Epidemic Outbreaks Research Articles

Objective and subjective evaluation of the use of protective clothing on the thermal strain and mental workload of nurses during the Covid-19 pandemic.

Nurses are among high-risk occupational groups during the outbreak of epidemics. Therefore, these people are required to use personal protective equipment (PPE). Previous studies have shown that the use of personal protective equipment can affect people's thermal strain and mental workload. The present study was conducted with the aim of objectively and subjectively evaluating the use of protective clothing on the thermal strain and mental workload of nurses during the covid-19 pandemic. This cross-sectional study was divided into two groups with a sample size of 300 people who were selected by systematic random method. In the first group, 120 people were objectively evaluated (use of protective clothing). In the second group, 180 people only completed the questionnaire of mental workload, feeling of heat and sweating and other information related to the type of clothing and the duration of its use, etc. Measurements were done in August and September of 2021 and in the morning shift of hospitals. Data were analyzed using SPSS version 21 software and chi-square, independent T-test and linear regression tests. The average age and average working hours per month of the participants in the study were 33.51±7.88 years, 187.91±35.37 hours, respectively. The results showed that the predicted average vote (PMV) index and the predicted percentage of dissatisfied (PPD), moisture felt on the skin surface, the feeling of desirability and pleasantness of the temperature, the weight of sweat and the humidity inside the clothes between the two groups of people using insulated clothes There is a significant difference between medicine and dressing (P < 0.05). There was a significant relationship between mental workload and type of clothing, average working hours per month. The use of medical isolation clothes causes a high heat load on nurses and can increase their mental workload. On the other hand, people using normal gowns suffer from a high mental workload due to the fear of disease due to the uncertainty of high-level protection. In addition, medical isolation clothes increase the humidity of the skin due to their impermeability, which causes skin complications.

Identifying influential factors using machine learning techniques on the intention to receive a COVID-19 booster dose and vaccine fatigue among partially vaccinated individuals

This study assesses COVID-19 booster intentions and hesitancy in Texas, a state known for its diversity and libertarian values. A survey was conducted with 274 participants residing in Texas between June and July 2022. The analysis examined sociodemographic and health-related factors, trusted information sources, and preventive behaviors. The survey focused on vaccinated participants and their intention to receive the booster dose, which was categorized into three outcomes: yes, no, and not sure. Machine learning techniques were employed to analyze the survey responses of vaccinated participants to identify the most critical factors. Among the participants, 113 expressed their intention to get the booster (41.2%), 107 did not plan to receive the booster (39.1%), and 54 remained undecided (19.7%). Our findings indicate that the perception of vaccine safety significantly influenced the decision to receive the booster dose. Those who reported trust in social media contacts as reliable information sources were more likely to intend to boost. Additionally, among those hospitalized when diagnosed with COVID-19, the largest proportion were unwilling to receive the booster (47.0%) compared to those who intended to receive the booster (33.3%). In contrast, most of those who believed they would be hospitalized if infected with COVID-19 intended to get the booster. Other factors did not demonstrate a significant association. Our findings are highly transferable and can offer valuable insights, particularly for countries where COVID-19 remains prevalent and are pivotal both presently and in the future for developing strategies to improve booster uptake and shape public health initiatives in epidemic and pandemic outbreaks.

Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.

Evolutionary dynamic of the coupled awareness-epidemic networks with higher-order structure

