Epidemic Outbreaks Research Articles

Coercive Vaccination Policy in Nigeria: Legal Perspectives

Background: In December 2021, the Nigerian federal government declared a compulsory COVID-19 immunisation for all employees of government. This declaration by the government has been viewed by some Nigerians as a contravention of the fundamental rights of Nigerian citizens. Aim: This study was aimed at identifying the human rights concerns surrounding vaccination mandates from the perspective of legal practitioners in Nigeria. Methods: This study was a cross-sectional study that used a semi-structured self-administered questionnaire to interview legal practitioners practicing in Uyo, Nigeria. The survey focused on identifying human right concerns surrounding vaccination mandates. Results: One hundred and five legal practitioners participated in the study. Data analysis revealed that 79 (75.2%) of our respondents agreed that vaccination mandates to prevent an epidemic is well within the powers of the state, while 97 (92.4%) asserted that the Nigerian constitution gives the state authority to enact health laws including quarantine and vaccination laws to protect its citizens. According to 59% (n=62) of our respondents, the only exception to a mandatory vaccination is an offer of apparent or reasonably certain proof to the state’s board of health that the vaccination would seriously impair health or probably cause death. Conclusion: In the opinion of majority of the legal practitioners interviewed, the Nigerian constitution gives the state the power to implement measures established by legislation to protect the health of her citizens. Thus, coercive vaccination policies by the state to protect the public from an epidemic outbreak of a disease which threatens the safety of citizens may be legally binding on the citizens.

Questionable Immunity to Mumps among Healthcare Workers in Italy—A Cross-Sectional Serological Study

Highly contagious diseases, such as mumps, are a global concern as new epidemics continue to emerge, even in highly vaccinated populations. The risk of transmission and spread of these viruses is even higher for individuals who are more likely to be exposed, including healthcare workers (HCWs). In healthcare settings, both HCWs and patients are at risk of infection during the care process, potentially leading to nosocomial epidemic outbreaks. Mumps is often underestimated compared with measles and rubella, despite being milder and less likely to spread. In fact, the risk of complications following mumps infection is extremely high, especially if the disease occurs in adulthood. The measles–mumps–rubella (MMR) vaccine has been shown to be an excellent preventive measure. Unfortunately, the mumps component appears to be less effective in inducing immunity than those for measles and rubella (two-dose effectiveness of 85%, 95% and 97%, respectively). The main aim of our study was to investigate the prevalence of detectable mumps antibodies (serum IgG antibodies) in a cohort of Italian and foreign HCWs in relation to personal and occupational factors. We included in the study 468 subjects who underwent health surveillance at the Occupational Medicine Unit of the Tor Vergata Polyclinic in Rome during the period from January 2021 to March 2023. In our study, the proportion of HCWs found to be unprotected against mumps was very high (8.3%), and those found to be immune are below the WHO threshold for herd immunity (95%). From our data, it seems essential that all occupational health services carry out an accurate screening with a dose of anti-mumps antibodies to assess serological protection before starting a job, regardless of an individual’s vaccination history. This approach is proving to be beneficial, accurate, as it allows all serologically non-immune individuals to be vaccinated in the workplace, including those who would be protected by their vaccination history but have lost the antibody response.

A multi-stage optimization model for managing epidemic outbreaks and hospital bed planning in Intensive Care Units

Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.

Influence of subsidy policies against insurances on controlling the propagation of epidemic security risks in networks

Subsidies are used to further control the propagation of epidemic security risks as they offer users incentives to practice security investment behaviors. However, previously proposed subsidy policies have typically been studied without insurance, and it is still challenging to build effective subsidy strategies when users can transfer some of the infection loss to the insurer by purchasing insurance, because these external incentives may alter users' decisions to purchase insurance. To this end, we designed three subsidy policies in a scenario where purchasing insurance is one of the available strategies to users: under SPIns and SPInsp, the subsidies are used as the insurance funds and the risk precaution aspect of the insurance funds, respectively, and under SPInsd, a fraction of the subsidies are used to support some free-riding users with high node importance in their purchase of insurance, and the remaining subsidies are used as the precaution aspect of the insurance funds. The subsidy policies are studied with a formulated security investment game model on scale-free networks from the perspectives of the public and insurer. The results show that SPInsd can always work effectively and outperforms others in limiting the extent of an epidemic outbreak with similar or lower social costs, and without a reduction in profit for the insurer. More importantly, an interesting phenomenon emerges in the SPInsp scenario: under specific insurance parameters, the increase in subsidy funding has a negative impact on preventing the risk spread, leading to larger final epidemic sizes. In addition, the effectiveness of SPInsd on scale-free networks with larger average degree or network size and random networks are also studied. We anticipate this work can provide useful insights for policy makers with respect to design and implementation of optimal subsidy policies related to the control of epidemic security risks under an insurance scenario.

