Opportunities and challenges in modeling emerging infectious diseases

Fabrication of photo-induced molecular superoxide radical generator for highly efficient therapy against bacterial wound infection

The pressing need for highly efficient antibacterial strategies arises from the prevalence of microbial biofilm infections and the emergence of rapidly evolving antibiotic-resistant strains of pathogenic bacteria. Photodynamic therapy represents a highly efficient and compelling antibacterial approach, offering promising prospects for effective control of the development of bacterial resistance. However, the effectiveness of many photosensitizers is limited due to the reduced generation of reactive oxygen species (ROS) in hypoxic microenvironment, which commonly occur in pathological conditions such as inflammatory and bacteria-infected wounds. Herein, we designed and prepared two phenothiazine-derived photosensitizers (NB-1 and NB-2), which can effectively generate superoxide anion radicals (O2●-) through the type I process. Both photosensitizers demonstrate significant efficacy in vitro for the eradication of broad-spectrum bacteria. Moreover, NB-2 possesses distinct advantages including strong membrane binding and strong generation of O2●-, rendering it an exceptionally efficient antibacterial agent against mature biofilms. In addition, laser activated NB-2 could be applied to treat MRSA-infected wound in vivo, which offers new opportunities for potential practical applications.

A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases

BACKGROUND

The early identification of outbreaks of both known and novel influenza-like illnesses is an important public health problem.

OBJECTIVE

The design and testing of a tool that detects and tracks outbreaks of both known and novel influenza-like illness, such as the SARS-CoV-19 worldwide pandemic, accurately and early.

METHODS

This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease which may represent a novel disease outbreak.

RESULTS

We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2014 through May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus EV-D68.

CONCLUSIONS

The results reported in this paper provide support that ILI Tracker was able to track well the incidence of four modeled influenza-like diseases over a one-year period, relative to laboratory confirmed cases, and it was computationally efficient in doing so. The system was alsoable to detect a likely novel outbreak of the enterovirus D68 early in an outbreak that occurred in Allegheny County in 2014, as well as clinically characterize that outbreak disease accurately.

CLINICALTRIAL

Engineering Sizable and Broad-Spectrum Antibacterial Fabrics through Hydrogen Bonding Interaction and Electrostatic Interaction

Long-lasting and highly efficient antibacterial fabrics play a key role in public health occurrences caused by bacterial and viral infections. However, the production of antibacterial fabrics with a large size, highly efficient, and broad-spectrum antibacterial performance remains a great challenge due to the complex processes. Herein, we demonstrate sizable and highly efficient antibacterial fabrics through hydrogen bonding interaction and electrostatic interaction between surface groups of ZnO nanoparticles and fabric fibers. The production process can be carried out at room temperature and achieve a production rate of 300 × 1 m2 within 1 h. Under both visible light and dark conditions, the bactericidal rate against Gram-positive (S. aureus), Gram-negative (E. coli), and multidrug-resistant (MRSA) bacteria can reach an impressive 99.99%. Furthermore, the fabricated ZnO nanoparticle-decorated antibacterial fabrics (ZnO@fabric) show high stability and long-lasting antibacterial performance, making them easy to develop into variable antibacterial blocks for protection suits.

The impacts of spatial-temporal heterogeneity of human-to-human contacts on the extinction probability of infectious disease from branching process model

In this study, we focus on the impacts of spatial-temporal heterogeneity of human-to-human contacts on the spread of infectious diseases and develop a multi-type branching process model by introducing random human-to-human contact mode into a structured population. We provide the general formulas of the generation size, extinction probability, and basic reproduction number of the proposed branching process model. The result shows that the natural temporal heterogeneity (i.e. random contacts over time) can lead to a higher extinction probability while remains the same basic reproduction number and generation size. This is also numerically verified by choosing the real contact distributions from different circumstances of four countries. In addition, we observe a non-monotonic pattern of the differences, against the transmission probability and the mean contact rate, between the extinction probabilities under the constant and random contact patterns. Given the spatial heterogeneity, we show that it can contribute to the increase of basic reproduction number, but also increase the extinction probability of the infectious disease. This study adds novel insights to the course of the impact of heterogeneity on the transmission dynamics and also provides additional evidence for the limited role of reproduction numbers.

ePyDGGA: automatic configuration for fitting epidemic curves

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health.

