Search Results (679)

The 100-Day Mission, coordinated by the Coalition for Epidemic Preparedness Innovations (CEPI) and endorsed by significant international stakeholders, aims to shorten the timeframe for developing and implementing vaccines to 100 days after the report of a new pathogen. This ambitious goal is outlined as an essential first step in improving pandemic preparedness worldwide. This review highlights the mission’s implementation potential and challenges by examining it through the lens of low- and middle-income countries (LMICs), which often face barriers to equitable vaccine access. This article explores the scientific, economic, political, and social aspects that could influence the mission’s success, relying on lessons learned from previous pandemics, such as the Spanish flu, H1N1, and COVID-19. We also examined important cornerstones like prototype vaccine libraries, accelerated clinical trial preparedness, early biomarkers identification, scalable manufacturing capabilities, and rapid pathogen characterization. The review also explores the World Health Organization (WHO) Pandemic Agreement and the significance of Phase 4 surveillance in ensuring vaccine safety. We additionally evaluate societal issues that disproportionately impact LMICs, like vaccine reluctance, health literacy gaps, and digital access limitations. Without intentional attempts to incorporate under-resourced regions into global preparedness frameworks, we argue that the 100-Day Mission carries the risk of exacerbating already-existing disparities. Ultimately, our analysis emphasizes that success will not only rely on a scientific innovation but also on sustained international collaboration, transparent governance, and equitable funding that prioritizes inclusion from the beginning. Full article

Background: Greece is among the fastest-aging countries globally, with one of the highest proportions of elderly individuals. As a result, the prevalence of mild cognitive impairment (MCI) and dementia is among the highest in Europe. The distribution of affected individuals varies considerably across different regions of the country. Method: We estimated the number of people living with MCI or dementia in Greece and visualized these estimates using heatmaps by regions for four census years: 1991, 2001, 2011, and 2023 (the 2023 census was delayed due to the COVID-19 pandemic). Age- and sex-specific prevalence rates of MCI and dementia were obtained from the Hellenic Longitudinal Investigation of Aging and Diet. These prevalence rates were then applied to population data from each census to estimate the number of affected individuals per region. Results: There was a consistent increase in the number of people living with MCI, rising from 177,898 in 1991 to 311,189 in 2023. Dementia cases increased from 103,535 in 1991 to 206,939 in 2023. Projections based on future census data for 2035 and 2050 suggest that the number of people with MCI will reach 375,000 and 440,000, respectively, while dementia cases will increase to 250,000 in 2035 and 310,000 in 2050. Conclusion: Given that each person with dementia typically requires care from at least two caregivers over time, these projections highlight the profound impact the dementia epidemic will have on Greece. The heatmaps developed in this study can serve as valuable tools for policymakers in designing and implementing clinical care programs tailored to the needs of each region based on the projected burden of disease. Full article

Background The COVID-19 epidemic had a deleterious impact on mental health and substance abuse and led to an increase in several forms of violence, including self-harm and interpersonal violence among youth from low- and middle-income countries. Nevertheless, the relationship between the variables and their directionality has not been recognized. This study describes the relationship directionality between these variables among 18- to 20-year-old Mexican youths during the COVID-19 pandemic. Methods The longitudinal cohort study comprises an evolving group of young Mexican adults: 1390 participants aged 18 in 2021, 654 aged 19 in 2022, and 442 aged 20 in 2023. Proportions by sex—50% were matched in every cohort, and the evolution–age sample accomplishment accounted for 47% in 2022 and 32% in 2023. Results According to a structural equation model, which fit the data from 195 iterations with 246 parameters (X²[2722] = 8327.33, p < 0.001), yielding a CFI of 0.946, a TLI of 0.943, and an RMSEA of 0.029 [0.028–0.029]), perpetrating intimate violence, preceded by suffering intimate violence, combined with suffering anxiety symptoms, was associated with self-harm–suicide thoughts and behaviors (ShSTB), marked distress, dysfunction, and somatization symptoms. The relationship was stronger in women and 20-year-old Mexicans. In men, this pathway was exclusively associated with ShSTB. Suffering from intimate violence has been associated with depression, anxiety, and PTSD symptoms, as well as harmful alcohol use. Conclusions During an epidemic, prevention programs should be designed to warn about self-harm–suicide thoughts and behaviors, not only to ensure the safety of the victims of intimate personal-violence but also to prevent the suicidal behavior of perpetrators. Full article

