Epidemic System Research Articles

Epidemic and Pandemic Preparedness and Response in a Multi-Hazard Context: COVID-19 Pandemic as a Point of Reference.

Infectious diseases manifesting in the form of epidemics or pandemics do not only cause devastating impacts on public health systems but also disrupt the functioning of the socio-economic structure. Further, risks associated with pandemics and epidemics become exacerbated with coincident compound hazards. This study aims to develop a framework that captures key elements and components of epidemic and pandemic preparedness and response systems, focusing on a multi-hazard context. A systematic literature review was used to collect data through peer-reviewed journal articles using three electronic databases, and 17 experts were involved in the validation. Epidemiological surveillance and early detection, risk and vulnerability assessments, preparedness, prediction and decision making, alerts and early warning, preventive strategies, control and mitigation, response, and elimination were identified as key elements associated with epidemic and pandemic preparedness and response systems in a multi-hazard context. All elements appear integrated within three interventional phases: upstream, interface, and downstream. A holistic approach focusing on all interventional phases is required for preparedness and response to pandemics and epidemics to counter their cascading and systemic effects. Further, a paradigm shift in the preparedness for multi-hazards during an epidemic or pandemic is essential due to the multiple challenges posed by concurrent hazards.

Resilience of weighted networks with dynamical behavior against multi-node removal.

In many real-world networks, interactions between nodes are weighted to reflect their strength, such as predator-prey interactions in the ecological network and passenger numbers in airline networks. These weighted networks are prone to cascading effects caused by minor perturbations, which can lead to catastrophic outcomes. This vulnerability highlights the importance of studying weighted network resilience to prevent system collapses. However, due to many variables and weight parameters coupled together, predicting the behavior of such a system governed by a multi-dimensional rate equation is challenging. To address this, we propose a dimension reduction technique that simplifies a multi-dimensional system into a one-dimensional state space. We applied this methodology to explore the impact of weights on the resilience of four dynamics whose weights are assigned by three weight assignment methods. The four dynamical systems are the biochemical dynamical system (B), the epidemic dynamical system (E), the regulatory dynamical system (R), and the birth-death dynamical system (BD). The results show that regardless of the weight distribution, for B, the weights are negatively correlated with the activities of the network, while for E, R, and BD, there is a positive correlation between the weights and the activities of the network. Interestingly, for B, R, and BD, the change in the weights of the system has little impact on the resilience of the system. However, for the E system, the greater the weights the more resilient the system. This study not only simplifies the complexity inherent in weighted networks but also enhances our understanding of their resilience and response to perturbations.

A glance at the COVID-19 epidemic control system in Taiwan: implications for organizational crisis management and leadership in black swan events

Abstract The COVID-19 pandemic caused substantial impacts and uncertainty in the world. For human resource leaders compelled into new roles in light of the pandemic, this paper used an exemplary case – Taiwan – to (1) discuss lessons and takeaways from the Taiwanese government’s and organizations’ response to the pandemic and (2) identify the structure of a pandemic control model and effective leadership behaviours at the national level that may be adopted in business settings to decrease the disruptive effects of a crisis. While efficient subsystems in organizational governance, policy, leadership, and culture are essential to combat a crisis, the evidence from Taiwan’s case in these contexts highlights the importance of various infrastructures and communication pathways in crisis management. The study aims to advance the understanding of effective responses and leadership skills in times of crisis and contribute to human resource literature and policy development in business settings. Human resource professionals and leaders play an important role as change agents to reduce the disruptive effects of a black swan event and can benefit from this study to be prepared for future crises. Implications for international human resource interventions in crisis management and leadership and recommendations for future research are provided.

Pakistan's national COVID-19 response: lessons from an emergent response to the pandemic.