Mitigating epidemic spreading and outbreaks is crucial to reducing their significant impacts on people’s lives. Although research has begun to consider the impact of information networks on disease transmission in recent years, most studies have not fully considered the high-order characteristics in network structures. To better capture the dynamics of epidemic spreading and comprehensively understand and predict epidemic transmission patterns, a model of epidemic spreading in a coupled awareness-epidemic model, considering higher-order structures, is proposed. Extensive simulations on real and synthetic structured networks show that information networks influence the spread on physical contact networks by altering individual behaviors, leading to a transition from bistability to a single stable state induced by higher-order structures. Through theoretical analysis, it is proved that the corresponding conclusion is that as the information influence value (an indicator that measures the impact of information networks on physical contact networks) decreases, the impact of higher-order structures on the system’s dynamic behavior gradually weakens. This leads to the disappearance of the bistable phenomenon originally caused by the presence of higher-order structures, and the system transitions from a bistable state to a single stable state. This transition is accompanied by an increase in the threshold for epidemic outbreaks and a decrease in the stable infection density. This work contributes to a deeper understanding of the influence of information on epidemic dynamics, providing important theoretical guidance for the future development of more effective prevention and control strategies.

Dynamics of delay epidemic model with periodic transmission rate

We introduce novel epidemic models with single and two strains described by systems of delay differential equations with a periodic time-dependent disease transmission rate, and based on the number of newly infected individuals. Transitions between infected, recovered, and returning to susceptible compartments due to waning immunity are determined by the accompanying time delays. Positiveness, existence and uniqueness of solutions are demonstrated with the help of fixed point theory. Reducing delay differential equations to integral equations facilitates determining the analytical estimation of the equilibrium solutions. When there are two strains, they compete together, and the strain with a larger basic reproduction number dominates in the population. However, both strains coexist, and the magnitudes of epidemic outbreaks are governed by the basic reproduction numbers. The outlines of this work are verified through comparison with seasonal influenza data.

Machine learning technology for good health and well-being for sustainable human security in South Africa

The failure to achieve sustainable development in South Africa is due to the inability to deliver quality and adequate health services that would lead to the achievement of sustainable human security. As we live in an era of digital technology, Machine Learning (ML) has not yet permeated the healthcare sector in South Africa. Its effects on promoting quality health services for sustainable human security have not attracted much academic attention in South Africa and across the African continent. Hospitals still face numerous challenges that have hindered achieving adequate health services. For this reason, the healthcare sector in South Africa continues to suffer from numerous challenges, including inadequate finances, poor governance, long waiting times, shortages of medical staff, and poor medical record keeping. These challenges have affected health services provision and thus pose threats to the achievement of sustainable security. The paper found that ML technology enables adequate health services that alleviate disease burden and thus lead to sustainable human security. It speeds up medical treatment, enabling medical workers to deliver health services accurately and reducing the financial cost of medical treatments. ML assists in the prevention of pandemic outbreaks and as well as monitoring their potential epidemic outbreaks. It protects and keeps medical records and makes them readily available when patients visit any hospital. The paper used a qualitative research design that used an exploratory approach to collect and analyse data.

GRIPE AVIÁRIA: CONSCIENTIZANDO A POPULAÇÃO.

Avian influenza outbreaks have already hit several countries and contact between domestic and migratory birds has been the source of many epidemic outbreaks, with high mortality rates in humans and animals. There is great concern about the spread to continents not yet affected, especially via migratory birds. Transmission occurs mainly through direct contact with infected birds, through saliva, feces, respiratory secretions and aerosols. Awareness-raising is therefore essential as a prophylactic measure. Therefore, the methodology of the work developed was a survey to analyze the population's knowledge of the disease. Based on the results, an informative video was produced and published. In this way, by being clear about the forms of transmission, the spread of avian influenza can be prevented and avoided.

Vicarious Acquisition of Learned Helplessness During the COVID-19 Campus Epidemics in China: Interactions Between Embodied and Mediated Experiences

