Epidemic Risk Research Articles

A method for characterizing disease emergence curves from paired pathogen detection and serology data

Abstract Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyse multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint‐analysis methods can more thoroughly analyse all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modelling approach that analyses multiple samples from individuals. We use ‘characterization maps’ to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and state. Our approach can also incorporate test sensitivity and specificity, and we propose model fit diagnostics. We motivate our approach through the need to use paired pathogen and antibody detection tests to estimate parameters and infection trajectories for the widely applicable susceptible, infectious, recovered (SIR) model. We contribute general formulas to link characterization maps to arbitrary process models and datasets and an extended SIR model that better accommodates paired data. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5 to 10 times fewer individuals. We use our method to study SARS‐CoV‐2 in wild white‐tailed deer (Odocoileus virginianus) from three counties in the United States. Estimates for average infectious times corroborate captive animal studies. The estimated average cumulative proportion of infected deer across the three counties is 73%, and the basic reproductive number (R0) is 1.88. Wildlife disease surveillance programs and research studies can use our methods to jointly analyse paired data to estimate epidemiological process parameters and track outbreaks. Paired data analyses can improve precision and accuracy when sampling is limited. Our methods use general statistical theory to let applications extend beyond the SIR model we consider and to more complicated examples of paired data. The methods can also be embedded in larger hierarchical models to provide landscape‐scale risk assessment and identify drivers of infection.

On Some families of Path-related graphs with their edge metric dimension

Locating the origin of diffusion in complex networks is an interesting but challenging task. It is crucial for anticipating and constraining the epidemic risks. Source localization has been considered under many feasible models. In this paper, we study the localization problem in some path-related graphs and study the edge metric dimension. A subset LE⊆VG is known as an edge metric generator for G if, for any two distinct edges e1,e2∈E, there exists a vertex a⊆LE such that d(e1,a)≠d(e2,a). An edge metric generator that contains the minimum number of vertices is termed an edge metric basis for G, and the number of vertices in such a basis is called the edge metric dimension, denoted by dime(G). An edge metric generator with the fewest vertices is called an edge metric basis for G. The number of vertices in such a basis is the edge metric dimension, represented as dime(G). In this paper, the edge metric dimension of some path-related graphs is computed, namely, the middle graph of path M(Pn) and the splitting graph of path S(Pn).

Key aspects of biosafety in modern animal husbandry

The relevance of this research is driven by the need to enhance biosecurity in animal husbandry to improve the health and productivity of livestock, as well as to safeguard human health. Biosecurity is a critical component that prevents the spread of infectious diseases, mitigates the risks of epidemics, reduces treatment costs, and increases productivity and economic efficiency within agricultural enterprises. In this regard, this study aimed to reveal the role of biosafety in preventive veterinary medicine. The leading approach to investigating this issue involved analysing existing practices and methods, which allows for a comprehensive examination of various aspects of biosecurity. The conducted analysis substantiated that preventive veterinary medicine is founded on the principle of disease prevention through risk reduction and the implementation of healthy practices. It has been established that this approach encompasses not only the treatment of sick animals but also the provision of advice to farmers on enhancing the health and productivity of their livestock. It has been established that biosecurity also helps prevent the emergence and spread of infectious diseases, reducing the need for antimicrobial drugs and lowering the risk of antimicrobial resistance. The research highlighted that in today’s rapidly changing world, biosecurity is becoming even more important due to population growth, urbanisation, climate change, and human health threats such as antimicrobial resistance. The concept of “One Health” is encompassed, emphasising the close connection and interdependence among the health of humans, domestic and wild animals, plants, and the environment. The core principles of biosecurity included segregation, cleaning and disinfection, and considering the behaviour and attitudes of people working with animals. The material presented in this article is of practical value to veterinarians, farmers, and livestock farms, assisting them in developing and implementing effective biosecurity plans adapted to changing conditions, thereby contributing to reducing the risks of infectious diseases and improving livestock productivity

Survival, mortality and epidemic risk status of COVID-19: a population-based Study in Golestan province, Iran

BackgroundAppreciating the various dimensions of the coronavirus disease 2019 (COVID-19) pandemic can improve health systems and prepare them to deal better with future pandemics and public health events. This study was conducted to investigate the association between the survival of hospitalized patients with COVID-19 and the epidemic risk stratification of the disease in Golestan province, Iran.MethodsIn this study, all patients with COVID-19 who were hospitalized in the hospitals of Golestan province of Iran from February 20, 2020, to December 19, 2022, and were registered in the Medical Care Monitoring Center (MCMC) system (85,885 individuals) were examined.The community's epidemic risk status (ERS) was determined based on the daily incidence statistics of COVID-19. The survival distribution and compare Survival in different subgroups was investigated using Kaplan–Meier and log-rank test and association between the survival and ERS by multiple Cox regression modeling.ResultsOut of 68,983 individuals whose data were correctly recorded, the mean age was 49 (SD = 23.98) years, and 52.8% were women. In total, 11.1% eventually died. The length of hospital stay was varying significantly with age, gender, ERS, underlying diseases, and COVID-19 severity (P < 0.001 for all). The adjusted hazard ratio of death for the ERS at medium, high, and very high-risk status compared to the low-risk status increased by 19%, 26%, and 56%, respectively (P < 0.001 for all).ConclusionsEnhancing preparedness, facilitating rapid rises in hospital capacities, and developing backup healthcare capacities can prevent excessive hospital referrals during health crises and further deaths.

Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis

BackgroundDengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China.MethodsWe analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk.ResultsBetween 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E–H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China.ConclusionsChina is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.Graphical

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.