Epidemic Risk Research Articles

On modelling airborne infection risk.

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiological model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet and behavioural properties of the individual. We calculate the time-dependent risk of being infected for two scenarios. We show how the epidemic infection risk depends on the viral latent period and the event time, the time infection occurs. Infection risk follows the dynamics of the infected population. As the latent period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher its risk of infection for equal exposure time to the pathogen is.

Optimization of "vehicle-UAV" joint distribution routing for cold chain logistics considering risk of epidemic spreading and green cost.

To address the epidemic, such as COVID-19, the government may implement the home quarantine policy for the infected residents. The logistics company is required to control the risk of epidemic spreading while delivering goods to residents. In this case, the logistics company often uses vehicles and unmanned aerial vehicles (UAVs) for delivery. This paper studies the distribution issue of cold chain logistics by integrating UAV logistics with epidemic risk management innovatively. At first, a "vehicle-UAV" joint distribution mode including vehicles, small UAVs and large UAVs, is proposed. The green cost for vehicles and UAVs is calculated, respectively. The formula for infection risk due to large numbers of residents gathering at distribution centers to pick up goods is then derived. Furthermore, based on the control of infection risk, an optimization model is developed to minimize the total logistics cost. A modified ant colony algorithm is designed to solve the model. The numerical results show that the maximum acceptable risk and the crowd management level of distribution centers both have significant effects on the distribution network, logistics cost and number of new infections. Our study provides a new management method and technical idea for ensuring the needs of residents during the epidemic.

Association of self-leadership and epidemic risk perception on quality of life in post-pandemic mainland of China: a cross-sectional study.

Self-leadership has proven to adjust individual psychological states and promote active behaviors to mitigate stress perception and negative lifestyle. This study aims to investigate the relationship between self-leadership, epidemic risk perception, and quality of life among the general public in post-pandemic mainland of China. Two online self-reported questionnaire surveys were carried out with 3,098 and 469 people in the Chinese mainland in February 2021 and December 2022, respectively. The univariate analysis, structural equation modeling, and fuzzy-set qualitative comparative analysis were used to analyze the data which was collected by Revised Self-Leadership Questionnaire, Perceived Risk of COVID-19 Pandemic Scale and World Health Organization Quality of Life Brief Scale. The Self-leadership was directly, moderately, and positively correlated with quality of life (Standardized path coefficients: 0.383 and 0.491, respectively; p < 0.05), and epidemic risk perception was negatively correlated with quality of life (Standardized path: 0.068 and 0.120, respectively; p < 0.05). The structural equation model for self-leadership, epidemic risk perception, and quality of life had a good fit (CFI = 0.957, 0.939 > 0.9; RSMEA = 0.058, 0.064 < 0.08, respectively) and was consistent across genders, educational levels, and types of occupations (Delata-CFI < 0.01). The core condition for achieving a high quality of life lies in maintaining a low level of self-punishment and a high level of self-cueing or a high level of self-punishment and a low level of self-cueing. In the post-epidemic era, the public can adjust their attitude toward stress by enhancing their self-leadership skills. Among various self-leadership skills, self-punishment or self-cueing may have the most significant impact on the quality of life.

Urban landscape pattern mainly drives the early epidemic distribution of dengue fever in Hangzhou, China

ContextA new dengue fever (DF) epidemic in Hangzhou, China has placed a serious burden on the urban sustainable development. However, the key drivers of DF epidemic are still unclear, especially the effects of urban landscape patterns.ObjectivesIn the present study, the spatial relationships between DF epidemic and urban landscape attributes in Hangzhou were investigated based on the framework and approach of landscape epidemiology.MethodsThe landscape indices of green space, waterbody, and built-up area were calculated based on land cover and land use (LULC) data. Population density, road density, GDP, and property price were used to represent socioeconomic conditions. The densities of social gathering places (SGPs) such as restaurants, malls, services, entertainment, traditional markets, and parks were recorded. A geographically weighted zero-inflated poisson regression (GWZIPR) model was applied to analyze the effects of LULC patterns, socioeconomic conditions, and SGPs on the risk of DF epidemics. An additional ZIPR-LASSO algorithm was used to explore the dominant drivers of spatial patterns of DF risk to construct the optimal model.ResultsDF cases were mainly clustered in central Hangzhou. Landscape patterns involving LULC and SGPs played a more important role in DF epidemic than the socioeconomic conditions. Among these factors, the total area of built-up area had the greatest effect to DF risk. Furthermore, property price, density of entertainment and services could better explain the spatial variation of DF risk in Hangzhou.ConclusionsOverall, the contribution of landscape patterns to the DF epidemic was more important than socioeconomic conditions within the urban region, especially in the new epidemic region. These findings revealed the key role of landscape epidemiology in public health management and landscape governance to improve the urban sustainable development and human well-being.

