Epidemic Outbreaks Research Articles

Factors Determining Adherence to Hand Antisepsis by Healthcare Workers during Pandemic Infection Spread (as exemplified by COVID­-19)

Relevance. The healthcare personnel’s adherence to hand antisepsis is а pressing problem during any epidemic outbreak. In the context of high biological hazards, the possibility of performing proper antiseptic procedures is reduced while nosocomial pathogen transmission and healthcare-associated infection (HAI) rate growth are promoted. Aims. Studying the factors determining the healthcare workers’ adherence to hand antisepsis during a pandemic infection spread (as exemplified by COVID-19). Materials and methods. The study was conducted during the entire COVID-19 pandemic. To estimate the rate of adherence rate by the healthcare personnel to hand hygiene, we conducted an anonymous questionnaire survey of the staff in infectious disease hospitals (2021–2022) as well as assessed the viral and bacterial contamination rates of the personnel’s hands and the hospital environment items (2021–2023). Epidemiological (descriptive-evaluation), bacteriological (conventional culture, AMR determination using a VITEK2 analyzer), molecular biological (RT-PCR, Sanger sequencing) and statistical (a questionnaire survey, building machine learning models) methods were used in the study. Results. Based on the results of the survey, the main factors influencing the formation of attitudes towards antiseptics among the staff of medical organizations with medical and non-medical education were identified. It was found that the pandemic spread of infections negatively affects the level of contamination of the hands of infectious diseases hospital staff, which amounted to 35.3% of doctors, 28.8% of nurses, and 25% of hospital cleaners. The spectrum of isolated pathogens included viruses (SARS-CoV-2) and opportunistic microorganisms (Enterococcus faecalis, Enterococcus faecium, Pantoea agglomerans, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus aureus, Escherichia coli), including those with a wide profile of antimicrobial resistance. Conclusion. During the COVID-19 epidemic, the rate of adherence to hand antisepsis in the infectious disease hospital was 82.3% in the staff with a medical background and 71.2% in the staff without one. The results confirm the significant role of staff hands in the in-hospital transmission of dangerous pathogens and indicate the need for systematic monitoring of the level of knowledge on antiseptics among all employees of a medical organization, regardless of specialization and level of education.

An affordable detection system based on RT-LAMP and DNA-nanoprobes for avian metapneumovirus

Airborne animal viral pathogens can rapidly spread and become a global threat, resulting in substantial socioeconomic and health consequences. To prevent and control potential epidemic outbreaks, accurate, fast, and affordable point-of-care (POC) tests are essential. As a proof-of-concept, we have developed a molecular system based on the loop-mediated isothermal amplification (LAMP) technique for avian metapneumovirus (aMPV) detection, an airborne communicable agent mainly infecting turkeys and chickens. For this purpose, a colorimetric system was obtained by coupling the LAMP technique with specific DNA-functionalized AuNPs (gold nanoparticles). The system was validated using 50 different samples (pharyngeal swabs and tracheal tissue) collected from aMPV-infected and non-infected chickens and turkeys. Viral detection can be achieved in about 60 min with the naked eye, with 100% specificity and 87.88% sensitivity for aMPV. In summary, this novel molecular detection system allows suitable virus testing in the field, with accuracy and limit of detection (LOD) values highly close to qRT-PCR-based diagnosis. Furthermore, this system can be easily scalable to a platform for the detection of other viruses, addressing the current gap in the availability of POC tests for viral detection in poultry farming.Key points•aMPV diagnosis using RT-LAMP is achieved with high sensitivity and specificity.•Fifty field samples have been visualized using DNA-nanoprobe validation.•The developed system is a reliable, fast, and cost-effective option for POCT.Graphical

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

The coupled dynamics of information-behavior-epidemic propagation considering the heterogeneity of adoption thresholds and network structures in multiplex networks

During epidemics, individual responses to prevention and control information vary significantly, affecting the execution of preventive measures. This paper introduces a novel model to analyze the co-evolution of the information-behavior-epidemic dynamic, accounting for the heterogeneity in individual behavioral adoption thresholds and network structures. The model consists of three layers: the upper layer, utilizing scale-free social networks to represent the diffusion of information; the middle and lower layers, employing community networks, to illustrate individuals’ decision-making and epidemic transmission processes, respectively. We applied the Heaviside step function to depict the effects of information and local environmental factors on individuals’ decisions. Through the microscopic Markov chain approach (MMCA), we determined the epidemic threshold. Our methodology also included extensive Monte Carlo (MC) simulations to validate our theoretical predictions. Our results reveal several key insights: (i) reducing community mobility and the threshold for adopting preventive behaviors can significantly restrain the scale of the epidemic and delay outbreaks; (ii) the level of individual activity in the physical contact network has a significant influence on epidemic transmission; (iii) integrating individuals’ behavior adoption thresholds induces a two-stage phase change, deviating from the continuous phase transition typically observed in epidemic transmission.