A Bayesian System to Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases

It would be highly desirable to have a tool that detects the outbreak of a new influenza-like illness, such as COVID-19, accurately and early. This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in a hospital emergency department using findings extracted from patient-care reports using natural language processing. We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2010 through May 31, 2015. We then show how the algorithm can be extended to detect the presence of an unmodeled disease which may represent a novel disease outbreak. We also include results for detecting an outbreak of an unmodeled disease during the mentioned time period, which in retrospect was very likely an outbreak of Enterovirus D68.

A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Biomimetic cell membrane-coated poly(lactic-co-glycolic acid) nanoparticles for biomedical applications

Poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) are commonly used for drug delivery because of their favored biocompatibility and suitability for sustained and controlled drug release. To prolong NP circulation time, enable target-specific drug delivery and overcome physiological barriers, NPs camouflaged in cell membranes have been developed and evaluated to improve drug delivery. Here, we discuss recent advances in cell membrane-coated PLGA NPs, their preparation methods, and their application to cancer therapy, management of inflammation, treatment of cardiovascular disease and control of infection. We address the current challenges and highlight future research directions needed for effective use of cell membrane-camouflaged NPs.

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.

Epidemiological characteristics of imported acute infectious diseases in Guangzhou, China, 2005-2019

BACKGROUND

The global spread of infectious diseases is currently a prominent threat to public health, with the accelerating pace of globalization and frequent international personnel intercourse. The present study examined the epidemiological characteristics of overseas imported cases of acute infectious diseases in Guangzhou, China.

METHODS

We retrospectively investigated the distribution of diseases, demographic characteristics, and temporal and spatial variations of imported cases of acute infectious diseases in Guangzhou based on the surveillance data of notifiable infectious diseases from 2005 to 2019, provided by Guangzhou center for Disease Control and Prevention. The Cochran-Armitage trend test was applied to examine the trend in the number of imported cases over time.

RESULTS

A total of 1,025 overseas imported cases of acute infectious diseases were identified during the study period. The top three diseases were dengue (67.12%), malaria (12.39%), and influenza A (H1N1) pdm09 (4.10%). Imported cases were predominantly males, with a sex ratio of 2.6: 1 and 75.22% of the cases were those aged 20-49 years. Businessmen, workers, students and unemployed persons accounted for a large proportion of the cases (68.49%) and many of the cases came from Southeast Asia (59.02%). The number of imported cases of acute infectious diseases increased during the study period and hit 318 in 2019. A clear seasonal pattern was observed in the number of imported cases with a peak period between June and November. Imported cases were reported in all of the 11 districts in Guangzhou and the central districts were more seriously affected compared with other districts.

CONCLUSIONS

The burden of dengue imported from overseas was substantial and increasing in Guangzhou, China, with the peak period from June to November. Dengue was the most common imported disease. Most imported cases were males aged 20-49 years and businessmen. Further efforts, such as strengthening surveillance of imported cases, paying close attention to the epidemics in hotspots, and improving the ability to detect the imported cases from overseas, are warranted to control infectious diseases especially in the center of the city with a higher population density highly affected by imported cases.

Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks

Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.

A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR

Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. In this paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. First, we combine a standard epidemic dynamic, the susceptible–exposed–infected–recovered (SEIR) model with population migration. This model can provide a biological spread process for emerging infectious diseases. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. Moreover, an objective function is defined to minimize the error based on these data. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method.

Apathogenic proxies for transmission dynamics of a fatal virus

Identifying drivers of transmission-especially of emerging pathogens-is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission-even based on analogously-transmitted, apathogenic agents-in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

Modeling HIV transmission among persons who inject drugs (PWID) at the "End of the HIV Epidemic" and during the COVID-19 pandemic

BACKGROUND

We explore injecting risk and HIV incidence among PWID in New York City (NYC), from 2012 to 2019, when incidence was extremely low, <0.1/100 person-years at risk, and during disruption of prevention services due to the COVID-19 pandemic.

METHODS

We developed an Agent-Based model (ABM) to simulate sharing injecting equipment and measure HIV incidence in NYC. The model was adapted from a previous ABM model developed to compare HIV transmission with "high" versus "low" dead space syringes. Data for applying the model to NYC during the period of very low HIV incidence was taken from the "Risk Factors" study, a long-running study of participants entering substance use treatment in NYC. Injecting risk behavior had not been eliminated in this population, with approximately 15 % reported recent syringe sharing. Data for possible transmission during COVID-19 disruption was taken from previous HIV outbreaks and early studies of the pandemic in NYC.