This study examines the epidemiological situation of tuberculosis (TB) in the regions of the Republic of Kazakhstan over the past seven years (2018–2024), which cover the before-, during- and after-COVID-19 periods, with a focus on the risks of its emergence and spread. The analysis revealed that while TB incidence is declining, mortality remains high in the before- and during-COVID-19 periods, indicating a general decline in population health. The concentration of TB incidence in relation to geographic location was mainly in the northern, western and southern regions. Before COVID-19, TB incidence reached 48.2 cases and mortality reached a maximum of 2.4 cases per 100,000 people. In 2024, the incidence and mortality of tuberculosis significantly decreased to 33.5 (30.5%) and 1.0 (58.3%), respectively, reflecting an improvement in health indicators in the post-pandemic period. In the after-COVID-19 period, in regions with high unemployment, the incidence was higher than in the before- and during-COVID-19 periods. Nevertheless, it is important that the trend in tuberculosis incidence shows positive improvement after the COVID-19 period. In addition, a comparative analysis of tuberculosis incidence trends in different age groups and social factor groups shows that the adult population remains the most vulnerable category among the general population. The above-listed factors, as well as our analysis of tuberculosis incidence, shows that TB incidence does not always correlate with the level of vaccination in different regions of Kazakhstan, indicating a multifactorial influence on the tuberculosis epidemic. Full article

The COVID-19 pandemic was a worldwide crisis with significant impact on professional, economic, and social well-being. In medical academics, researchers were hampered by the need to provide critical support to pandemic efforts at their institutions, while balancing rapid communication of information that could impact practices and inform behavior. Autoethnography as a research method was employed by many early on as a means of characterizing aspects of the COVID-19 response. Two surgeons from heavily hit areas early in the epidemic—Bergamo, Italy, and Boston, the United States—entered into an online, virtual, professional relationship that helped them both endure the pandemic and inform their institutions and communities. Their relationship influenced practices across the United States and beyond. This paper explores how the principles of autoethnography as a valid, essential and important method of research can lead to significant impacts during times of crises. Full article

Neurological symptoms involving the central nervous system (CNS) and peripheral nervous system (PNS) are common complications of acute COVID-19 as well as post-COVID conditions. Most research into these neurological sequalae focuses on the CNS, disregarding the PNS. Guinea pigs were previously shown to be useful models of disease during the SARS-CoV-1 epidemic. However, their suitability for studying SARS-CoV-2 has not been experimentally demonstrated. To assess the suitability of guinea pigs as models for SARS-CoV-2 infection and the impact of SARS-CoV-2 infection on the PNS, and to determine routes of CNS invasion through the PNS, we intranasally infected wild-type Dunkin-Hartley guinea pigs with ancestral SARS-CoV-2 USA-WA1/2020. We assessed PNS sensory neurons (trigeminal ganglia, dorsal root ganglia), autonomic neurons (superior cervical ganglia), brain regions (olfactory bulb, brainstem, cerebellum, cortex, hippocampus), lungs, and blood for viral RNA (RT-qPCR), protein (immunostaining), and infectious virus (plaque assay) at three- and six-days post infection. We show that guinea pigs, which have previously been used as a model of SARS-CoV-1 pulmonary disease, are not susceptible to intranasal infection with ancestral SARS-CoV-2, and are not useful models in assessing neurological impacts of infection with SARS-CoV-2 isolates from the early pandemic. Full article

COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. We systematically reviewed COVID-19 ML prediction models developed under the background of the epidemic using the PRISMA method. We used the selected keywords to screen the relevant literature of COVID-19 prediction using ML technology from 2020 to 2023 in the Web of Science, Springer and Elsevier databases. Based on predetermined inclusion and exclusion criteria, 136 eligible studies were ultimately selected from 5731 preliminarily screened publications, and the datasets, data preprocessing, ML models, and evaluation metrics used in these studies were assessed. By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. In addition, we compared the performance of ML models with other models in the COVID-19 prediction task. The results showed that the propagation of COVID-19 is affected by multiple factors, including meteorological and socio-economic conditions. Compared to traditional methods, ML methods demonstrated significant advantages in COVID-19 prediction, especially hybrid modelling strategies, which showed great potential in optimizing accuracy. However, these techniques face challenges and limitations despite their strong performance. By reviewing existing research on COVID-19 prediction, this study provided systematic theoretical support for AI applications in infectious disease prediction and promoted technological innovation in public health. Full article