Wastewater monitoring of SARS-CoV-2 gene for COVID-19 epidemiological surveillance in Tucumán, Argentina.

Wastewater-based epidemiology provides temporal and spatial information about the health status of a population. The objective of this study was to analyze and report the epidemiological dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the province of Tucumán, Argentina during the second and third waves of coronavirus disease 2019 (COVID-19) between April 2021 and March 2022. The study aimed to quantify SARS-CoV-2 RNA in wastewater, correlating it with clinically reported COVID-19 cases. Wastewater samples (n = 72) were collected from 16 sampling points located in three cities of Tucumán (San Miguel de Tucumán, Yerba Buena y Banda del Río Salí). Detection of viral nucleocapsid markers (N1 gene) was carried out using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Viral loads were determined for each positive sample using a standard curve. A positive correlation (p < 0.05) was observed between viral load (copies/mL) and the clinically confirmed COVID-19 cases reported at specific sampling points in San Miguel de Tucumán (SP4, SP7, and SP8) in both months, May and June. Indeed, the high viral load concurred with the peaks of COVID-19 cases. This method allowed us to follow the behavior of SARS-CoV-2 infection during epidemic outbreaks. Thus, wastewater monitoring is a valuable epidemiological indicator that enables the anticipation of increases in COVID-19 cases and tracking the progress of the pandemic. SARS-CoV-2 genome-based surveillance should be implemented as a routine practice to prepare for any future surge in infections.

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.

Role of Livestock Insurance in Mitigating Financial Impact of Epidemic Outbreaks on Cattle Farms in India

Purpose: The aim of the study was to analyze the role of livestock insurance in mitigating financial impact of epidemic outbreaks on cattle farms in India. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Livestock insurance plays a crucial role in mitigating the financial impact of epidemic outbreaks on cattle farms. Studies consistently show that cattle farms with livestock insurance experience lower financial losses post-epidemic compared to those without insurance, as they are able to recoup a portion of their losses through insurance claims. Moreover, insured cattle farms exhibit a higher recovery rate, enabling them to rebuild their herds more quickly and resume normal operations. Comprehensive livestock insurance policies that address various risk factors, including disease outbreaks and market price fluctuations, are particularly effective in stabilizing farm incomes and enhancing financial resilience. Unique Contribution to Theory, Practice and Policy: Risk management theory, theory of planned behavior (TPB) &amp; prospect theory may be used to anchor future studies on analyze the role of livestock insurance in mitigating financial impact of epidemic outbreaks on cattle farms. Subsidizing premiums can change the economic calculus for farmers, making the decision to invest in insurance more attractive and financially feasible. Encouraging regional risk pools would necessitate legislative support and possibly inter-state agreements to facilitate broader risk management strategies.

Development and preliminary application of a quadruplex real-time PCR assay for differential detection of porcine circovirus 1-4 in Chengdu, China.

Porcine circovirus (PCV) typically causes severe immune suppression in pigs, leading to mixed clinical infections with various pathogens that can cause significant harm to the pig industry. PCV has four subgenotypes, with PCV4 being an emerging virus that requires investigation due to its potential for epidemic outbreaks. Therefore, there is a need to develop a method that can detect all four PCV strains simultaneously. In this study, four pairs of specific primers and TaqMan probes were designed based on the conserved sequence of the PCV1-4 ORF2 gene to establish a PCV1-4 TaqMan multiplex real-time quantitative PCR method. The novel method was compared to six commercial testing kits for its efficacy. Then, a total of 595 mixed samples of spleen and lymph node collected from 12 districts in Chengdu from July to December 2021 were tested using the novel method. The results showed that the novel PCV1-4 TaqMan multiplex real-time quantitative PCR detection method has satisfied specificity, sensitivity, and repeatability. The positive rates of PCV1, PCV2, and PCV3 in Chengdu were 2.18%, 31.60%, and 15.29%, respectively, while no positive PCV4 was detected. The mixed infection rate of PCV2 and PCV3 was 5.21%. Our novel method may be as a potential method for PCV1-4 detection. Currently, PCV2 is the main epidemic PCV subtype in Chengdu, while the potential threat of PCV4 should also be considered.

Modeling spillover dynamics: understanding emerging pathogens of public health concern

The emergence of infectious diseases with pandemic potential is a major public health threat worldwide. The World Health Organization reports that about 60% of emerging infectious diseases are zoonoses, originating from spillover events. Although the mechanisms behind spillover events remain unclear, mathematical modeling offers a way to understand the intricate interactions among pathogens, wildlife, humans, and their shared environment. Aiming at gaining insights into the dynamics of spillover events and the outcome of an eventual disease outbreak in a population, we propose a continuous time stochastic modeling framework. This framework links the dynamics of animal reservoirs and human hosts to simulate cross-species disease transmission. We conduct a thorough analysis of the model followed by numerical experiments that explore various spillover scenarios. The results suggest that although most epidemic outbreaks caused by novel zoonotic pathogens do not persist in the human population, the rising number of spillover events can avoid long-lasting extinction and lead to unexpected large outbreaks. Hence, global efforts to reduce the impacts of emerging diseases should not only address post-emergence outbreak control but also need to prevent pandemics before they are established.