RESULTS

The modeled incidence rates fell within the 95 % confidence bounds of all of the empirically observed incidence rates, without any additional calibration of the model. Potential COVID-19 disruptions increased the probability of an outbreak from 0.03 to 0.25.

CONCLUSIONS

The primary factors in the very low HIV incidence were the extremely small numbers of PWID likely to transmit HIV and that most sharing occurs within small, relatively stable, mostly seroconcordant groups. Containing an HIV outbreak among PWID during a continuing pandemic would be quite difficult. Pre-pandemic levels of HIV prevention services should be restored as quickly as feasible.

Learning From the Past: The Role of Social and Behavior Change Programming in Public Health Emergencies

The contributions of social and behavior change research/programming in 6 recent epidemics highlight the importance of further integrating such expertise into outbreak response.

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.

Think like a Virus: Toward Improving Nanovaccine Development against SARS-CoV-2

There is no doubt that infectious diseases present global impact on the economy, society, health, mental state, and even political aspects, causing a long-lasting dent, and the situation will surely worsen if and when the viral spread becomes out of control, as seen during the still ongoing coronavirus disease 2019 (COVID-19) pandemic. Despite the considerable achievements made in viral prevention and treatment, there are still significant challenges that can be overcome through careful understanding of the viral mechanism of action to establish common ground for innovating new preventative and treatment strategies. Viruses can be regarded as devil nanomachines, and one innovative approach to face and stop the spread of viral infections is the development of nanoparticles that can act similar to them as drug/vaccine carriers. Moreover, we can use the properties that different viruses have in designing nanoparticles that reassemble the virus conformational structures but that do not present the detrimental threats to human health that native viruses possess. This review discusses the current preventative strategies (i.e., vaccination) used in facing viral infections and the associated limitations, highlighting the importance of innovating new approaches to face viral infectious diseases and discussing the current nanoapplications in vaccine development and the challenges that still face the nanovaccine field.

Photocatalytic Biocidal Coatings Featuring Zr6Ti4-Based Metal-Organic Frameworks

The world is currently suffering socially, economically, and politically from the recent pandemic outbreak due to the coronavirus disease 2019 (COVID-19), and those in hospitals, schools, and elderly nursing homes face enhanced threats. Healthcare textiles, such as masks and medical staff gowns, are susceptible to contamination of various pathogenic microorganisms, including bacteria and viruses. Metal-organic frameworks (MOFs) can potentially address these challenges due to their tunable reactivity and ability to be incorporated as porous coatings on textile materials. Here, we report how incorporating titanium into the zirconium-pyrene-based MOF NU-1000, denoted as NU-1012, generates a highly reactive biocidal photocatalyst. This MOF features a rare ligand migration phenomenon, and both the Ti/Zr center and the pyrene linker act synergistically as dual active centers and widen the absorption band for this material, which results in enhanced reactive oxygen species generation upon visible light irradiation. Additionally, we found that the ligand migration process is generally applicable to other csq topology Zr-MOFs. Importantly, NU-1012 can be easily incorporated onto cotton textile cloths as a coating, and the resulting composite material demonstrates fast and potent biocidal activity against Gram-negative bacteria (Escherichia coli), Gram-positive bacteria (Staphylococcus epidermidis), and T7 bacteriophage virus with up to a 7-log(99.99999%) reduction within 1 h under simulated daylight.

Poly(lactic acid)/artificially cultured diatom frustules nanofibrous membranes with fast and controllable degradation rates for air filtration

The worldwide pandemic, coronavirus COVID-19, has been posing a serious threat to the global economy and security in last 2 years. The monthly consumption and subsequent discarding of 129 billion masks (equivalent to 645,000 tons) pose a serious detrimental impact on environmental sustainability. In this study, we report a novel type of nanofibrous membranes (NFMs) with supreme filtration performance and controllable degradation rates, which are mainly composed of polylactic acid (PLA) and artificially cultured diatom frustules (DFs). In this way, the filtration efficiency of particular matter (PM) and the pressure drop were significantly improved in the prepared PLA/DFs NFMs as compared with the neat PLA NFM. In specific, with incorporation of 5% DFs into fibers, PM_0.3 removal with a filtration efficiency of over 99% and a pressure drop of 109 Pa were achieved with a membrane thickness of only 0.1 mm. Moreover, the yield strength and crystallinity degree of the PLA/DFs5 NFMs were sharply increased from 1.88 Mpa and 26.37% to 2.72 Mpa and 30.02%. Besides those unique characters, the PLA/DFs5 presented excellent degradability, accompanied by the degradation of 38% in 0.01 M sodium hydroxide solution after 7 days and approximately 100% in natural condition after 42 days, respectively. Meanwhile, the environmentally friendly raw materials of the composite polylactic acid and artificially cultured diatom frustules could be extracted from corn-derived biomass and artificially cultivated diatoms, ensuring the conformance to carbon neutrality and promising applications in personal protection.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42114-022-00474-7.