Investor sentiment and the holiday effect in the cryptocurrency market: evidence from China

This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market, using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1, 2017 to July 1, 2022. The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods. Additionally, we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns. However, when positive investor sentiment prevails in the cryptocurrency market, the holiday effect weakens, implying that positive investor sentiment attenuates the holiday effect. Robustness tests based on the Bitcoin market generate consistent results. Moreover, this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon. These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.

Climate change and resilience for antimicrobial stewardship and infection prevention.

This review covers recent research regarding the challenges posed by climate change within the areas of antimicrobial stewardship and infection prevention, and ways to build resiliency in these fields. Infectious disease patterns are changing as microbes adapt to climate change and changing environmental factors. Capacity for testing and treating infectious diseases is challenged by newly emerging diseases, which exacerbate challenges to antimicrobial stewardship and infection prevention.Antimicrobial resistance is accelerated due to environmental factors including air pollution, plastic pollution, and chemicals used in food systems, which are all impacted by climate change.Climate change places infection prevention practices at risk in many ways including from major weather events, increased risk of epidemics, and societal disruptions causing conditions that can overwhelm health systems. Researchers are building resilience by advancing rapid diagnostics and disease modeling, and identifying highly reliable versus low efficiency interventions. Climate change and associated major weather and socioeconomic events will place significant strain on healthcare facilities. Work being done to advance rapid diagnostics, build supply chain resilience, improve predictive disease modeling and surveillance, and identify high reliability versus low yield interventions will help build resiliency in antimicrobial stewardship and infection prevention for escalating challenges due to climate change.

The role of mGovernment applications in building trust during public crises: Evidence from the COVID-19 epidemic

Throughout the COVID-19 pandemic, China effectively contained the virus, resulting in increased public trust in the government. Mobile government (mGovernment) applications (apps) played a critical role in this improvement. This study aims to examine how mGovernment apps build citizens' trust in governments during public crises. The DeLone and McLean Information Systems Success Model (ISSM) is used to evaluate the quality of these apps. Public satisfaction serves as an intermediary variable, while public awareness of epidemic risks in sudden public relations crises acts as a moderating variable to explore the impact of mGovernment apps on government trust. Data analysis is conducted using SPSS Statistics 22.0 and AMOS 21.0. The study's results show that the system quality, information quality, and service quality of mobile government apps influence citizens' trust in governments through the mediating effect of public satisfaction. All three factors positively correlate with public satisfaction, with service quality having the greatest impact. Similarly, system quality, information quality, and service quality are positively correlated with public trust in governments, with system quality having the most noticeable influence. There is a strong correlation between public satisfaction and trust in governments, and the mediating effect of public satisfaction is significant. In addition, epidemic risk perception moderates the relationship between public satisfaction and citizens' trust in governments.

Analysis on the Social Demand and Training Path of the Aged Service Talents: Take Dazhou City of Sichuan Province as An Example

