Epidemic Outbreaks Research Articles

Spatiotemporal Variations of COVID-19 Variants in 100 Countries: Exploring Spatial Patterns and Time-Lag Effects

Learning from the COVID-19 pandemic is vital for future global health crises. Forecasting pandemic spread across countries in the presence of viral mutations, such as SARS-CoV-2 variant strains, remains a challenge. Previous studies indicate spatial and temporal regularities in SARS-CoV-2 variant distribution, yet limitations persist: (1) static geographical patterns overlook dynamic changes and their causes, and (2) the association between temporal variations in SARS-CoV-2 lineages and epidemic outbreaks is underexplored. To address these gaps, we propose an analysis framework using more than 10 million SARS-CoV-2 genome sequence metadata from 100 countries. Our framework identifies spatial heterogeneity patterns in the relative frequency of SARS-CoV-2 variants through clustering, examines factors influencing spatial heterogeneity using explainable machine learning, and analyzes the time lag effect of temporal variation on COVID-19 infection waves based on spatial patterns. Our findings demonstrate the following: (1) The distribution of SARS-CoV-2 variant strains among 100 countries exhibits a spatial pattern characterized by geographic proximity, with observed dynamic changes. (2) Bilateral distance consistently dominates, and factors related to globalization and response policy contribute to nongeographic proximity and temporal variation of the spatial pattern. (3) Leveraging this spatial pattern allows for the forecast of time lags (mainly ranging from twenty to thirty-eight days) between the emergence of new strains and subsequent infection waves. Our study clarifies the spatial pattern and time lag effect of the dynamic distribution of SARS-CoV-2 variant strains. It offers a spatial basis and temporal reference for early warning in future global public health crises from a geographical perspective.

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) & 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.

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.

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...

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

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.

Characterising information gains and losses when collecting multiple epidemic model outputs

BackgroundCollaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. MethodsWe compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. ResultsBy collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. ConclusionsWe observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.

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.

Mathematics of Epidemics: On the General Solution of SIRVD, SIRV, SIRD, and SIR Compartment Models

The susceptible–infected–recovered–vaccinated–deceased (SIRVD) epidemic compartment model extends the SIR model to include the effects of vaccination campaigns and time-dependent fatality rates on epidemic outbreaks. It encompasses the SIR, SIRV, SIRD, and SI models as special cases, with individual time-dependent rates governing transitions between different fractions. We investigate a special class of exact solutions and accurate analytical approximations for the SIRVD and SIRD compartment models. While the SIRVD and SIRD equations pose complex integro-differential equations for the rate of new infections and the fractions as a function of time, a simpler approach considers determining equations for the sum of ratios for given variations. This approach enables us to derive fully exact analytical solutions for the SIRVD and SIRD models. For nonlinear models with a high-dimensional parameter space, such as the SIRVD and SIRD models, analytical solutions, exact or accurately approximative, are of high importance and interest, not only as suitable benchmarks for numerical codes, but especially as they allow us to understand the critical behavior of epidemic outbursts as well as the decisive role of certain parameters. In the second part of our study, we apply a recently developed analytical approximation for the SIR and SIRV models to the more general SIRVD model. This approximation offers accurate analytical expressions for epidemic quantities, such as the rate of new infections and the fraction of infected persons, particularly when the cumulative fraction of infections is small. The distinction between recovered and deceased individuals in the SIRVD model affects the calculation of the death rate, which is proportional to the infected fraction in the SIRVD/SIRD cases but often proportional to the rate of new infections in many SIR models using an a posteriori approach. We demonstrate that the temporal dependence of the infected fraction and the rate of new infections differs when considering the effects of vaccinations and when the real-time dependence of fatality and recovery rates diverge. These differences are highlighted for stationary ratios and gradually decreasing fatality rates. The case of stationary ratios allows one to construct a new powerful diagnostics method to extract analytically all SIRVD model parameters from measured COVID-19 data of a completed pandemic wave.

A time of decline: An eco-anthropological and ethnohistorical investigation of mpox in the Central African Republic.

The Central African Republic (CAR) has experienced repeated mpox outbreaks since 2001. Although several mpox epidemiological risk factors for zoonotic and interhuman transmission have been documented, the reasons for more frequent epidemic outbreaks are less well understood, relying on vague explanatory categories, including deforestation, hunting, and civil unrest. To gain insight into increasingly frequent outbreaks, we undertook an ethnohistorical, eco-anthropological analysis in two CAR regions: the Lobaye prefecture, experiencing one or more annual outbreaks in the past decade, and the Sangha-Mbaere prefecture, with a longer history of mpox but less frequent outbreaks. We comparatively examined changing political economies, forest use practices, and understandings of mpox. In 2022, we conducted 40 qualitative ethnohistorical, anthropological interviews and participant-observation of forest activities in two languages (Sango and French). We compared contemporary practices with hunting, trapping, and meet consumption practices, documented through quantitative and qualitative observation in one research site, over 6 months in 1993. We find increased rodent capture and consumption in both sites in the past 30 years and expanded practices of other potentially risky activities. Simultaneously, we also identify important differences in risky practices between our Lobaye and Sangha-Mbaere participants. In addition, Lobaye and Sangha participants underscored historical processes of decline producing mpox among other emergences, but they framed these declension processes diversely as economic, political, nutritional, and moral. Our findings are important because they mobilize new types of evidence to shed light on the processual dynamics of mpox outbreaks in the CAR. This study also reveals variability across two sites within the same country, highlighting the importance of comparative, fine-grained anthropological and historical research to identify underlying dynamics of mpox outbreaks. Finally, our study points to the need for mpox interventions and risk communication accounting for these regional differences, even within a single country.

A Two-Objective Model for the Multilevel Supply Chain of Blood Products with the Approach of Reducing the Rate of Contagion under the (COVID-19) Epidemic Outbreak Conditions

The conditions of the coronavirus epidemic have put much pressure on the healthcare system. This disease has hurt the blood supply through the reduction of blood donation and the reduction of access to suitable collection facilities due to dysfunction. Considering the importance of the subject, the purpose of this paper is to design a two-level supply chain network for blood products with the approach of reducing costs and the rate of contagion under the conditions of epidemic outbreaks (COVID-19). After examining the solution methods for multilevel supply chain networks of blood products under the conditions of the spread of the COVID-19 virus, three exact solution methods, including LP-metric, an improved version of the augmented ε-constraint (AUGMECON2), and an improved weighted Chebyshev, are proposed. They are used to solve the model in small dimensions. In order to compare the methods in the obtained solutions, several numerical examples of different sizes are generated and solved. Then, using the statistical assumption test, the obtained results are compared in all numerical examples by Tukey’s technique. Also, the TOPSIS is applied to select the best method. Finally, in order to investigate the reaction of the objectives to the changes in the contagion probability parameter, a sensitivity analysis has been performed. The results emphasize that improving the performance of the blood supply chain (BSC) can lead to a reduction in BSC costs and improved service to patients. Also, the adaptation of different components of the BSC and regular coordination between them play an efficient role in controlling and improving this disease and reducing the costs of the BSC. Also, receiving the plasma product of recovered people from type (II) donors can play a vital role in reducing the percentage of disease transmission.