Over half of known human pathogenic diseases can be aggravated by climate change

It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.

Pathogen infection-responsive nanoplatform targeting macrophage endoplasmic reticulum for treating life-threatening systemic infection

Systemic infections caused by life-threatening pathogens represent one of the main factors leading to clinical death. In this study, we developed a pathogen infection-responsive and macrophage endoplasmic reticulum-targeting nanoplatform to alleviate systemic infections. The nanoplatform is composed of large-pore mesoporous silica nanoparticles (MSNs) grafted by an endoplasmic reticulum-targeting peptide, and a pathogen infection-responsive cap containing the reactive oxygen species-cleavable boronobenzyl acid linker and bovine serum albumin. The capped MSNs exhibited the capacity to high-efficiently load the antimicrobial peptide melittin, and to rapidly release the cargo triggered by H₂O₂ or the pathogen-macrophage interaction system, but had no obvious toxicity to macrophages. During the interaction with pathogenic Candida albicans cells and macrophages, the melittin-loading nanoplatform MSNE+MEL+TPB strongly inhibited pathogen growth, survived macrophages, and suppressed endoplasmic reticulum stress together with pro-inflammatory cytokine secretion. In a systemic infection model, the nanoplatform efficiently prevented kidney dysfunction, alleviated inflammatory symptoms, and protected the mice from death. This study developed a macrophage organelle-targeting nanoplatform for treatment of life-threatening systemic infections.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material (N₂ adsorption curves of the initial synthesized MSNs, FT-IR spectra of MSN, and MSNE, MEL release from the FITC-MEL-loading MSNE + TPB induced by different concentration of H₂O₂, viability of NIH3T3 cells, and DC2.4 cells after treatment of free MEL or the used nanoparticles, effect of MEL on C. albicans growth and macrophage death during the interaction between C. albicans and macrophages, effect of MEL and the nanoparticles on S. aureus growth and macrophage death during the interaction between S. aureus and macrophages, quantification of GRP78 (a) and activated Caspase-3, flow cytometry analysis of kidney non-macrophages with the Alexa Fluor 594 signal, survival curve of the infected mice treated by MEL or MSNE + MEL, kidney burden, blood urea levels and serum TNF-α levels in the infected mice) is available in the online version of this article at 10.1007/s12274-022-4211-z.

Development of PET Fabrics Containing N-halamine Compounds with Durable Antibacterial Property

Antibacterial textile materials are widely used in daily life, but most are disposable products with poor antibacterial durability. N-halamine can rapidly inactivate microorganisms, has good stability, and shows great potential applications in antibacterial fabrics. In this study, an N-halamine monomer precursor was synthesized and treated onto PET fabrics. The treated PET fabrics were rendered antibacterial functionality after chlorination, and exhibited good antibacterial properties with inactivation rate of 100.0 % for both E. coli O157:H7 and S. aureus. After 50 wash cycles, the chlorinated treated PET fabrics could maintain 80.0 % antibacterial efficacy, demonstrating durable antibacterial properties. Storage stability and UV irradiation tests showed that the treated PET fabrics had remarkable regenerable properties. The reduction of the breaking strength was within 12 % after treatment, which is in a satisfying range in antimicrobial finishing.

A simple model for how the risk of pandemics from different virus families depends on viral and human traits

Different virus families, like influenza or corona viruses, exhibit characteristic traits such as typical modes of transmission and replication as well as specific animal reservoirs in which each family of viruses circulate. These traits of genetically related groups of viruses influence how easily an animal virus can adapt to infect humans, how well novel human variants can spread in the population, and the risk of causing a global pandemic. Relating the traits of virus families to their risk of causing future pandemics, and identification of the key time scales within which public health interventions can control the spread of a new virus that could cause a pandemic, are obviously significant. We address these issues using a minimal model whose parameters are related to characteristic traits of different virus families. A key trait of viruses that "spillover" from animal reservoirs to infect humans is their ability to propagate infection through the human population (fitness). We find that the risk of pandemics emerging from virus families characterized by a wide distribution of the fitness of spillover strains is much higher than if such strains were characterized by narrow fitness distributions around the same mean. The dependences of the risk of a pandemic on various model parameters exhibit inflection points. We find that these inflection points define informative thresholds. For example, the inflection point in variation of pandemic risk with time after the spillover represents a threshold time beyond which global interventions would likely be too late to prevent a pandemic.