The economic consequence of large-scale epidemic outbreak: The path and loss evaluation of COVID-19 in China based on input-output analysis

ABSTRACT The COVID-19 pandemic has significantly impacted China's economic and social development. Understanding the direct and indirect effects of the epidemic on the economy is vital for formulating scientifically grounded epidemic management policies. This study assesses the economic losses and influence paths of a large-scale epidemic in China. We proposed three COVID-19 scenarios – serious, normal, and mild – to evaluate the direct economic impact on China's GDP from a demand perspective. An input-output model was used to estimate the indirect impact. Our findings show that China's GDP could lose 94,206, 75,365, and 56,524 hundred million yuan under serious, normal, and mild scenarios, respectively, with corresponding GDP decline rates of 9.27%, 7.42%, and 5.56%. Under the normal scenario, indirect economic loss and total loss are projected at 75,364 and 489,386 hundred million yuan, respectively. Additionally, the pandemic led to a reduction in carbon emissions: direct emissions decreased by 1,218.69 million tons, indirect emissions by 9,594.32 million tons, and total emissions by 10,813.01 million tons across various industries. This study provides a comprehensive analysis of the economic and environmental impacts of the pandemic.

Impact of an ICU bed capacity optimisation method on the average length of stay and average cost of hospitalisation following implementation of China’s open policy with respect to COVID-19: a difference-in-differences analysis based on information management system data from a tertiary hospital in southwest China

ObjectivesFollowing the implementation of China’s open policy with respect to COVID-19 on 7 December 2022, the influx of patients with infectious diseases has surged rapidly, necessitating hospitals to adopt temporary...

Evaluation of age-structured vaccination strategies forcurbing the disease spread.

Age structure is one of the crucial factors in characterizing the heterogeneous epidemic transmission. Vaccination is regarded as an effective control measure for prevention and control epidemics. Due to the shortage of vaccine capacity during the outbreak of epidemics, how to design vaccination policy has become an urgent issue in suppressing the disease transmission. In this paper, we make an effort to propose an age-structured SVEIHR model with the disease-caused death to take account of dynamics of age-related vaccination policy for better understanding disease spread and control. We present an explicit expression of the basic reproduction number , which determines whether or not the disease persists, and then establish the existence and stability of endemic equilibria under certain conditions. Numerical simulations are illustrated to show that the age-related vaccination policy has a tremendous influence on curbing the disease transmission. Especially, vaccination of people over 65 is better than for people aged 21-65 in terms of rapid eradication of the disease in Italy.

Molecular epidemiology and resistance mechanisms of tigecycline-non-susceptible Acinetobacter baumannii isolated from a tertiary care hospital in Chongqing, China.

We studied 34 isolates of Tigecycline-Non-Susceptible A. baumannii (TNAB) obtained from clinical specimens at a large tertiary care hospital in Chongqing, China. These 34 strains belonged to 8 different clones including ST195 (35.3%) and ST208 (17.7%). EBURST analysis found that these 8 ST types belonged to the Clonal Complex 92. Tigecycline resistance-associated genes adeR, adeS, adeL, adeN, rrf, rpsJ, and trm were detected in most strains. The expression level of the resistance-nodulation-cell division (RND) efflux pumps in TNAB strains was higher than the reference strain ATCC19606. 58.8% of strains had a decrease in the tigecycline minimum inhibitory concentration (MIC) after the addition of carbonyl cyanide 3-chlorophenylhydrazone (CCCP). The TNAB strains in our hospital have a high degree of affinity and antibiotic resistance. Regular surveillance should be conducted to prevent outbreaks of TNAB epidemics.

A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Climate, diseases and medicine: the welfare of soldiers during the East Asian War of 1592-1598.

This article examines the care provided for the welfare of soldiers by the three combatant countries - China, Korea and Japan - during the East Asian War of 1592-8. Also known as the Imjin War, this large-scale military conflict can also be understood as an encounter between different state cultures and strategies of military medicine. This study focuses on cold-induced injuries, epidemic outbreaks and external wounds suffered during the war. I illuminate provision of prophylactic measures against cold by the Ming state, as well as attempts by the Sino-Chosŏn medical alliance to manage epidemics and treat wounded soldiers. I contrast these measures with the lack of similar centralised support for the Japanese forces, and examine the effect these differences had upon on military outcomes during the war. The difference in the amount of time, efforts and resources that the three combatant states devoted to sick and injured soldiers has implications not only for our understanding of the war but also for illuminating the early modern history of military medicine in East Asia. By exploring East Asian military medicine during and after the Imjin War, this article responds to recent calls for more detailed examination of histories of military medicine in premodern periods and non-European regions.