In 2020, Pakistan faced the formidable challenge of the COVID-19 pandemic with an existing yet disjointed healthcare infrastructure, that included by over 18,000 public and an estimated 75,000 private health facilities and some elements of an epidemic surveillance and response system. This descriptive study examines how Pakistan developed a COVID-19 response, driven by establishing a central coordination and decision-making mechanism to overcome these systemic challenges. The study is based on interviews and interactions of the many actors in the response by the authors, who also participated in nearly all proceedings of the National Command and Operation Centre (NCOC) and many of the National Coordination Committee (NCC). This information is supplemented by reviewing documents, reports, news items, media and social media, and journal articles. The study highlights the critical role of political arrangement, where the NCC, comprising of ministers, bureaucrats, and military personnel, facilitated federal and provincial integration. The NCC found resources and set policy. Its direction was implemented by the NCOC, a top-down yet inclusive platform, integrated political, military, and civil society actors, to ensure cohesive decision-making and implementation. It provided technical guidance, harnessed data for strategic decisions and held implementers accountable. At its peak, the NCOC boasted nearly 300 personnel, including high-ranking military officers, a stark contrast to the limited staffing in most ministries. In addition, the response's success is attributed to the perception of COVID-19 as an existential threat, leading to unprecedented collaboration and decisive actions that were enforced authoritatively. Pakistan's experience offers valuable insights for proactive management of health emergencies in resource-limited settings. It underscores the necessity for inter-sectoral dialog and data-driven policy implementation, especially in the context of political economies where activity-driven governance often overshadows objective-driven policy execution. However, the lessons from the COVID-19 response, including a blueprint for future epidemic responses and lessons for use of data and evidence in developing country health systems, if not institutionalized, risk being lost in the post-pandemic era.

Dynamics of a linear source epidemic system with diffusion and media impact

Dynamics of a linear source epidemic system with diffusion and media impact

Surveillance of respiratory viruses by aerosol screening in indoor air as an early warning system for epidemics.

The development of effective methods for the surveillance of seasonal respiratory viruses is required for the timely management of outbreaks. We aimed to survey Influenza-A, Influenza-B, RSV-A, Rhinovirus and SARS-CoV-2 surveillance in a tertiary hospital and a campus over 5 months. The effectiveness of air screening as an early warning system for respiratory viruses was evaluated in correlation with respiratory tract panel test results. The overall viral positivity was higher on the campus than in the hospital (55.0% vs. 38.0%). Influenza A was the most prevalent pathogen in both locations. There were two influenza peaks (42nd and 49th weeks) in the hospital air, and a delayed peak was detected on campus in the 1st-week of January. Panel tests indicated a high rate of Influenza A in late December. RSV-A-positivity was higher on the campus than the hospital (21.6% vs. 7.4%). Moreover, we detected two RSV-A peaks in the campus air (48th and 51st weeks) but only one peak in the hospital and panel tests (week 49). Although rhinovirus was the most common pathogen in panel tests, rhinovirus positivity was low in air samples. The air screening for Influenza-B and SARS-Cov-2 revealed comparable positivity rates with panel tests. Air screening can be integrated into surveillance programs to support infection control programs for potential epidemics of respiratory virus infections except for rhinoviruses.

How do time delays influence dynamics and controls of a generalized SEAIR model?

Given the critical role of time delays in epidemic modeling, this paper delves into the dynamics and finite-time optimal stabilization of a novel epidemic system characterized by such delays. Our findings reveal that time delays significantly influence both the system’s dynamics and the formulation of an optimal control strategy. Specifically, the system’s endemic equilibrium point remains locally asymptotically stable under mild conditions for small time delays. However, exceeding critical delay thresholds induces Hopf bifurcation. To closely regulate the system’s state towards the equilibrium point and minimize control costs, we propose an optimal control problem. We derive the explicit form of the optimal control strategy employing Pontryagin’s Maximum Principle. Finally, numerical simulations further confirm the theoretical results obtained in this paper.

Development, Implementation and Evaluation of An Epidemic Communication System

The purpose of this study is to discuss epidemic communication for drones to share information in flight and to develop a wireless system for implementation. Various theoretical studies have been conducted on epidemic communication, but their applications are not clear, so a system that assumes practical use is developed. As the main evaluation items, we analyzed the effect of communication interference between drones on the amount of data transmission, and furthermore, proposed an optimal transmission method depending on the flight speed. In these analysis results, we designed functions to be implemented in drones, developed wireless devices, and confirmed their operation through demonstration tests using actual drones. Based on the results of this research, we succeeded in identifying issues to be addressed in order to implement the system on drones and in developing an epidemic communication system based on the results of demonstration experiments, thereby contributing to the realization of inter-drone communication in the future.