High-Efficiency Air Filter Media with a Three-Dimensional Network Composed of Core-Shell Zeolitic Imidazolate Framework-8@Tunicate Nanocellulose for PM0.3 Removal

Particulate matter (PM) in air has seriously endangered human health. Especially, PM0.3 can easily enter the lungs and blood through breathing. Herein, an air filter with a three-dimensional (3D) network consisting of core-shell structured fibers was designed by in situ growth of zeolitic imidazolate framework-8 on tunicate nanocellulose/glass fiber composite filter media (ZIF-8@TNC/GF). The filtration performance of the obtained ZIF-8@TNC/GF membranes against sodium chloride particles with the MPPS (most penetrating particle size) was investigated. The air filter media at the optimal ratio of ZIF-8 exhibited an ultrahigh efficiency of 99.998% and a quality factor of 0.0308 Pa^-1 for PM0.3. Further characterizations showed that the ZIF-8@TNC/GF air filter had a hierarchical and rich pore structure, showing a large specific surface area (50.3 m² g^-1). More significantly, compared with the TNC/GF prepared by the dipping method, TNCs changed from the original two-dimensional (2D) nonuniform network to a uniform 3D network after the ZIF-8 was introduced. Moreover, the ZIF-8@TNC fibers with a core-shell structure inhibited the aggregation of nanocellulose. This study will shed light on the fabrication of high-efficiency TNC composite air filter media with fluffy 3D networks.

Printed Nanochain-Based Colorimetric Assay for Quantitative Virus Detection

Fast and ultrasensitive detection of pathogens is very important for efficient monitoring and prevention of viral infections. Here, we demonstrate a label-free optical detection approach that uses a printed nanochain assay for colorimetric quantitative testing of viruses. The antibody-modified nanochains have high activity and specificity which can rapidly identify target viruses directly from biofluids in 15 min, as well as differentiate their subtypes. Arising from the resonance induced near-field enhancement, the color of nanochains changes with the binding of viruses that are easily observed by a smartphone. We achieve the detection limit of 1 PFU μL^-1 through optimizing the optical response of nanochains in visible region. Besides, it allows for real-time response to virus concentrations ranging from 0 to 1.0×10⁵  PFU mL^-1 . This low-cost and portable platform is also applicable to rapid detection of other biomarkers, making it attractive for many clinical applications.

Challenges in modeling the emergence of novel pathogens

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.

In Situ Functionalization of Cellulose with Zinc Pyrithione for Antimicrobial Applications

Considering the public health demands for stronger and effective personal protective clothing, herein, antimicrobial fabrics using a known bacteriostatic and fungistatic drug zinc pyrithione (ZPT) have been reported. ZPT was synthesized in situ on cellulosic fabric, viscose (VC), using a zinc metal precursor and 2-mercaptopyridine-N-oxide as a ligand (VC-ZPT). For comparison, viscose was also phosphorylated (VP) before in situ functionalization with ZPT (VP-ZPT). Both approaches provided adequate protection from microbes; however, functionalization of cellulose with phosphate (VP) resulted in the formation of a linking group between cellulose and ZPT, which exhibited better uniformity of ZPT over the fabric surface and higher durability to washing. The functionalization was confirmed by inductively coupled plasma mass spectroscopy (ICP-MS), scanning electron microscopy (SEM), and Raman spectroscopy. Further, the bonding of phosphate with ZPT was confirmed by ³¹P solid-state NMR. The physical properties, such as appearance, bending length, and mechanical strength, of the treated fabrics remained unchanged. The antimicrobial activities of VP-ZPT with VC-ZPT were studied against Escherichia coli, Staphylococcus aureus, and Candida albicans, which were found to be effective until 20 laundry cycles in VP-ZPT. Additionally, VP-ZPT samples exhibited poor adherence of bacteria on the fabric surface. The functionalized fabrics may find applications for topical skin diseases in reducing the necessity of repeated use of antibiotic ointments.

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Why are there so few (or so many) circulating coronaviruses?

