessment tools.ResultsOf the 11,281 identified papers 52 met the inclusion criteria. With the exception of one, all the included publications presented data from high-income countries, predominantly originating from the United States. Across the world, the most prevalent vaccines available in prison settings were COVID-19 and HBV vaccines, typically distributed in response to health crises such as pandemics, epidemics, and local outbreaks. Vaccine coverage and uptake rates within correctional facilities displayed noteworthy disparities among various countries and regions. Besides, individual and organizational barriers and facilitating factors of vaccination in prison settings emerged and discussed in the text.DiscussionThe lack of vaccination services combined with low rates of vaccination coverage and uptake among people living and working in correctional facilities represents a cause for concern. Prisons are not isolated from the broader community, therefore, efforts to increase vaccine uptake among people who live and work in prisons will yield broader public health benefits.

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

IntroductionIn 2015, the National Institute of Dental and Craniofacial Research (NIDCR) launched the Multidisciplinary Collaborative Research Consortium to Reduce Oral Health Disparities in Children, supporting four randomized trials testing strategies to improve preventive care. A Coordinating Center provides scientific expertise, data acquisition and quality assurance services, safety monitoring, and final analysis-ready datasets. This paper describes the trials' economic analysis strategies, placing these strategies within the broader context of contemporary economic analysis methods.MethodsThe Coordinating Center established a Cost Collaborative Working Group to share information from the four trials about the components of their economic analyses. Study teams indicated data sources for their economic analysis using a set of structured tables. The Group meets regularly to share progress, discuss challenges, and coordinate analytic approaches.ResultsAll four trials will calculate incremental cost-effectiveness ratios; two will also conduct cost-utility analyses using proxy diseases to estimate health state utilities. Each trial will consider at least two perspectives. Key process measures include dental services provided to child participants. The non-preference-weighted Early Childhood Oral Health Impact Scale (ECOHIS) will measure oral health-related quality of life. All trials are measuring training, implementation, personnel and supervision, service, supplies, and equipment costs.ConclusionsConsistent with best practices, all four trials have integrated economic analysis during their planning stages. This effort is critical since poor quality or absence of essential data can limit retrospective analysis. Integrating economic analysis into oral health preventive intervention research can provide guidance to clinicians and practices, payers, and policymakers.

BackgroundVaccine hesitancy, delaying or refusing to vaccinate despite the availability of vaccines, impedes the progress of achieving optimal HPV vaccine coverage. Little is known about the sources of human papillomavirus (HPV) vaccine hesitancy among racially/ethnically and geographically diverse communities. The purpose of this paper is to explore HPV vaccine hesitancy among rural, Slavic, and Latino communities that reside in counties with low HPV vaccine uptake rates.MethodsKey informant interviews and focus groups were conducted with rural, Slavic, and Latino communities that reside within counties in California that have low HPV vaccine up to date rates (16-25%). Qualitative data were transcribed verbatim and analyzed using inductive and deductive thematic analysis.ResultsA total of seven focus groups and 14 key informant interviews were conducted with 39 individuals from seven California counties. Salient themes that contributed to HPV vaccine hesitancy included the following: social media and the anti-vaccination movement; a strong belief in acquiring immunity naturally; prior vaccine experiences; and vaccine timing concerns. Participants suggested the provision of culturally appropriate, in-language, in-person easy to understand HPV vaccine education to mitigate HPV vaccine hesitancy.ConclusionsOur findings can inform future interventions to increase HPV vaccine uptake among hesitant communities.