Assessing the reactions of tourist markets to reinstated travel restrictions in the destination during the post-COVID-19 phase

This study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises.

Exploring threshold dynamics of a behavioral epidemic model featuring two susceptible classes and second-order jump–diffusion

Understanding and modeling behavioral changes during epidemic outbreaks is crucial for devising effective public health interventions. In this paper, we introduce an enhanced behavioral epidemic model that integrates novel elements to better capture real-world phenomena. Our model incorporates logistic growth as a recruitment rate, reflecting the population’s intrinsic growth constraints. Additionally, we consider two incidence rates to account for varying transmission dynamics. To extend the model’s applicability, we introduce perturbations through second-order jump–diffusion processes, allowing for abrupt changes in population dynamics. We define a threshold parameter based on the properties of an auxiliary system, which distinguishes between the existence of a unique stationary distribution and stochastic extinction. Through numerical simulations, we validate our theoretical findings and demonstrate the impact of nonlinear noise on the model dynamical behavior.

The Emergence of Chemical Medicine in Early Modern Naples (1600–1660)

Despite the increasing interest in Italian medicine, comparatively little attention has been paid to the establishment of iatrochemistry. Though this process spread throughout the Peninsula, Naples witnessed an impressive growth of chemical research and the outbreak of a conflict between the medical establishment and the chemical physicians. The purpose of this article is to explore the emergence of chemical medicine in Naples in the period that precedes the founding (1663) of the Accademia degli Investiganti. In the first part of the seventeenth century, chemistry achieved recognition in settings like academies, pharmacies, hospitals, and monasteries. Chemical studies and the making of new remedies were spurred by the scientific exchange that Neapolitan savants established with scholars from different areas. The so-called medical pluralism and the recurrent outbreaks of epidemics stimulated the introduction of new chemical therapies, which coexisted with old ones. The establishment of chemical medicine was triggered by Marco Aurelio Severino (1580–1656), who, besides promoting chemical remedies, resorted to chemical theories, including Paracelsian ones, to account for physiological processes. Severino was the mentor of the chemical physicians who gave rise to the Accademia degli Investiganti. One of Severino’s disciples was Giuseppe Donzelli (1596–1670), who fostered chemical remedies in Naples.

Optimizing information-driven awareness allocation for controlling activity-triggered epidemic spread

In the contemporary era, the advent of epidemics instigates a substantial upswing in relevant information dissemination, bolstering individuals’ resistance to infection by concurrently reducing activity contacts and reinforcing personal protective measures. To elucidate this intricate dynamics, we introduce a composite four-layer network model designed to capture the interplay among information-driven awareness, human activity, and epidemic spread, with a focus on the allocation of individuals’ limited attention in diminishing activity frequency and self-infection rates. One intriguing observation from our findings is an anomalous, concave non-monotonic relationship between awareness trade-off and epidemic spread, with a more pronounced prevalence at an intermediate least awareness efficacy. This underscores the inadvisability of relaxing self-protection through reduced activity frequency or compensating for increased activity frequency by enhancing self-protection. Especially noteworthy is the significance of enhancing self-protection in response to heightened information dissemination and inherent activity demands to curtail infection risk. However, in scenarios with increasing ancillary activity frequency, the emphasis should exclusively shift towards reducing activity exposure. The model establishes a theoretical threshold for accurately predicting awareness efficacy in epidemic outbreaks. Optimal awareness allocation consistently resides at the extremes—either completely avoiding unnecessary activity contact or adopting full self-protection. This guidance, contingent on information level and activity demand, offers valuable insights into the delicate balance between individual behaviors and epidemic prevention.