Despite vast diversity in non-human hosts and conspicuous recent spillover events, only a small number of coronaviruses have been observed to persist in human populations. This puzzling mismatch suggests substantial barriers to establishment. We detail hypotheses that might contribute to explain the low numbers of endemic coronaviruses, despite their considerable evolutionary and emergence potential. We assess possible explanations ranging from issues of ascertainment, historically lower opportunities for spillover, aspects of human demographic changes, and features of pathogen biology and pre-existing adaptive immunity to related viruses. We describe how successful emergent viral species must triangulate transmission, virulence, and host immunity to maintain circulation. Characterizing the factors that might shape the limits of viral persistence can delineate promising research directions to better understand the combinations of pathogens and contexts that are most likely to lead to spillover.

Sleep Quality in Medical Staffs During the Outbreak of Coronavirus Disease 2019 in China: A Web-Based Cross-Sectional Study

Background: The aim of this study was to describe the sleep quality and its influencing factors among medical workers of different working statuses and staff types during the coronavirus disease 2019 (COVID-19) epidemic. Methods: Through an online questionnaire survey, all medical staffs in Xiangya Hospital were invited to complete sections on general information, the Self-Rating Scale of Sleep (SRSS), the Depression, Anxiety and Stress Scale (DASS-21), the Social Support Rating Scale (SSRS), and the Simplified Coping Style Questionnaire (CSQ). Results: A total of 4,245 respondents completed the survey. Among them, 38.7% had sleep disturbance. After matching, the SRSS scores in the staffs who were assigned to the intensive care unit (ICU) of Union Hospital in Wuhan and working in the epidemic area of Xiangya Hospital were not significantly different (P > 0.05); the SRSS scores in the battlefront staffs were significantly higher than (P < 0.05) those who were not treating patients infected with COVID-19. The SRSS scores of nurses were significantly higher than those of doctors and hospital administrators (P < 0.01). Anxiety, depression, and coping style were associated with sleep disturbance. Conclusion: The sleep quality of the medical staffs has been impaired during the epidemic period, especially among nurses, doctors, and administrators who are working on the front line. Medical institutions should strengthen psychological services and coping strategies for medical staffs.

Multistructured Electrospun Nanofibers for Air Filtration: A Review

Air filtration materials (AFMs) have gradually become a research hotspot on account of the increasing attention paid to the global air quality problem. However, most AFMs cannot balance the contradiction between high filtration efficiency and low pressure drop. Electrospinning nanofibers have a large surface area to volume ratio, an adjustable porous structure, and a simple preparation process that make them an appropriate candidate for filtration materials. Therefore, electrospun nanofibers have attracted increased attention in air filtration applications. In this paper, first, the preparation methods of high-performance electrospun air filtration membranes (EAFMs) and the typical surface structures and filtration principles of electrospun fibers for air filtration are reviewed. Second, the research progress of EAFMs with multistructures, including nanoprotrusion, wrinkled, porous, branched, hollow, core-shell, ribbon, beaded, nets structure, and the application of these nanofibers in air filtration are summarized. Finally, challenges with the fabrication of EAFMs, limitations of their use, and trends for future developments are presented.

Unique "posture" of rose Bengal for fabricating personal protective equipment with enhanced daylight-induced biocidal efficiency

The aggregation-caused self-quenching of photosensitizers (PS), especially on a solid substrate, has highly limited their photo-induced biocidal efficiency in practical applications. Here, we designed a unique "posture" of rose Bengal (RB) on cotton-based super-adsorptive fibrous equipment, with RB being separately captured in the mesopores of porous organic polymers (POPs). The resultant daylight-induced biocidal cotton fabric with enhanced efficiency was named as DBwEE-Cotton. The enhanced biocidal activity of the DBwEE-Cotton was achieved based on two mechanisms: (1) the separation of RB in mesopores on the fabric avoids the aggregation-caused self-quenching; and (2) other than singlet oxygen generation, RB is forced to undergo type I photoreaction by surrounding the RB with massive amounts of good hydrogen donors (i.e., POP) under daylight irradiation. Given the enhanced production efficiency of reactive oxygen species by the DBwEE-Cotton, 99.9999% of E. coli and L. innocua bacteria were killed within 20 min of daylight exposure. The DBwEE-Cotton also presents excellent wash and light durability with no biocidal function loss. The development of DBwEE-Cotton provides a facile strategy of avoiding aggregation-caused self-quenching and modulating photoreactions of PS on a flexible substrate, which may guide the design of novel personal protective equipment (PPE) integrated with improved biocidal efficiency, wearability, and repeated and long-term applicability for protecting people from lethal infectious diseases.