ABSTRACT Numerous universities nationwide announced urgent implementation of closed campus management due to the epidemics of COVID-19 in Chinese universities since March 2022, and a large number of students were under quarantine. This study aims to explore how embodied experiences (i.e., centralized quarantine and self-quarantine) and mediated experiences (i.e., exposure to media sources, pandemic information overload, and online help seeking) influence the acquisition of learned helplessness during the campus epidemics among the Chinese university students, besides, how do these two types of experiences interact with each other to elicit learned helplessness is another interested research question. The data was collected nationwide via an online survey platform from March 22nd to April 16 2022 immediately after the outbreak of campus epidemics. A total of 1267 valid student samples were retained. Hierarchical regression analysis showed that embodied experience of centralized quarantine is positively related to learned helplessness. Mediated experience of information access from friends on social media and government official media as well as online help seeking are negatively related to learned helplessness. Information access from overseas media and pandemic information overload are positively related to learned helplessness. Besides, the interaction between self-quarantine and pandemic information overload is positively related to learned helplessness. Theoretical contributions and intervention strategies aimed at enhancing authoritative communication and managing information overload during public health crisis were discussed.

Wastewater-Based Epidemiology of Influenza Viruses: a systematic review

Abstract Background Influenza viruses (IVs) are responsible for annual epidemic outbreaks during the winter months. Beyond conventional surveillance systems, the circulation of these pathogens can be monitored using Wastewater-Based Epidemiology (WBE), an epidemiological tool that describes the health status of an entire community by analyzing the presence of pathogens within wastewater. Systematically reviewing existing experiences in this emerging field is crucial to highlight the best practices to establish a WBE surveillance system providing useful information for public health actions. Methods The study protocol for this systematic review has been registered on the PROSPERO website (registration ID: CRD42024532435). Three academic databases (PubMed, Web of Science and Scopus) were accessed to collect studies examining the presence of IVs in wastewater. Studies included were those reporting quantitative measures of viral concentration or detailing molecular/phylogenetic characterization of IVs in wastewater. Results Databases were searched on July 8th, 2024. After de-duplication and screening of the 400 remaining records, 42 were included in the review. IVs detection in wastewater was reported in all papers except from one, although with high variability in terms of positivity titers and concentration levels. More than half of the collected papers (52.4%) evaluated association between environmental viral concentration and clinical data, generally showing concordance between the two aspects. Moreover, 28.6% of papers described subtyping of IVs in wastewater through different methods (specific PCR assays being the most used). Conclusions WBE is a promising approach for surveilling viral circulation, and IVs represent a high value target for implementing this innovative system. Systematically reviewing previous experiences can be useful to obtain information for the establishment of a novel environmental surveillance system of Influenza Viruses Key messages • Nucleic acids belonging to Influenza Virus are detectable in wastewater, even though we report high levels of variability in sampling techniques, laboratory methods and data management systems. • Association between environmental data and number of clinical cases is reported to be generally strong in collected papers; subtyping of IVs present in wastewater is also feasible.

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation

This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints.

Sterilization mechanism of CuCeOx on fungus: Oxidative damage and energy metabolism disequilibrium

Pathogens in bioaerosols and aquatic environment cause epidemic outbreak frequently. Relative to bacteria and virus, fungi has not received attention enough. The development of sterilization materials and mechanism on fungi are limited. In this study, we proposed a non-noble metal sterilization material CuCeOx in order to investigate its sterilization mechanism on target fungal Hanseniaspora uvarum collected from the air. The active components of CuCeOx were uniformly distributed and induced the production of R·. The reactive oxygen species (ROS) changed from R· to RO· upon contact with fungal cells. Transcriptomics identified 1665 Differentially Expressed Genes (DEGs), including 1540 up-regulated and 125 down-regulated genes. The cell wall and external encapsulation structure were firstly damaged when the fungus contacted with CuCeOx, and the related DEGs were significantly down-regulated. CuCeOx could exert great impact on the energy metabolism of the cell, especially on the process of oxidative phosphorylation, which contained 59 up-regulated and 2 down-regulated genes. The cell is not sufficient to cope with the adverse environment caused by oxidative stress and metal ion homeostatic imbalance, which then modulates the intrinsic apoptotic pathway triggering apoptosis.