Respiratory illness of unknown cause as an early signal of the COVID-19 pandemic

Background: As the COVID-19 pandemic began, cases of respiratory illness of unknown cause could have presented early disease signals. The detection of such signals can provide useful clues regarding disease emergence and opportunities for early outbreak control. This study assessed trends in the global incidence of reports of respiratory illness of unknown cause as early indicators of COVID-19 in late 2019, using the early warning system, EPIWATCH, to investigate the disease’s origins and early epidemiology. Methods: Global news articles published between 1-Oct and 31-Dec in 2019 and collected by EPIWATCH, an open-source Artificial Intelligence (AI)-driven epidemic surveillance system, were searched for keywords relating to unknown respiratory illness. Control periods representing the pre-COVID period (1-Oct to 31-Dec 2016-2018) were used to compare report trends over time. Data on report location, case demographics, outbreak setting, and type of illness, were extracted and descriptively analysed. Fisher’s exact tests were conducted to assess proportional differences between years. Results: Overall, 113 EPIWATCH reports of unknown respiratory illness were identified from 12 countries in 2019. Most reports (n=111 [98.2%]) were published in Northern hemisphere countries, in particular Russia (n=A72 [63.7%]) and the United States (n=17 [15.0%]). China experienced a month-on-month increase in reports. Schools were the most reported outbreak setting (n=49 [43.4%]). The proportion of included reports in 2019 was significantly higher compared to 2016-2018 (p<.001). Conclusion: Potential early COVID-19 signals were detected from various countries before the end of 2019. AI-driven open-source disease surveillance systems, such as EPIWATCH, can provide early warnings to enhance disease surveillance and improve outbreak prevention and response.

Examining primary healthcare services following earthquake shocks in Turkiye: A critical analysis of the initial 3months and the vital role of humanitarian policy transfer for rapid response.

The purpose of this article is to discuss the importance of policy transfer by humanitarian NGOs to post-disaster regions and the effectiveness of mobile Primary Health Care (PHC) services immediately after disasters. This study also focused on analysing the first 3months aftermath of the earthquake and assessed the changes in the access and needs of vulnerable groups in emergency response creation and systematic interventions after disasters. In disasters that require urgent response such as earthquakes, the importance of the existing NGOs (Such as MdM) capacity in the countries has emerged in terms of rapid response and experience sharing. During the field work, it is observed that MdM Mobile Medical Units (MMU) teams had played a key role in terms of tracking PHC needs, functioning as early warning system for epidemics, and prevention of communicable diseases in the EQ effected areas. In this aspect, the changes in diagnoses in the first trimester is examined using the comparative analysis methods. This study used a cross-sectional mixed-method approach in terms of objective evaluation. The results from a quantitative needs assessment were complemented by qualitative data. Herein, the data were collected in two stages: First, primary data was collected through the field activities of MMU, and second field-based assessments was conducted with health professionals who have been working in the MMU teams since the first moments of the earthquake.

Construction and reflections on the disease prevention and control system of aquatic organisms based on management perspective

Disease prevention and control are a crucial aspect of fishery management, as they ensure the sustainability of aquacultural systems. With a specific focus on Shandong Province, China, this study outlines the occurrence of diseases in aquatic organisms. It summarizes dynamics that drive disease prevention and control and supports technology, work mode, and emergency prevention and control. In addition, this study also delves into the role of epidemic prevention and control institutions, the upgrading of the social service of disease prevention systems, and insufficient technical support for new species and models. Overall, the findings outlined in this study are intended to achieve the following aims: enhance organizational management of disease prevention and control, standardize the implementation of social services, improve research into prevention and control technologies, and highlight the importance of information technology in disease prevention and control. Based on practical aquaculture production, our aim is to comprehensively analyze the situation of the construction of a disease prevention and control system, strengthen guidance on epidemic prevention technologies, establish a robust animal epidemic prevention system, and propose development strategies, serving as a reference for competent authorities to enhance the disease prevention and control of aquatic organisms.