Seasonality Prediction of Meningitis Using Artificial Neural Network (ANN)

Communities are concerned about controlling, preventing, and handling infectious diseases due to recent epidemic outbreaks. Meningitis, an inflammation of the membranes surrounding the brain and spinal cord, is a significant risk in Nigeria. It can cause death within hours of infection, with an average case fatality rate of 10%. To prevent meningitis outbreaks, this paper focuses on using an Artificial Neural Network (ANN) to predict outbreaks based on climatological factors. Previous research has shown that climate plays a major role in these outbreaks. The study found that the Levenberg-Marquaralt ANN algorithm was the best model, with the lowest prediction error and fewer iterations. High temperature and low humidity were identified as major triggers for meningitis outbreaks. It is crucial to address these factors to prevent future outbreaks and protect communities.

Anthrax Epidemıc Management in an Eastern Turkey Region

Background: Anthrax is a zoonotic disease caused by Bacillus anthracis. It has been known for centuries and remains hyperendemic in certain parts of the world, with occasional epidemic outbreaks. This study aimed to investigate an anthrax outbreak affecting a family and their close contacts in the Akçakiraz town of Elazığ province in eastern Turkey. Methods: Twenty-two individuals who had contact with a sick animal in Akçakiraz were included in the study. Weekly anamnesis, physical examinations, and follow-ups were conducted over three weeks. Swab samples were collected from the wound sites of hospitalized patients for culture and Gram-staining. Blood and wound samples were sent for polymerase chain reaction (PCR) analysis to detect B. anthracis. A sample was taken from the liver tissue of the infected animal. Statistical analysis was performed using SPSS 25. Results: Of the 22 individuals, 9 (40.9%) were hospitalized and treated, while 13 (59.1%) were monitored on an outpatient basis. B. anthracis spores were identified in Gram-stained samples from the liver of diseased animal. PCR positivity for B. anthracis was detected in the wound samples of two patients (22.2%), although the PCR results from the blood samples of these patients were negative. No bacterial growth was observed in wound cultures, and gram-staining did not reveal any notable features. All patients were successfully treated. Contacts, family members, and residents were informed and educated about the disease, and necessary public health measures were implemented through coordination with the Provincial Hygiene Board. Conclusion: Increasing awareness of zoonotic diseases, especially anthrax, is essential. It is crucial to implement societal prevention measures, including controlling animal production, educating at-risk populations in endemic areas, vaccinating animals, preventing contact with sick or deceased animals, and preventing environmental contamination by decontaminating affected areas.

Multiple endemic equilibria in an environmentally-transmitted disease with three disease stages

We construct, analyze and interpret a mathematical model for an environmental transmitted disease characterized for the existence of three disease stages: acute, severe and asymptomatic. Besides, we consider that severe and asymptomatic cases may present relapse between them. Transmission dynamics driven by the contact rates only occurs when a parameter R∗>1, as normally occur in directly-transmitted or vector-transmitted diseases, but it will not adequately correspond to a basic reproductive number as it depends on environmental parameters. In this case, the forward transcritical bifurcation that exists for R∗<1, becomes a backward bifurcation, producing multiple steady-states, a hysteresis effect and dependence on initial conditions. A threshold parameter for an epidemic outbreak, independent of R∗ is only the ratio of the external contamination inflow shedding rate to the environmental clearance rate. R∗ describes the strength of the transmission to infectious classes other than the I-(acute) type infections. The epidemic outbreak conditions and the structure of R∗ appearing in this model are both responsible for the existence of endemic states.

Fully Integrated and High-Throughput Microfluidic System for Multiplexed Point-Of-Care Testing.

For every epidemic outbreak, the prevention and treatments in resource-limited areas are always out of reach. Critical to this is that high accuracy, stability, and more comprehensive analytical techniques always rely on expensive and bulky instruments and large laboratories. Here, a fully integrated and high-throughput microfluidic system is proposed for ultra-multiple point-of-care immunoassay, termed Dac system. Specifically, the Dac system only requires a handheld portable device to automatically recycle repetitive multi-step reactions including on-demand liquid releasing, dispensing, metering, collecting, oscillatory mixing, and discharging. The Dac system performs high-precision enzyme-linked immunosorbentassays for up to 17 samples or targets simultaneously on a single chip. Furthermore, reagent consumption is only 2% compared to conventional ELISA, and microbubble-accelerated reactions shorten the assay time by more than half. As a proof of concept, the multiplexed detections are achieved by detecting at least four infection targets for two samples simultaneously on a singular chip. Furthermore, the barcode-based multi-target results can rapidly distinguish between five similar cases, allowing for accurate therapeutic interventions. Compared to bulky clinical instruments, the accuracy of clinical inflammation classification is 92.38% (n = 105), with a quantitative correlation coefficient of R2 = 0.9838, while the clinical specificity is 100% and the sensitivity is 98.93%.