Ferroptotic cyanobacteria as biocontrol agent of the southern house mosquito Culex quinquefasciatus

Culex quinquefasciatus is a hematophagous mosquito, widely distributed around the world, that plays a crucial role in public and veterinary health. As an efficient vector of etiological agents, it exhibits a marked preference for urban environments and human blood. Despite advances in mosquito-borne disease control, managing mosquito populations remains an economically efficient and safe strategy to reduce the impact of epidemic outbreaks. However, achieving this goal requires ecologically acceptable tools that ensure sustainability and minimize adverse environmental impacts. In the present work, we investigated the effect of a non-toxigenic model cyanobacterium on Cx. quinquefasciatus larvae through regulated cell death. We observed that heat stress treatment of Synechocystis PCC 6803 inducing ferroptosis, results in larval lipid oxidation, leading to their death. This effect can be mitigated by rearing larvae in an environment containing canonical inhibitors of ferroptosis, such as ferrostatin 1, or antioxidants, like glutathione and ascorbic acid. Furthermore, larval cell death induced by ferroptotic cyanobacteria is closely linked to oxidative dysregulation and lipid peroxidation, both hallmarks of ferroptosis. Moreover, while ferroptotic Synechocystis significantly affects larval development, it does not influence oviposition site selection by gravid females.

Epidemic, factor misallocation and efficiency of digital enterprises: heterogeneity study of labour and capital

ABSTRACT To improve the development quality of digital economy, the factor misallocation shall be reduced effectively in the post-epidemic era. This study discussed the impact mechanism of how the labour and capital misallocation affect heterogeneously the epidemic’s effect on the efficiency of digital enterprises. The epidemic outbreak was considered a quasi-natural experiment to set a PSM-DID model and an intermediary effect model. Empirical research was carried out using the data of 1752 digital and non-digital high-tech enterprises from the fourth quarter of 2017 to the first quarter of 2022. The results show that: (1) The epidemic improved digital enterprises’ efficiency, mainly from the positive effect on technological efficiency and scale efficiency. (2) The epidemic worsened the labour misallocation but improved capital allocation. (3) Through the factor misallocation, the epidemic improved the efficiency of digital enterprises, contributing to the worsening technical progress. Through the capital misallocation, the epidemic improved the TFP of the digital enterprises but there is no significant evidence of its source. While through the labour misallocation, the epidemic improved the digital enterprises’ TFP because of its positive effect on the technological efficiency and pure efficiency.

Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models.

Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectory degeneracy, as different sets of epidemiological parameters yield comparable predictions at early stages of the outbreak but disparate future epidemic scenarios. In this study, we use the Doi-Peliti approach and extend the classical deterministic compartmental models to a quantum-like formalism to explore whether the uncertainty of epidemic forecasts is also shaped by the stochastic nature of epidemic processes. This approach allows us to obtain a probabilistic ensemble of trajectories, revealing that epidemic uncertainty is not uniform across time, being maximal around the epidemic peak and vanishing at both early and very late stages of the outbreak. Therefore, our results show that, independently of the models' complexity, the stochasticity of contagion and recovery processes poses a natural constraint for the uncertainty of epidemic forecasts.

Coexistence and food sources of adult mosquitoes (Diptera: Culicidae) in a rural health center in Piura, Peru 2024.