A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system

In this study, a stochastic computing structure is provided for the numerical solutions of the SIRC epidemic delay differential model, i.e. SIRC-EDDM using the dynamics of the COVID-19. The design of the scale conjugate gradient (CG) neural networks (SCGNNs) is presented for the numerical treatment of SIRC-EDDM. The mathematical model is divided into susceptible S ( ρ ) , recovered R ( ρ ) , infected I ( ρ ) , and cross-immune C ( ρ ) , while the numerical performances have been provided into three different cases. The exactitude of the SCGNNs is perceived through the comparison of the accomplished and reference outcomes (Runge-Kutta scheme) and the negligible absolute error (AE) that are performed around 10−06 to 10−08 for each case of the SIRC-EDDM. The obtained results have been presented to reduce the mean square error (MSE) using the performances of train, validation, and test data. The neuron analysis is also performed that shows the AE by taking 14 neurons provide more accurateness as compared to 4 numbers of neurons. To check the proficiency of SCGNNs, the comprehensive studies are accessible using the error histograms (EHs) investigations, state transitions (STs) values, MSE performances, regression measures, and correlation.

Heat-Related Emergency Department Visits - United States, May-September 2023.

Unprecedented heat waves can affect all persons, but some are more sensitive to the effects of heat, including children and adults with underlying health conditions, pregnant women, and outdoor workers. Many regions of the United States experienced record-breaking high temperatures in 2023, with populations exposed to extremely high temperatures for prolonged periods. CDC examined emergency department (ED) visits associated with heat-related illness (HRI) from the National Syndromic Surveillance Program and compared daily HRI ED visit rates during the warm-season months (May-September) of 2023 with those during 2018-2022. In the 2023 warm-season months, daily HRI ED visit rates peaked in several regions and remained elevated for a prolonged duration. More males than females sought care in EDs for HRI, especially males aged 18-64 years. CDC issued multiple public health alerts using the Epidemic Information Exchange system to bring attention to increases in ED utilization for HRI. Deaths and illnesses associated with heat exposure are a continuing public health concern as climate change results in longer, hotter, and more frequent episodes of extreme heat. Near real-time monitoring of weather conditions and adverse health outcomes can guide public health practitioners' timing of risk communication and implementation of prevention measures associated with extreme heat.

On a diffusive epidemic model with the tendency to move away from the infectious diseases

This paper is purported to investigate an epidemic system with the tendency of the susceptible to move away from the infectious diseases in a bounded domain with no flux boundary condition. The local and global stabilities of positive equilibrium are investigated to this system without cross-diffusion. The sufficient conditions to nonexistence and existence of non-constant positive solution are considered for this epidemic system. The results show that there exists spatiotemporal pattern formation when the tendency of susceptible individuals is strong to move away from the infectious diseases with 1<R0<1+min⁡{dSd,βσ1Aσ2}, while there does not exist spatiotemporal pattern formation when the self pressure is big enough with a weak tendency of the susceptible individuals to keep away from the infectious individual. Moreover, there exists positive solution bifurcation from the semi-trivial solution when the basic reproduction number is equal to one, which implies that endemic disease exists locally.

Stochastic dynamic effects of media coverage and incubation on a distributed delayed epidemic system with Lévy jumps

A stochastic SIAM (Susceptible individual–Infected individual–Aware individual–Media coverage) epidemic system with Lévy jumps and distributed time delay is established, which is utilized to investigate hybrid dynamic effects of media coverage and incubation on infectious disease transmission. Stochastically ultimate boundedness of the positive solution is discussed. Existence of a unique global positive solution is studied. By constructing appropriate stochastic Lyapunov functions, existence of a unique ergodic stationary distribution is investigated. Sufficient conditions are derived to discuss exponential ergodicity based on verifying a Foster–Lyapunov condition. Furthermore, sufficient conditions for persistence in mean and extinction of infectious disease are investigated. Finally, numerical simulations are carried out to show consistency with theoretical analysis.