The impact of social interventions on COVID-19 spreading based on multilayer commuter networks

From March to June 2022, Shanghai was struck by a new coronavirus variant, Omicron, resulting in the infected cases of at least 600,000 people. Despite implementing a strict containment policy of city-wide silence (i.e., residents were not allowed to go out unless necessary), the outbreak cannot be effectively prevented within a short period of time. A significant academic and practical question is: how could we prevent and control outbreak of COVID-19 in large, densely populated cities like Shanghai? It is necessary to develop a rational epidemic spreading model for large cities, in order to accurately predict the trend of disease and quantitatively assess the impact of non-pharmaceutical interventions. In this paper, a multilayer commuter metapopulation network model is constructed to capture commuting flows and the size of epidemic outbreak during commuting between districts. The model accurately predicts epidemic spreading in each district of Shanghai. Assuming strict city-wide lockdowns, with each district locked down and limited inter-district commuting as social zones, simulations demonstrate significant suppression of outbreaks due to social-level interventions. For example, a 1-fold increase in PCR (Polymerase Chain Reaction) testing efficiency reduces the size of epidemic outbreak by approximately 70%. Larger districts require stricter controls to prevent exponential growth. Lockdowns effectively prevent epidemic outbreak at low disease rates but less so at high rates. Liberalized policies lead to varied outbreak trends, with economically developed regions peaking earlier due to higher population densities. This study provides a comprehensive framework for quantitatively evaluating the impact of social and regional controls on urban epidemics.

An innovative fractional-order evolutionary game theoretical study of personal protection, quarantine, and isolation policies for combating epidemic diseases

This study uses imposed control techniques and vaccination game theory to study disease dynamics with transitory or diminishing immunity. Our model uses the ABC fractional-order derivative mechanism to show the effect of non-pharmaceutical interventions such as personal protection or awareness, quarantine, and isolation to simulate the essential control strategies against an infectious disease spread in an infinite and uniformly distributed population. A comprehensive evolutionary game theory study quantified the significant influence of people’s vaccination choices, with government forces participating in vaccination programs to improve obligatory control measures to reduce epidemic spread. This model uses the intervention options described above as a control strategy to reduce disease prevalence in human societies. Again, our simulated results show that a combined control strategy works exquisitely when the disease spreads even faster. A sluggish dissemination rate slows an epidemic outbreak, but modest control techniques can reestablish a disease-free equilibrium. Preventive vaccination regulates the border between the three phases, while personal protection, quarantine, and isolation methods reduce disease transmission in existing places. Thus, successfully combining these three intervention measures reduces epidemic or pandemic size, as represented by line graphs and 3D surface diagrams. For the first time, we use a fractional-order derivate to display the phase-portrayed trajectory graph to show the model’s dynamics if immunity wanes at a specific pace, considering various vaccination cost and effectiveness settings.

Understanding the role of energy sector in the outbreak of epidemic in China: An analysis based on SEIDR epidemic model, dynamic input-output model and resource allocation DEA model

The global epidemic has triggered severe health crises and economic downturns, while energy sector plays an important role in the complicated sector interdependence. In such context, to gain a deeper understanding of the energy sector's role during epidemic outbreaks, this study develops a new methodological framework. The framework first combines SEIDR epidemic model with dynamic input-output model and resource allocation DEA model, linking epidemiological and socioeconomic aspects. Empirically, this study focuses on China's economy by constructing an energy-manufacturing-service system. Our main conclusions are summarized as follows. First, the epidemic generates substantial effects on energy sector, while the size of effects is sensitive to the settings of some key parameters. Second, the energy sector suffers the most significant decline in total output and capital due to the epidemic, particularly in Shandong and Gansu. Conversely, Beijing and Chongqing have shown the least changes, indicating a more resilient performance in comparison to other provinces or province-equivalents. Finally, a well-designed resource allocation strategy can reduce inefficiency, and then reduce the economic losses possibly caused by the epidemic. The output of energy sector has reduced by 29.5%, indicating a need for targeted interventions and strategies to boost its performance.

Predicting the stock opening price of Apple company

As the stock market plays a crucial role in the world economy, researchers have used multiple mathematical and statistical models such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks model to forecast the fluctuation in stock price despite their unpredictability as the stock market, being a stochastic process, would be easily affected by an abundance of factors such as governmental policies, industrial news, and natural calamities. Therefore, based on the previous studies, this paper attempts to forecast the stock opening price of Apple Inc., one of the world-leading companies in the technology industry, utilizing the Autoregressive Integrated Moving Average (ARIMA) model. In order to minimize the impact on the stock market brought by the COVID-19 pandemic, this paper will analyze separately the opening price of Apple stock before and after the epidemic outbreak and will compare the difference the pandemic made in the stock market, as well as the forecasting models.