BackgroundHIV or sexually transmitted infections remain a significant public health concern in the United States, with adolescents affected disproportionately. Adolescents engage in HIV/STI risk behaviors, including drug use and condomless sex, which increase the risk for HIV/STIs. At-risk adolescents, many of whom are racial minorities, experience HIV/STI disparities. Although at-risk adolescents are disproportionately affected by HIV/STI risk behaviors and infections and although the Centers for Disease Control and Prevention recommends routine HIV/STI testing for adolescents, relatively few adolescents report having ever been tested for HIV/STI. With expected increases in health clinic visits as a result of the Affordable Care Act combined with technological advances, health clinics and mobile health (mHealth), including apps, provide innovative contexts and tools to engage at-risk adolescents in HIV/STI prevention programs. Yet, there is a dearth of efficacious mHealth interventions in health clinics to prevent and reduce both condomless sex and drug use and increase HIV/STI testing for at-risk adolescents.ObjectiveTo address this gap in knowledge, we developed a theory-driven, culturally congruent mHealth intervention (hereon referred to as S4E [Storytelling 4 Empowerment]) that has demonstrated feasibility and acceptability in a clinical setting. The next step is to examine the preliminary efficacy of S4E on adolescent HIV/STI testing and risk behaviors. This goal will be accomplished by 2 aims: the first aim is to develop a cross-platform and universal version of S4E. The cross-platform and universal version of S4E will be compatible with both iOS and Android operating systems and multiple mobile devices, aimed at providing adolescents with ongoing access to the intervention once they leave the clinic, and the second aim is to evaluate the preliminary efficacy of S4E, relative to usual care control condition, in preventing or reducing drug use and condomless sex and increasing HIV/STI testing in a clinical sample of at-risk adolescents aged 14-21 years living in Southeast Michigan.MethodsIn this study, 100 adolescents recruited from a youth-centered community health clinic will be randomized via blocked randomization with random sequences of block sizes to one of the 2 conditions: S4E mHealth intervention or usual care. Theory-driven and culturally congruent, S4E is an mHealth adaptation of face-to-face storytelling for empowerment, which is registered with the Substance Abuse and Mental Health Services Administration's National Registry of Evidence-Based Programs and Practices.ResultsThis paper describes the protocol of our study. The recruitment began on May 1, 2018. This study was registered on December 11, 2017, in ClinicalTrials.gov. All participants have been recruited. Data analysis will be complete by the end of March 2024, with study findings available by December 2024.ConclusionsThis study has the potential to improve public health by preventing HIV/STI and substance use disorders.Trial registrationClinicalTrials.gov NCT03368456; https://clinicaltrials.gov/study/NCT03368456.International registered report identifier (irrid)DERR1-10.2196/47216.

ObjectivesSurveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.MethodsThis cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.ResultsWe developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.ConclusionsThe combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Introduction: Pandemics are global health crises that have caused millions of deaths, enormous socio-economic disruptions, and considerable public and global health security consequences. This study assessed Nigerians' knowledge, attitudes, and perceptions regarding preparedness for the next pandemics. Methods: A cross-sectional questionnaire-based national survey was conducted among 1276 respondents in Nigeria to determine public knowledge, attitudes, and perceptions of preparedness for future pandemics. Descriptive and inferential statistics were used for analyses. Results: Of the 1276 respondents to this survey, > 50% had not heard of the Spanish flu and Zika disease. Approximately half (594) of the respondents did not consider antimicrobial resistance a possible cause of future pandemics. Over an average proportion of respondents reported unsatisfactory knowledge (655, 51.3%) and poor perceptions (695, 54.5%) about pandemic preparedness. The respondents' good knowledge and satisfactory attitudes toward pandemic preparedness decreased with age. The respondents' education, age, and state of residence were significantly associated with good knowledge, satisfactory attitudes, and positive perceptions of pandemic preparedness with respondents with tertiary education levels being at least two times more likely to have these positive outcomes. Conclusion: This study revealed serious gaps in Nigerians' knowledge, attitudes, and perceptions of their readiness for upcoming pandemics. To protect Nigerians' health, emphasis should be placed on these issues by all stakeholders, including the government. [ABSTRACT FROM AUTHOR]

This study introduces an SIRS compartmental mathematical model encompassing vaccination and variable immunity periods for infectious diseases. I derive a basic reproduction number formula and assess the local and global stability of disease-free and the local stability of the endemic equilibria. I demonstrate that the basic reproduction number in the presence of a vaccine is highly sensitive to the rate of immunity loss, and even a slight reduction in this rate can significantly contribute to disease control. Additionally, I have derived a formula to calculate the critical efficacy period required for a vaccine to effectively manage and control the disease.The analysis conducted for the model suggests that increasing the vaccine’s immunity duration (efficacy) decelerates disease dynamics, leading to reduced rates of reinfection and less severe disease outcomes. Furthermore, this delay contributes to a decrease in the basic reproduction number ( R 0 ), thus facilitating more rapid disease control efforts. [ABSTRACT FROM AUTHOR]

Background: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. Methods: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. Results: In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P =.012). The same trend was observed in Arabic, albeit without statistical significance (P =.082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P =.002). Conclusions: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes. [ABSTRACT FROM AUTHOR]