Impact of the coronavirus disease 2019 pandemic on the diversity of notifiable infectious diseases: a case study in Shanghai, China.

The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.

Implementation of Digital Health in Addressing Global Threats: Lessons from the Use of Technology during COVID-19 Pandemic in Indonesia

This research conducted a systematic literature review to explore the implementation of digital health in Indonesia, focusing on the digital health policies, usage during the COVID-19 pandemic, benefits, and lessons learned. The study identified 10 relevant journals through database searches and analyzed the trends in publication, productive journals, and top institutions involved in digital health research. The findings revealed an increasing interest in digital health, with a growing number of published articles from 2021 to 2023. ScienceDirect emerged as the most productive journal, followed by PubMed and MDPI. The University of Indonesia and the University of Gajah Mada were the leading institutions in digital health research in Indonesia. The study discussed the implementation of digital health during the pandemic, highlighting its role in epidemic surveillance, telemedicine services, and data systems for COVID-19 management. The research also emphasized the challenges faced in the 3T regions (remote areas) of Indonesia due to limited internet access and infrastructure. Overall, the findings contribute to understanding the landscape of digital health in Indonesia and provide insights into its benefits and lessons learned.

Boundedness and non‐existence of traveling wave solutions for a four‐compartment lattice epidemic system with exposed class and standard incidence

A recent paper (Zhang R, Yu X. Traveling waves for a four‐compartment lattice epidemic system with exposed class and standard incidence. Math Meth Appl Sci. 2022; 45: 113‐136) presented a four‐compartment lattice epidemic system with exposed class and standard incidence. The authors studied the existence and non‐existence of traveling wave solutions of this system. However, the limit behavior of ‐component of traveling wave solutions is still open. In this paper, we solve this open problem and establish the boundedness of traveling wave solutions by analysis technique. Meanwhile, we study the non‐existence of traveling wave solutions with non‐positive wave velocity.

How does accounting save lives? An econometric analysis exploring the effectiveness of the Crispi-Pagliani Health Reform (1888) in Italy

This study compares the healthcare management systems for cholera epidemics in Italy before and after the Crispi-Pagliani Health Reform of 1888. Specifically, we compare the monitoring, accounting, and reporting systems of deaths and infections during the 1884 and 1893 epidemics. Based on an econometric analysis of data from the Italian National Statistical Institute, we demonstrate that the post-reform system was more effective in reducing deaths by optimising medical personnel allocation in areas with higher epidemic intensity. From a Foucauldian perspective, we provide insights into the role of accounting in supporting noso-politics, bio-politics, and bio-power. Further, this study advances our understanding of reporting systems and the dynamics that governments trigger as part of strategies for preventing or containing contagion during epidemics. Finally, this study supports the use of econometrics in accounting history.

Novel neuro-stochastic adaptive supervised learning for numerical treatment of nonlinear epidemic delay differential system with impact of double diseases

ABSTRACT This paper presents a novel neuro-stochastic adaptive processing to investigate the dynamic behavior of the nonlinear SIS epidemic delayed model with the impact of double disease (SIS-EDDM) using the knack of prediction and modeling of artificial neural networks (ANNs) optimized through Levenberg-Marquardt technique (LMT), i.e. ANNs-LMT. The model captures the dynamic progression of individuals in the population, differentiating between those susceptible to infection and affected by two unique viruses. The inclusion of time delays in the differential equations introduces a critical temporal aspect to the model, enhancing its precision in portraying the transmutation process. The reference dataset for ANNs-LMT is produced using the explicit Runge-Kutta method, with variations in the transmission coefficients between susceptible and infective populations, the death rate of infective population caused by diseases, the cure rate of treatment for infected population, vaccination proportional coefficient for the susceptible population, white noise of the environment, and time delay. The designed computing framework of ANNs-LMT is used to determine the numerical solutions of the variants of SIS-EDDM by incorporating the training, testing, and validation samples based adaptive learning processing. The neuro-stochastic adaptive processing of ANNs-LMT is validated by minimal absolute and mean squared errors, coupled with the attainment of nearly optimal regression measures, demonstrating its accuracy and effectiveness.