The training of senior service personnel is an important part of the development of senior service industry. The lack of senior service personnel seriously restricts the improvement of senior service quality. At present, there is a large gap in the demand for talents in the elderly care service, the talent structure is unreasonable, and the talent training mechanism is not perfect. In order to better promote the development of pension services, it is necessary to make plans for the construction of pension service talent team as soon as possible, increase preferential policies for the training of elderly service and nursing talents, and vigorously support local colleges and universities to apply for pension service management, nursing and rehabilitation therapy and other related majors, so as to realize the healthy and sustainable development of pension services. This report aims to investigate how can Adidas survive and improve in a consistently changing environment and strong competition. This report focuses on the multi-billion dollar shoe industry in 2021, with green, sustainable, and innovative themes. Sports and fashion will become the trend of the future industry. Adidas' global revenue is 21.23 billion euros, lagging behind the powerful Nike, with a growth rate of 16% compared to 2020, but still lagging behind the level before the 2019 pandemic. A turbulent environment, capturing consumer demand and supply chain management are the main risk issues. The company has made progress in climate change and SDG's, establishing a good brand image, but only 42% of supplier facilities met the standards and lacked the comprehensiveness of goals. They are committed to commercial product innovation, but there is a lack of internal innovation and innovative product sustainability. They opt for a detailed and systematic approach to supply chain management. They also pay attention to employee development and a fair environment, but lack concern for daily life. Adidas has a sound risk management system and has effectively responded to epidemic risks, but measures to address small and potential risks need to be improved. Because they have a certain business scale and continue to work hard, Adidas can survive in the constantly changing environment and competition, but surpassing Nike may require breakthroughs.

“Why Should I Be Scared?” Epidemic Uncertainties and Risk Construction in Emerging Infectious Disease Epidemics

ABSTRACT The onset of emerging infectious disease epidemics is marked by uncertainties regarding the nature of the causative pathogen, mode of transmission, and preventive measures among other factors. While scientists are developing knowledge on these subjects, people must adapt to these uncertainties. We explore risk construction in the epidemic context by conceptualizing risk as a knowledge construct and identifying the constitutive elements that influence its construction. Using qualitative interviews and comparing three locations in Ghana during the 2014–2016 Ebola epidemic in West Africa, we argue that the construction of risk is influenced by a combination of disease, networked information, and information on enabled factors—factors triggered by the epidemic context (geographic location, government policies, absence of confirmed cases). These enabled factors, we suggest, shape the disease risk characteristics that amplify or attenuate risk perception and influence health behavior.

Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.

Tight or Loose: Analysis of the Organization Cognition Process of Epidemic Risk and Policy Selection

In the context of Disease X risks, how governments and public health authorities make policy choices in response to potential epidemics has become a topic of increasing concern. The tightness of epidemic prevention policies is related to the effectiveness of the implementation of measures, while the organizational cognition of epidemic risks is related to the rationality of policy choices. During the three years of COVID-19, the Chinese government constantly adjusted the tightness of its prevention policies as awareness of the epidemic risk improved. Therefore, based on the epidemic risk organizational cognition model, the key nodes that affect the tightness of epidemic prevention policies can be explored to find the organizational behavior rules behind the selection of prevention policies. Firstly, through observing the adjustments made to the Chinese government’s prevention strategies during the epidemic, a time-series cross-case comparative analysis reveals how policy tightness shifted from stringent to lenient. This shift coincided with the organizational cognition of epidemic risk evolving from vague to clear. Secondly, by building the “knowledge-cognition” coordinate system to draw the organizational cognition spiral of epidemic risk, it is clear that the changes in the tightness of the prevention policies mainly came from the internalization and externalization of knowledge such as epidemic risk characteristics to promote the level of organizational cognition, which is manifested as expansion and deepening. Thirdly, the node changes in the interaction between organizational cognition development and policy choice proved that different stages of the epidemic had diverse environmental parameters. Moreover, as the epidemic nears its end, the focus of policy tightness is shifting from policy objectives to policy implementation around governance tools. The results indicate that organizational cognition of epidemic risk exhibits significant stages and periodicity. Additionally, epidemic risk characteristics, environmental coupling, and governance tools are crucial factors in determining the tightness of epidemic prevention policies.

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.

Epidemic features and megagenomic analysis of childhood Mycoplasma pneumoniae post COVID-19 pandemic: a 6-year study in southern China