Statistical Estimation of the Reproductive Number From Case Notification Data

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

In Vitro and In Vivo Demonstration of Ultraefficient and Broad-Spectrum Antibacterial Agents for Photodynamic Antibacterial Chemotherapy

Increasing threats from both pathogenic infections and antibiotic resistance highlight the pressing demand for nonantibiotic agents and alternative therapies. Herein, we report several new phenothiazinium-based derivatives, which could be readily synthesized via fragment-based assembly, which exhibited remarkable bactericidal activities both in vitro and in vivo. Importantly, in contrast to numerous clinically and preclinically used antibacterial photosensitizers, these compounds were able to eliminate various types of microorganisms, including Gram-(+) Staphylococcus aureus (S. aureus), Gram-(-) Escherichia coli, multidrug-resistant S. aureus, and their associated biofilms, at low drug and light dosages (e.g., 0.21 ng/mL in vitro and 1.63 ng/cm² in vivo to eradicate S. aureus at 30 J/cm²). This study thus unveils the potential of these novel phenothiaziniums as potent antimicrobial agents for highly efficient photodynamic antibacterial chemotherapy.

A Scoping Insight on Potential Prophylactics, Vaccines and Therapeutic Weaponry for the Ongoing Novel Coronavirus (COVID-19) Pandemic- A Comprehensive Review

The emergence of highly virulent CoVs (SARS-CoV-2), the etiologic agent of novel ongoing "COVID-19" pandemics has been marked as an alarming case of pneumonia posing a large global healthcare crisis of unprecedented magnitude. Currently, the COVID-19 outbreak has fueled an international demand in the biomedical field for the mitigation of the fast-spreading illness, all through the urgent deployment of safe, effective, and rational therapeutic strategies along with epidemiological control. Confronted with such contagious respiratory distress, the global population has taken significant steps towards a more robust strategy of containment and quarantine to halt the total number of positive cases but such a strategy can only delay the spread. A substantial number of potential vaccine candidates are undergoing multiple clinical trials to combat COVID-19 disease, includes live-attenuated, inactivated, viral-vectored based, sub-unit vaccines, DNA, mRNA, peptide, adjuvant, plant, and nanoparticle-based vaccines. However, there are no licensed anti-COVID-19 drugs/therapies or vaccines that have proven to work as more effective therapeutic candidates in open-label clinical trial studies. To counteract the infection (SARS-CoV-2), many people are under prolonged treatment of many chemical drugs that inhibit the PLpro activity (Ribavirin), viral proteases (Lopinavir/Ritonavir), RdRp activity (Favipiravir, Remdesivir), viral membrane fusion (Umifenovir, Chloroquine phosphate (CQ), Hydroxychloroquine phosphate (HCQ), IL-6 overexpression (Tocilizumab, Siltuximab, Sarilumab). Mesenchymal Stem Cell therapy and Convalescent Plasma Therapy have emerged as a promising therapeutic strategy against SARS-CoV-2 virion. On the other hand, repurposing previously designed antiviral agents with tolerable safety profile and efficacy could be the only promising approach and fast response to the novel virion. In addition, research institutions and corporations have commenced the redesign of the available therapeutic strategy to manage the global crisis. Herein, we present succinct information on selected anti-COVID-19 therapeutic medications repurposed to combat SARS-CoV-2 infection. Finally, this review will provide exhaustive detail on recent prophylactic strategies and ongoing clinical trials to curb this deadly pandemic, outlining the major therapeutic areas for researchers to step in.

Using a clinical prediction rule to prioritize diagnostic testing leads to reduced transmission and hospital burden: A modeling example of early SARS-CoV-2

BACKGROUND

Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions.

METHODS

Using early SARS-CoV-2 as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2.

RESULTS

We found that applying this CPR (AUC: 0.69 (95% CI: 0.68 - 0.70)) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (i.e., "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden.

CONCLUSION

We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.

Regional forecasting of COVID-19 caseload by non-parametric regression: a VAR epidemiological model

OBJECTIVES

The COVID-19 pandemic (caused by SARS-CoV-2) has introduced significant challenges for accurate prediction of population morbidity and mortality by traditional variable-based methods of estimation. Challenges to modelling include inadequate viral physiology comprehension and fluctuating definitions of positivity between national-to-international data. This paper proposes that accurate forecasting of COVID-19 caseload may be best preformed non-parametrically, by vector autoregression (VAR) of verifiable data regionally.