The Preventive Indicators for Evaluating the Design of Existing Buildings as Epidemic-Resilient Architecture: A Theoretical Framework

The recent outbreak of epidemics created health challenges and emerging requirements that revealed weaknesses and increased alerts about the contribution of the existing buildings’ design to increasing the possibility of epidemics’ spread. Those risks necessitated focusing on designing more effectively resilient buildings to epidemics. The study focuses on improving the safety aspects of existing building design from an architectural design perspective. Therefore, this study proposes a theoretical framework for a set of preventive indicators to evaluate the existing building design as a resilient architectural system in its response to epidemics. These indicators have been identified and selected based on an extensive examination of the literature and the most effective practices for preventing and controlling epidemics. Those preventive indicators covered all the various aspects of the design of the existing building, including the effectiveness of social distancing, indoor air quality, indoor environmental quality, control engineering and preventive. Using these indicators, architecture professionals and policymakers can evaluate the effectiveness of existing buildings in reducing the spread of epidemics and making the necessary improvements to create a more resilient environment. The proposed preventive indicators aim to contribute to developing epidemic-resilient architecture and promote the creation of healthier and safer living environments for occupants.

Social media sensors as early signals of influenza outbreaks at scale

Detecting early signals of an outbreak in a viral process is challenging due to its exponential nature, yet crucial given the benefits to public health it can provide. If available, the network structure where infection happens can provide rich information about the very early stages of viral outbreaks. For example, more central nodes have been used as social network sensors in biological or informational diffusion processes to detect early contagious outbreaks. We aim to combine both approaches to detect early signals of a biological viral process (influenza-like illness, ILI), using its informational epidemic coverage in public social media. We use a large social media dataset covering three years in a country. We demonstrate that it is possible to use highly central users on social media, more precisely high out-degree users from Twitter, as sensors to detect the early signals of ILI outbreaks in the physical world without monitoring the whole population. We also investigate other behavioral and content features that distinguish those early sensors in social media beyond centrality. While high centrality on Twitter is the most distinctive feature of sensors, they are more likely to talk about local news, language, politics, or government than the rest of the users. Our new approach could detect a better and smaller set of social sensors for epidemic outbreaks and is more operationally efficient and privacy respectful than previous ones, not requiring the collection of vast amounts of data.

Insurance design for the loss of epidemic outbreaks involving Cramer-Lundberg model

Insurance design for the loss of epidemic outbreaks involving Cramer-Lundberg model

Novel stochastic descriptors of a Markovian SIRD model for the assessment of the severity behind epidemic outbreaks

In contemporary epidemiology, it is of paramount importance to effectively evaluate epidemic occurrences by employing novel approaches that yield dependable predictions for the progression of widespread outbreaks. The present paper focuses on proposing novel stochastic descriptors based on a Markovian SIRD (susceptible-infected-recovered-deceased) epidemiological scheme that accounts for multiple re-infections. A continuous-time, three-dimensional Markovian process is employed tailored to the characteristics of the presented SIRD model. Our study examines several new time- and especially size-related characteristics, including the final size of infections and deaths, the maximum number of concurrent infectious cases, and the alarm time associated with escalating numbers of infections or deaths. We present theorems and algorithms for the determination of the distributions and moments of key statistical measures. Additionally, we share further insights on effective computational techniques designed to enhance the efficiency of employing the suggested algorithms. Moreover, numerous illustrative instances, coupled with a thorough sensitivity analysis, demonstrate how system parameters affect the severity of epidemics. This not only bolsters the credibility of the outcomes, but also underscores the significance of the suggested stochastic descriptors when assessing real-life epidemics. At the same time, we examine the applicability of the proposed methodology to real Marburg fever data in Angola. Unlike other approaches that mainly concentrate on using mathematical models to fit the evolution of a disease, our goal is to complement these studies by emphasizing significant aspects that play an important role in assessing the seriousness of epidemics. The introduced epidemiological quantities can aid public health authorities in making well-informed choices, potentially leading to a substantial decrease in both mortality rates and socio-economic repercussions. MSC62G30, 60G10, 62P10, 62G32