ABSTRACT With the atypical rise of Mycoplasma pneumoniae infection (MPI) in 2023, prompt studies are needed to determine the current epidemic features and risk factors with emerging trends of MPI to furnish a framework for subsequent investigations. This multicentre, retrospective study was designed to analyse the epidemic patterns of MPI before and after the COVID-19 pandemic, as well as genotypes and the macrolide-resistance-associated mutations in MP sampled from paediatric patients in Southern China. Clinical data was collected from 1,33,674 patients admitted into investigational hospitals from 1 June 2017 to 30 November 2023. Metagenomic next-generation sequencing (mNGS) data were retrieved based on MP sequence positive samples from 299 paediatric patients for macrolide-resistance-associated mutations analysis. Pearson's chi-squared test was used to compare categorical variables between different time frames. The monthly average cases of paediatric common respiratory infection diseases increased without enhanced public health measures after the pandemic, especially for influenza, respiratory syncytial virus infection, and MPI. The contribution of MPI to pneumoniae was similar to that in the outbreak in 2019. Compared to mNGS data between 2019–2022 and 2023, the severity of MP did not grow stronger despite higher rates of macrolide-resistance hypervariable sites, including loci 2063 and 2064, were detected in childhood MP samples of 2023. Our findings indicated that ongoing surveillance is necessary to understand the impact of post pandemic on MP transmission disruption during epidemic season and the severity of clinical outcomes in different scenarios.

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.

Detection of DENV-1, DENV-3, and DENV-4 Serotypes in Aedes aegypti and Aedes albopictus, and Epidemic Risk in the Departments of Oueme and Plateau, South-Eastern Benin.

Background: This study conducted in the departments of Oueme and Plateau aims to assess the presence of the dengue virus and its different serotypes in Aedes aegypti and Aedes albopictus, as well as the epidemic risk incurred by the populations. Methods: Collections of adult mosquitoes using human landing catches (HLC) were carried out in six communes, three (Porto-Novo, Adjarra, and Avrankou) in the Oueme department and the rest (Ifangni, Kétou, and Pobè) in the Plateau department. Pools of ten Aedes mosquitoes were formed, and stored at -80°C in RNA later. RT-PCR was used to detect dengue virus, and conventional PCR for the different serotypes. Inspection of water containers and collection of Aedes larvae was performed inside and around each house to calculate the stegomyan indices. Results: In the six communes, the dengue virus was present both in Ae. aegypti and Ae. albopictus. Combined data of the two Aedes species at the communes level revealed infection rates ranging from 80.00% (95% CI: 61.43-92.29) to 96.67% (95% CI: 82.78-99.92). In all the communes, the values of stegomyan indices reached the WHO threshold, which indicates the existence of the risk of an arbovirus epidemic. In addition, the infection rates were similar for Ae. aegypti [88.19% (95% CI: 81.27-93.24)] and Ae. albopictus [86.79% (95% CI: 74.66-94.52)]. The three virus serotypes detected in the pools of Aedes were DENV-1, DENV-3, and DENV-4, with a high prevalence for the first two. Conclusion: This study revealed that three serotypes (DENV-1, DENV-3, and DENV-4) of dengue virus circulate in Ae. aegypti and Ae. albopictus in the departments of Oueme and Plateau. Moreover, the risk of transmission of arboviruses was globally high and variable from commune to commune. This information is essential for informed decision-making in the preventive control of the disease.

Global warming significantly increases the risk of Pierce’s disease epidemics in European vineyards

Pierce’s disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world’s largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above +2∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,2\\,^{\\circ }\\hbox {C}$$\\end{document} in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above +3∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,3\\,^{\\circ }\\hbox {C}$$\\end{document} for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry.

Development of a TaqMan Real-Time PCR for Early and Accurate Detection of Anthracnose Pathogen Colletotrichum siamense in Pachira glabra.

Anthracnose, caused by Colletotrichum siamense, is a destructive disease of Pachira glabra in southern China. Early and proper monitoring and quantification of C. siamense is of importance for disease control. A calmodulin (CAL) gene-based TaqMan real-time PCR assay was developed for efficient detection and quantification of C. siamense, which reliably detected as low as 5 pg of genomic DNA and 12.8 fg (5800 copies) of target DNA. This method could specifically recognize all tested C. siamense isolates, while no amplification was observed in other closely related Colletotrichum species. The assay could still detect C. siamense in plant mixes, of which only 0.01% of the tissue was infected. A dynamic change in the amount of C. siamense population was observed during infection, suggesting that this real-time PCR assay can be used to monitor the fungal growth progression in the whole disease process. Moreover, the method enabled the detection of C. siamense in naturally infected and symptomless leaves of P. glabra trees in fields. Taken together, this specific TaqMan real-time PCR provides a rapid and accurate method for detection and quantification of C. siamense colonization in P. glabra, and will be useful for prediction of the disease to reduce the epidemic risk.