Background: Fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) has been suggested as a useful imaging method for diagnosing cyst infections in patients with autosomal dominant polycystic kidney disease (ADPKD). The aim of this article is to provide evidence-based data in this setting. Methods: A systematic literature review (exploring several bibliographic databases) and a bivariate meta-analysis were carried out to calculate the pooled diagnostic performance of [18F]FDG PET/CT in diagnosing probable cyst infection in ADPKD. Results: Ten studies (282 PET/CT scans and 249 patients) were included in the analysis. The pooled sensitivity and specificity of [18F]FDG PET/CT in this setting were 84.6% (95% confidence interval: 75.4–90.7) and 94.9% (95% confidence interval: 72.6–99.2), respectively, without statistical heterogeneity or significant publication bias. [18F]FDG PET/CT significantly changed patient management in more than half of ADPKD patients with suspected cyst infection. Conclusions: [18F]FDG PET/CT has high performance in diagnosing probable cyst infections in ADPKD patients with an impact on management in the majority of patients. Although more studies are warranted, the provided evidence-based data are an important step towards the integration of [18F]FDG PET/CT in clinical and diagnostic guidelines on probable cyst infection in ADPKD patients. [ABSTRACT FROM AUTHOR]

Purpose: To provide summarization of the most significant infectious diseases (ID) pharmacotherapy articles published in peer-reviewed literature in 2022. Summary: Members of the Houston Infectious Diseases Network (HIDN) nominated notable articles providing significant contributions to ID pharmacotherapy in 2022. Article nominations included those pertaining to general ID, as well as those including coronavirus disease 2019 (COVID-19), and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) pharmacotherapy. A total of 71 articles were nominated by HIDN. Members: 68 articles pertaining to general ID pharmacotherapy and 3 articles focusing on HIV/AIDS. To aid selection of the most these most notable articles of 2022, a survey was created and distributed to members of the Society of Infectious Diseases Pharmacists (SIDP). Of the 153 SIDP members who participated in the survey, there were 128 recorded votes for the top 10 general ID pharmacotherapy articles and 30 votes recorded for the top HIV/AIDS article. The most notable publications are summarized. Conclusion: Post pandemic significant advances in antimicrobial stewardship and infectious disease states continues to occur in a world recently focused on the coronavirus disease 2019 (COVID-19) global pandemic. Continuous growth in publication of ID-related articles over the past year lends towards the aims of this review to aid clinicians in remaining current on key practice-changing ID pharmacotherapy publications from 2022. [ABSTRACT FROM AUTHOR]

Climate change, caused by both natural and human activities, poses significant threats to global health, including rising temperatures, extreme weather events, and environmental disruptions. The One Health concept, emphasizing the interconnectedness of human, animal, and environmental health, is crucial in addressing these challenges. Climate change is linked to the emergence and resurgence of infectious diseases, resulting in substantial economic losses worldwide due to outbreaks, floods, and disrupted trade, among other factors. Therefore, it is crucial to adapt to this changing climate to safeguard One Health. By implementing a One Health approach, we can effectively address the consequences of climate change and make substantial contributions to health and food safety. This approach also holds the potential to mitigate economic losses, particularly in low and middle-income countries. Additionally, in the future, research interventions based on the One Health framework may offer sustainable solutions to combat climate change. [ABSTRACT FROM AUTHOR]

This case report describes the case of a female infant hospitalized for severe pneumonia. During the treatment process, various antibiotics are used to treat and prevent further infection. The child had a weak physical condition, combined with neuroblastoma, paraneoplastic syndrome, and low immune function, leading to Tsukamurella tyrosinosolvens infection. The treatment was eventually abandoned owing to poor prognosis. This study aims to through the medium, dyeing, electron microscope observation, 16s rRNA and high-throughput sequencing investigated the morphological characteristics, staining properties, electron microscope morphology, antibiotic resistance, and genomic characteristics of Tsukamurella tyrosinosolvens. The aim of the study is to provide data reference for clinical laboratory staff in bacteria identification research, and to provide relevant help for clinicians in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is one of the major burdens to developing nations. India is among the countries categorized by the World Health Organization (WHO) as experiencing a significant burden of TB, HIV-associated TB, and MDR/RR-TB within the context of low- and middle-income nations. Globally South East Asian and African region alone accounted for 82% of death (HIV negative), where India accounts for 36% of TB fatalities. Materials and Methods: The data was extracted from NIKSHAY web portal on TB notification cases in UP, India. The random effects meta-analysis was performed to estimate pooled proportion of TB cases in public and private notified during both the periods. Spatial analysis was used to display the spread of TB cases across the state, during both the periods. Results: A total 75 districts data on TB notified cases were available for current investigation. The pooled proportion of TB cases were 0.24 (0.21, 0.27) and 0.76 (0.73, 0.79) during COVID-19 period for private and public hospitals, respectively. Similarly, the pooled proportion of TB cases were 0.26 (0.23, 0.29) and 0.74 (0.71, 0.77) post-COVID-19 for private and public hospitals, respectively. Conclusion: The proportion of TB cases were more in public hospital as compared to private hospitals during COVID-19 period. Similarly, result was obtained in post-COVID-19 period. [ABSTRACT FROM AUTHOR]