Motivation for the study. Rural health facilities could be potential foci of transmission and scenarios of zoonosis during epidemic outbreaks of dengue and other arbovirosis due to the coexistence of mosquito species that feed on different vertebrates. Main findings. Aedes aegypti feeds on Homo sapiens sapiens. Culex quinquefasciatus feeds on Homo sapiens sapiens and Canis familiaris. Both coexist in health care areas of the Querecotillo health center. Implications. Molecular techniques should be integrated into vector control to understand feeding patterns in natural conditions and information on probable reservoirs. This study aimed to determine the coexistence and food sources of adult mosquitoes (Diptera: Culicidae) in a rural health center in Piura, Peru by using a descriptive cross-sectional design. Entomological techniques were used to capture and identify mosquitoes, and molecular biotechnology techniques were used to identify food sources. A total of 793 specimens of the Culex and Aedes genera were found coexisting, 789 (99.5%) were Culex quinquefasciatus, 607 (76.9%) were males and 182 (23.1%) were females. Likewise, 4 (100%) corresponded to Aedes aegypti females. The food sources of Aedes aegypti were Homo sapiens sapiens, and Homo sapiens sapiens and Canis familiaris were the food sources of Culex quinquefasciatus. This study provides evidence that rural health centers could be acting as foci of arbovirosis, with the risk that people who come for different ailments could contract diseases transmitted by C. quinquefasciatus and A. aegypti.

Development, characterization and evaluation of in vitro safety and in vivo efficacy of repellent gel formulations

Contagious diseases caused by mosquito bites are responsible for epidemic outbreaks around the world. Applying mosquito repellents is the main and most cost-effective prophylactic measure to reduce the transmission risk of arboviruses. In this study, two topical gel Formulations were developed as mosquito repellents which use two different systems to release 20 % Icaridin. The first, Formulation 1b uses Pluronic F127®, and the second, Formulation 2b uses a biopolymer produced by bacteria from the Rhizobium tropici species. The repellent Formulations developed showed non-Newtonian behavior. For Formulation 1b the viscosity increased after adding Icaridin. However, for Formulation 2b, there was no increase in viscosity after adding the active substance. The thixotropy analysis showed that the Formulations have good recovery of their structure and adhesion. Adding of Icaridin led to an increase in gel strength in Formulation 1b and did not change the gel strength for Formulation 2b. The Formulations presented very similar occlusion capacities which were all higher than free Icaridin. The Formulations were not cytotoxic in the concentration range between 0.04 % and 0.3 %. In addition, at the 0.62 % concentration, they showed a slight reduction in cell viability, although the LD50 value was not reached. The mean protection time was longer for Formulation 2b compared to Formulation 1b. Both Formulations had a longer mean protection time than free Icaridin against Aedes aegypti. The combination of Icaridin with Pluronic F127® and the biopolymer is an innovation in the pharmaceutical area.

Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been performed regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844-264,075) and 24,546 deaths (CI 95% 2548-46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. In Spain, patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination. Our use of machine learning models provided a detailed estimation of the averted burden of the pandemic, demonstrating the effectiveness of vaccination at a population-wide level.

Designing a robust logistics model for perishable emergency commodities in an epidemic outbreak using Lagrangian relaxation: a case of COVID-19

Designing a robust logistics model for perishable emergency commodities in an epidemic outbreak using Lagrangian relaxation: a case of COVID-19

Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison

Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.

Identification of adenosine analogues as nsp14 N7‑methyltransferase inhibitors for treating coronaviruses infection

Coronaviruses are RNA viruses that have coevolved with humans and animals over time, exhibiting high mutation rates and mortality rates upon epidemic outbreaks. The nonstructural protein (nsp14) is crucial for various coronaviruses processes, including genome replication, protein translation, virus particle assembly, and evasion of host immunity via RNA methylation modification. In this study, a series of adenosine analogs were designed, synthesized, and evaluated for their inhibitory activities. Among them, MTI013 exhibited the strongest nsp14 MTase inhibition and antiviral activity, with an IC50 of 10.33 μM in HCoV-229E-infected Huh7 cells, along with low cytotoxicity. When combined with the RdRp inhibitor ATV014, MTI013 showed a synergistic antiviral effect, indicating its potential both as a standalone therapy and in combination treatments. Furthermore, MTI013 displayed high selectivity against the SARS-CoV-2 nsp10-nsp16 complex and five human methyltransferases. These results offer valuable structural insights for future exploration of nsp14 as a drug target for SARS-CoV-2 and other coronaviruses.