A View on technology and controls for epidemic preparedness and the growth of COVID-19 disease in Saudi Arabia

This work emphasizes that the best chance of ending the pandemic lies in the Measures that can help us respond to these epidemic threats more effectively than before in the scope of protection from epidemic risks that we were not aware of before the outbreak of the disease, which is to benefit from the study of previous experiences. The work show the importance of technology about how to prepare for and handle any pandemic by discussing a successful case from the past. We examine Saudi Arabia's COVID-19 pandemic response experiences. We proposed a case evolution study from the southern region of Jazan to demonstrate the consequences of these techniques. Data on the daily Jazan COVID-19 infection curve collected from July 1st to September 30th, 2020, was processed using multiple Python software modules, such as encoder-decoder LSTM, 1D-CNN, and Complex network analysis of time series to identify changes within the infection curve's structure. A Gaussian modeling study was performed to compare the data movement with the protection from epidemic risks computed by stratification based on the data group.The network analysis shows that hubs are stable while medium and low-degree nodes are unstable. Also, from the perspective of Gaussian modeling, the COVID-19 infection curve parts indicate that the pandemic in Saudi Arabia is about to peak and move into the second (declining) half of the bell-shaped distribution.Technology in many areas and internet access make Saudi Arabia's social separation strategies a pandemic-eradication success story

Present and future situation of West Nile virus in the Afro‐Palaearctic pathogeographic system

West Nile virus (WNV) is a globally widespread arthropod‐borne virus that poses a significant public health concern. Mosquitoes transmit the virus in an enzootic cycle among birds, which act as reservoirs. Climate plays a crucial role in these outbreaks as mosquitoes are highly influenced by climatic conditions, and bird migrations are also affected by weather patterns. Consequently, changes in climate can potentially impact the occurrence of WNV outbreaks. We used biogeographic modelling based on machine learning algorithms and fuzzy logic to analyse and evaluate separately the risk of WNV outbreaks in two different biogeographic regions, the Afrotropical and the Western Palaearctic region. By employing fuzzy logic tools, we constructed a comprehensive risk model that integrates the Afro‐Palaearctic system as a unified operational unit for WNV spread. This innovative approach recognizes the Afro‐Palaearctic region as a pathogeographic system, characterized by biannual connections facilitated by billions of migratory bird reservoirs carrying the disease. Subsequently, we forecasted the effects of different climate change scenarios on the spread of WNV in the Afro‐Palaearctic system for the years 2040 and 2070. Our findings revealed an increasing epidemic and epizootic risk south of the Sahara. However, the area where an upsurge in risk was forecasted the most lies within Europe, with the anticipation of risk expansion into regions presently situated beyond the virus' distribution range, including central and northern Europe. Gaining insight into the risk within the Afro‐Palaearctic system is crucial for establishing coordinated and international One Health surveillance efforts. This becomes particularly relevant in the face of ongoing climate change, which disrupts the ecological equilibrium among vectors, reservoirs, and human populations. We show that the application of biogeographical tools to assess risk of infectious disease, i.e. pathogeography, is a promising approach for understanding distribution patterns of zoonotic diseases and for anticipating their future spread.

Risk control of epidemic in urban cold-chain transportation

The COVID-19 pandemic has severely disrupted the daily running of urban logistics system, thereby increasing the resilience and health requirements in the design of sustainable logistics system. Most scientific contributions have enriched the knowledge of how to deal with viral infection threats from the external environment but ignored the virus transmission from fomites within the logistics system. This study designs an urban cold-chain logistic network with control strategies to reduce the epidemic risk caused by cold-chain fomites while concurrently balancing the total cost and customer satisfaction, which is modeled as a multi-objective vehicle routing problem (MOVRP). The control strategies are modeled by dividing customer points into small groups, and the virus transmission in populations is considered when evaluating epidemic risk. A multi-objective evolutionary algorithm with special initialization (MOEA-SI) is proposed to solve this problem. Simulation results show that all the strategies can efficiently reduce the epidemic risk with reasonable expense of total cost and customer satisfaction. The introduction of special initialization strategy enables the MOEA-SI to have good performance in balancing the three objectives. The above results can enrich the knowledge of how to deal with unexpected epidemics and help the scientific decision-making in sustainable urban management.