METHODS

A non-linear VAR model across 7 major demographically representative New York City (NYC) metropolitan region counties was constructed using verifiable daily COVID-19 caseload data March 12-July 23, 2020. Through association of observed case trends with a series of (county-specific) data-driven dynamic interdependencies (lagged values), a systematically non-assumptive approximation of VAR representation for COVID-19 patterns to-date and prospective upcoming trends was produced.

RESULTS

Modified VAR regression of NYC area COVID-19 caseload trends proves highly significant modelling capacity of observed patterns in longitudinal disease incidence (county R2 range: 0.9221-0.9751, all p < 0.001). Predictively, VAR regression of daily caseload results at a county-wide level demonstrates considerable short-term forecasting fidelity (p < 0.001 at one-step ahead) with concurrent capacity for longer-term (tested 11-week period) inferences of consistent, reasonable upcoming patterns from latest (model data update) disease epidemiology.

CONCLUSIONS

In contrast to macroscopic variable-assumption projections, regionally-founded VAR modelling may substantially improve projection of short-term community disease burden, reduce potential for biostatistical error, as well as better model epidemiological effects resultant from intervention. Predictive VAR extrapolation of existing public health data at an interdependent regional scale may improve accuracy of current pandemic burden prognoses.

What Now? Epidemiology in the Wake of a Pandemic

The COVID-19 pandemic and the coming transition to a post-pandemic world where COVID-19 will likely remain as an incident disease present a host of challenges and opportunities in epidemiologic research. The scale and universality of this disruption to life and health provide unique opportunities to study phenomena, and health challenges, in all branches of epidemiology, from the obvious infectious disease and social consequences, to less clear impacts on chronic disease and cancer. If we are to both take advantage of the largest natural experiment of our lifetimes, and provide evidence to inform the numerous public health and clinical decisions being made every day, we must act quickly to ask critical questions and develop new methods to answer them. In doing so we should build on each of our strengths and expertise, and try to provide new insights rather than becoming yet another voice commenting on the same set of questions with limited evidence.

Recent insights for the emerging COVID-19: Drug discovery, therapeutic options and vaccine development

SARS-CoV-2 has been marked as a highly pathogenic coronavirus of COVID-19 disease into the human population, causing over 5.5 million confirmed cases worldwide. As COVID-19 has posed a global threat with significant human casualties and severe economic losses, there is a pressing demand to further understand the current situation and develop rational strategies to contain the drastic spread of the virus. Although there are no specific antiviral therapies that have proven effective in randomized clinical trials, currently, the rapid detection technology along with several promising therapeutics for COVID-19 have mitigated its drastic transmission. Besides, global institutions and corporations have commenced to parse out effective vaccines for the prevention of COVID-19. Herein, the present review will give exhaustive details of extensive researches concerning the drug discovery and therapeutic options for COVID-19 as well as some insightful discussions of the status of COVID-19.

Epidemiological Characteristics of Infectious Diseases Among Travelers Between China and Foreign Countries Before and During the Early Stage of the COVID-19 Pandemic

Background: International travel during the Coronavirus disease 2019 (COVID-19) pandemic carries a certain magnitude of infection risk both to travelers and their destination, which may be difficult to assess in the early stage. The characteristics of common infectious diseases of tourists may provide some clues to identify the high-risk travelers and protect susceptible population. Methods: From among 48,444 travelers screened at Shanghai Port, we analyzed 577 travelers with 590 infectious diseases for age, sex, disease type, and World Health Organization (WHO) regions. We used the Joinpoint Regression Program to identify the average percent changes (APC) in the various trends among these individuals. Results: Hepatitis B, syphilis, and HIV were the most common infectious diseases in travelers entering China, and Hepatitis B, pulmonary tuberculosis, and syphilis in Chinese nationals traveling abroad (overall detection rates, 1.43 and 0.74%, respectively; P < 0.05). Africa (2.96%), the Americas (1.68%), and the Western Pacific (1.62%) exhibited the highest detection rates. This trend did not decrease since the COVID-19 pandemic (P > 0.05) and rather showed an upward trend with increasing age [APC 95% CI = 5.46 (3.41,7.56)%, P < 0.05]. However, there were no evident trends in monthly infection rates of travelers exiting and entering China from different WHO regions (all P > 0.05). Conclusion: Travelers always carry a transmission risk of common infectious diseases. It may be reasonable to adjust strategies for airport screening and quarantine according to the age and departure area of travelers to prevent and control new infectious diseases.