Animal Cell Lines as Expression Platforms in Viral Vaccine Production: A Post Covid-19 Perspective.

Vaccines are considered the most effective tools for preventing diseases. In this sense, with the Covid-19 pandemic, the effects of which continue all over the world, humanity has once again remembered the importance of the vaccine. Also, with the various epidemic outbreaks that occurred previously, the development processes of effective vaccines against these viral pathogens have accelerated. By these efforts, many different new vaccine platforms have been approved for commercial use and have been introduced to the commercial landscape. In addition, innovations have been made in the production processes carried out with conventionally produced vaccine types to create a rapid response to prevent potential epidemics or pandemics. In this situation, various cell lines are being positioned at the center of the production processes of these new generation viral vaccines as expression platforms. Therefore, since the main goal is to produce a fast, safe, and effective vaccine to prevent the disease, in addition to existing expression systems, different cell lines that have not been used in vaccine production until now have been included in commercial production for the first time. In this review, first current viral vaccine types in clinical use today are described. Then, the reason for using cell lines, which are the expression platforms used in the production of these viral vaccines, and the general production processes of cell culture-based viral vaccines are mentioned. Also, selection parameters for animal cell lines as expression platforms in vaccine production are explained by considering bioprocess efficiency and current regulations. Finally, all different cell lines used in cell culture-based viral vaccine production and their properties are summarized, with an emphasis on the current and future status of cell cultures in industrial viral vaccine production.

Revolutionizing healthcare image analysis in pandemic-based fog-cloud computing architectures

Healthcare data analysis has become essential after epidemic outbreaks. The manual examination of medical images such as X-rays and computed tomography (CT) scans became one of these challenges. This paper introduces a healthcare architecture that tackles the analysis efficiency and accuracy challenges by harnessing artificial intelligence (AI) capabilities. This architecture utilizes fog computing and presents a modified convolutional neural network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can improve the efficacy and accuracy of medical image analysis, leading to improved patient care and outcomes.

Epidemicity indices and reproduction numbers from infectious disease data in connected human populations

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.

Epidemic process on partially overlapped multi-layer networks

The phenomenon of epidemic spread has received continuous attention due to its profound applications in a wide range of social and economic activities. In this paper we propose a partially overlapped multi-layer network model and illustrate the influence of multi-layer structure on outbreaks. Combined with the classic SIS model, we propose a set of discrete Markov equations and make first-order approximation on the threshold of epidemic outbreak. In comparison with independent simplex networks, we find that a multi-layer structure promotes epidemic spread and leads to a smaller critical threshold. In addition, we also find that the epidemic process on partially overlapped multi-layer networks is dominated by the layer with the largest main eigenvalue. Through Monte Carlo simulations, we find that the role of the dominant layer is irrelevant with its size, which means a small set of nodes can exhibit a disproportionate impact on the epidemics of a large network. Our research sheds light on the epidemic process on partially overlapped multi-layer complex systems, and provides a theoretical explanation of unexpected real-world outbreaks.

Eggplant Little Leaf-Associated Phytoplasma Detection in Seedlings under Insect-Proof Conditions

Eggplant, or brinjal, is one of the most consumed and important tropical solanaceous vegetable crops grown worldwide. Little leaf is a disease associated with the presence of phytoplasmas especially widespread in brinjal in India. To clarify the epidemiology of this disease, a verification of its transmission through seeds to seedlings and their progeny derived from symptomatic mother plants was performed. Brinjal seeds field-collected in the Dharwad district of Karnataka State, India, were sowed in a greenhouse under insect-proof conditions. DNA was extracted from seedlings and their progeny and from symptomatic plant samples collected in the field. The first- and second-generation seedlings obtained *under these conditions were tested at various time points after germination by amplification of the 16S rRNA gene of phytoplasmas. The amplicons obtained were subjected to restriction fragment length polymorphism (RFLP) analysis and sequencing for the identification of detected phytoplasmas. Ribosomal groups 16SrI, 16SrII, 16SrIII, 16SrV, 16SrVI, and 16SrXII were identified. Moreover, a number of fruits produced from the first-generation seedlings showed precocious seed germination, and the young seedlings resulted as phytoplasma-positive. The seed transmission of phytoplasmas in eggplants for two subsequent generations highlights the risk of additional sources of infection of the disease represented by asymptomatic and infected seedlings in the presence of insect vectors. The seed transmission could explain the continuous presence of epidemic outbreaks of phytoplasmas in brinjal cultivations in several cultivation areas.