Background/Objectives: Epidemics have historically shaped societies, influencing demographic structures, social organization, and economic stability. The 1742–1743 epidemic had a profound impact on populations along the Royal Road (Camino Real), the main colonial corridor between Buenos Aires and Lima. However, its specific demographic and socio-economic effects remain underexplored. This study aims to examine these impacts of the 1742–1743 epidemic through a comparative analysis of urban centers and Indigenous communities. Methods: A historical–comparative approach was employed, analyzing secondary sources including parish records and colonial administrative documents. This study assessed excess mortality and socio-economic consequences across different population groups and settlement types. Results: Mortality rates increased dramatically—up to twelve times the pre-epidemic average in Cordova (Córdoba) and by 45% in Santa Fe—disproportionately affecting Indigenous and enslaved populations. Urban centers experienced severe economic disruption and slow recovery, whereas Indigenous communities and Jesuit missions demonstrated greater resilience. Their communal strategies and early isolation measures contributed to a faster demographic stabilization. Additionally, the epidemic weakened colonial governance in some areas, altering local power structures. Conclusions: The epidemic of 1742–1743 revealed divergent patterns of vulnerability and resilience. Comparative analysis underscores recurring themes in the epidemic response and recovery, drawing relevant parallels with contemporary crises such as COVID-19. Recognizing these historical patterns of adaptation can inform present and future public health strategies. The terminology “plague” is used based on contemporary sources and not confirmed clinically. Full article

Mathematical modeling allows taking into account registered and hidden infections to make correct predictions of epidemic dynamics and develop recommendations that can reduce the negative impact on public health and the economy. A model for visible and hidden epidemic dynamics (published by the author in February 2025) has been generalized to account for the effects of re-infection and newborns. An analysis of the equilibrium points, examples of numerical solutions, and comparisons with the dynamics of real epidemics are provided. A stable quasi-equilibrium for the particular case of almost completely hidden epidemics was also revealed. Numerical results and comparisons with the COVID-19 epidemic dynamics in Austria and South Korea showed that re-infections, newborns, and hidden cases make epidemics endless. Newborns can cause repeated epidemic waves even without re-infections. In particular, the next epidemic peak of pertussis in England is expected to occur in 2031. With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients. Full article

Although the immediate impact of the COVID-19 pandemic has been alleviated, its long-term effects continue to shape global health and public safety. Policymakers should prepare for potential future health crises and direct urban planning toward more sustainable outcomes. While numerous studies have examined factors influencing the risk of COVID-19, few have investigated the spatial spillover effects of urban form and green space. In this study, we quantified urban form using landscape pattern indices, represented population mobility with the Baidu Migration Scale Index, and assessed the role of key influencing factors on the epidemic through STIRPAT and spatial Durbin models. Our findings reveal that population migration from Wuhan had a significant local impact on the spread of COVID-19. These factors not only intensified local transmission, but also triggered positive spatial spillover effects, spreading the virus to neighboring regions. We also found that green space connectivity (pc₅) plays a crucial role in reducing the spread of the virus, both locally and in surrounding areas. High green space connectivity helps mitigate disease transmission during an epidemic. In contrast, the spatial configuration and unipolarity of urban areas (pc₁) contributed to the increased spread of the virus to neighboring cities. Ultimately, balancing building density with green space distribution is essential for enhancing urban resilience. This research provides new insights into sustainable urban planning and helps us understand the impact of the spillover effects of gray–green space forms on public health and safety. Full article

This paper introduces a novel fractional Susceptible-Infected-Recovered ($\mathbb{SIR}$) model that incorporates a power Caputo fractional derivative (PCFD) and a density-dependent recovery rate. This enhances the model’s ability to capture memory effects and represent realistic healthcare system dynamics in epidemic modeling. The model’s utility and flexibility are demonstrated through an application using parameters representative of the COVID-19 pandemic. Unlike existing fractional $\mathbb{SIR}$ models often limited in representing diverse memory effects adequately, the proposed PCFD framework encompasses and extends well-known cases, such as those using Caputo–Fabrizio and Atangana–Baleanu derivatives. We prove that our model yields bounded and positive solutions, ensuring biological plausibility. A rigorous analysis is conducted to determine the model’s local stability, including the derivation of the basic reproduction number (${\mathbf{R}}_0$) and sensitivity analysis quantifying the impact of parameters on ${\mathbf{R}}_0$. The uniqueness and existence of solutions are guaranteed via a recursive sequence approach and the Banach fixed-point theorem. Numerical simulations, facilitated by a novel numerical scheme and applied to the COVID-19 parameter set, demonstrate that varying the fractional order significantly alters predicted epidemic peak timing and severity. Comparisons across different fractional approaches highlight the crucial role of memory effects and healthcare capacity in shaping epidemic trajectories. These findings underscore the potential of the generalized PCFD approach to provide more nuanced and potentially accurate predictions for disease outbreaks like COVID-19, thereby informing more effective public health interventions. Full article

Understanding the effectiveness of different quarantine strategies is crucial for controlling the spread of COVID-19, particularly in regions with limited data. This study presents a SCIRD-inspired model to simulate the transmission dynamics of COVID-19 in medium-sized cities and their surrounding satellite towns. Unlike previous works that focus primarily on large urban centers or homogeneous populations, our approach incorporates intercity mobility and evaluates the impact of spatially differentiated interventions. By analyzing lockdown strategies implemented during the first year of the pandemic, we demonstrate that short, localized lockdowns are highly effective in reducing virus propagation, while intermittent restrictions balance public health concerns with socioeconomic demands. A key contribution of this study is the validation of the epidemic model using real-world data from the 2021 lockdown that occurred in a medium-sized city, confirming its predictive accuracy and adaptability to different contexts. Additionally, we provide a detailed analysis of how mobility patterns between municipalities influence infection spread, offering a more comprehensive mathematical framework for decision-making. These findings advance the understanding of epidemic control in regions with sparse data and provide evidence-based insights to inform public health policies in similar contexts. Full article

Diphtheria is a severely infectious and deadly bacterial disease with Corynebacterium diphtheriae as the causative agent. Since the COVID-19 pandemic, contagious diseases such as diphtheria have re-emerged due to disruptions in routine childhood immunization programs worldwide. Nigeria is witnessing a significant increase in diphtheria outbreaks likely due to an inadequate health care system and insufficient public enlightenment campaign. This paper presents a mathematical epidemic diphtheria model in Nigeria, which includes a public enlightenment campaign to assess its positive impact on the prevalence of the disease. The mathematical analysis of the model reveals two equilibrium points: the diphtheria infection-free equilibrium and the endemic equilibrium. These equilibrium points are shown to be stable globally asymptotically if ${\mathcal{R}}_c<1$ and ${\mathcal{R}}_c>1$, respectively. The model was fit using the confirmed diphtheria cases data of Kano State from January to December 2023. Sensitivity analysis indicates that the transmission rate and recovery rate of asymptomatic peopleare crucial parameters to be considered in developing effective strategies for diphtheria control and prevention. This analysis also reveals that the implementation of a high-level public enlightenment campaign and its high efficacy effectively reduce the prevalence of diphtheria. Finally, numerical simulations show that combining the public enlightenment campaign and isolating infected individuals is the best strategy to contain the spread of diphtheria. Full article

Livelihood resilience research is a critical area in contemporary sustainable livelihood studies, offering valuable insights into residents’ livelihood transformation and strategies under sudden shocks or disruptions. This research analyzes 365 households from five towns reliant on tourism in the Wulingyuan Scenic range, situated in the central section of the Wuling Mountain range. The findings reveal that residents’ livelihood resilience decreased by 6.38% from the normal tourism stage (before 2020) to the epidemic disruption stage (2020–2022), followed by a 4.54% increase during the tourism recovery stage (after 2022). Despite fluctuations caused by exogenous shocks like the COVID-19 pandemic, residents’ livelihood resilience remained at a moderate level overall. Spatially, livelihood resilience exhibited a northwest–southeast dispersion trend, with a noticeable shift toward the southeast. Key drivers of resilience included increased material capital, enhanced organizational management capabilities, residents’ clear understanding of livelihood challenges, and positive attitudes. Conversely, constraints included the pandemic’s impacts, limited community participation, reduced tourist numbers, inefficient ecotourism management, insufficient financial capital, weak learning capacities, and monolithic livelihood strategies. The study highlights that those changes in the tourism development environment, coupled with interactive pathways of buffering, adaptation, and transformation capabilities, jointly influence livelihood resilience. Synergistic efforts in these areas can significantly enhance residents’ livelihood resilience. Full article

After the end of the COVID-19 epidemic, the global tourism market is continuing to recover, and tourism is once again becoming a significant part of the national economies of many countries. This study used panel data from 31 provinces and cities in China between 2011 and 2019 for empirical testing, aiming to understand the contribution of new infrastructure investment in China to the international tourism industry. The research findings indicate that infrastructure investment in China had a positive impact on the development of international tourism. Infrastructure investment in China increased by 1%, with the number of inbound overnight tourists and international tourism revenue increasing by 0.373% and 0.570%, respectively. Mechanism analysis shows that transportation accessibility and information technology levels influenced international tourism; that is, new infrastructure investments improved the regional transportation environment and enhanced the level of information technology, which was beneficial for international tourism. In addition, there was apparent regional heterogeneity in the impact of new infrastructure investments in China on the international tourism industry. Overall, the conclusions drawn in this article are novel and provide vital policy implications for promoting the sustainable development of China’s tourism industry. Full article