Spread Of Influenza Research Articles

Preparing for pandemic response in the context of limited resources

AbstractPandemics are outbreaks of an infectious disease that increase morbidity and mortality over a wide geographic area. These events require large‐scale interventions from governments and health agencies to curtail transmission within a population. In assessing the impact of these interventions, policymakers avail themselves of simulations from compartmental models. However, it is commonplace that modellers employ fixed fractions to represent interventions, which implicitly assume that resources instantaneously and boundlessly adjust to the desired target and may predict overly optimistic outcomes. Here, we propose a model that integrates four crucial countermeasures subject to resource constraints. We present this structure utilising the concept of small models and frame the simulations in the context of a hypothetical spread of influenza in the Netherlands, drawing on empirical data. After incorporating resource constraints, we find that only a comprehensive response controls the disease. These findings highlight the need for improving strategic plans to support pandemic preparedness. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study

BackgroundHospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.AimThis study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.MethodsThis retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011–2012 and 2019–2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.ResultsPatients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.ConclusionThis study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1

BackgroundInfluenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. MethodsThe generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. ResultsConsidering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. ConclusionsOur study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

THE IMMUNITY DURATION AND INTENSITY IN INDUSTRIAL LAYING HENS FOLLOWING VACCINATION WITH INACTIVATED H5N1 AVIAN INFLUENZA VACCINE

After the severe AIV H5 outbreak in Kazakhstan in 2020 the extensive use of AIV H5 vaccines started in the industrial poultry farms to limit the H5N1 influenza spread. Traditional methods, such as stamping out are no longer a viable option in countries where Highly Pathogenic Avian Influenza (HPAI) has become endemic. However, available vaccines and vaccination protocols have been widely researched in laboratory conditions, and limited testing has been conducted on commercial layers for the immunity persistence in field conditions. Immunity persistence after AIV vaccination can be quite different between laboratory and field conditions. Our basic goal was to assess the intensity and duration of immunity in commercial layers following 1, 2 and 3 vaccinations. H5N1 hemagglutination inhibition (HI) antibodies were observed 370 days after vaccination of chickens using three schemes of vaccination. However, at 370 days post vaccination 1 experimental group obtaining one vaccination had comparatively low HI titers (mean titer 6,5±1,2 log2), the other two groups having two and three vaccinations showed 8,8±2,4 mean log2 and 9,4±2,2 mean log2 respectively. The described results showed that inactivated H5N1 vaccine can produce lengthy, intense and homogenous immunity under field conditions following 2 and 3 vaccinations.

Rich dynamics of a delayed Filippov avian-only influenza model with two-thresholds policy

A nonsmooth Filippov avian-only influenza model with threshold strategies of culling susceptible and/or infected birds under different conditions is proposed to control the spread of avian influenza. The time delay representing the incubation period of avian influenza is incorporated into our model to make it more realistic, which is different from the traditional Filippov model. The stability of various types of equilibria and the existence of Hopf bifurcation are researched. Moreover, the existence of the sliding mode and its dynamics are investigated by Filippov convexity method. The theoretical analysis and numerical simulations indicate that, according to the value of thresholds and time delay, all solutions eventually converge to the regular equilibrium, pseudoequilibrium or stable periodic solution. We also indicate that time delay has a great influence on the sliding mode. Due to the many factors involved, and our main purpose is to study the impact of time delay on system stability, we have only conducted a simple analysis of the impact of time delay on the sliding mode. Furthermore, the boundary bifurcation switching stable regular equilibrium or stable limit cycle to a stable pseudoequilibrium can be exhibited by numerical simulations. Finally, with the increase of time delay, the global bifurcations from grazing bifurcation to buckling bifurcation and then to cross bifurcation are obtained. Our results indicate that although Filippov control strategies can effectively control the number of infected birds in many cases, the existence of time delay may challenge influenza control by the emergence of buckling bifurcation and cross bifurcation.

Can we predict multi-party elections with Google Trends data? Evidence across elections, data windows, and model classes

Google trends (GT), a service aggregating search queries on Google, has been used to predict various outcomes such as as the spread of influenza, automobile sales, unemployment claims, and travel destination planning [1, 2]. Social scientists also used GT to predict elections and referendums across different countries and time periods, sometimes with more, sometimes with less success. We provide unique evidence on the predictive power of GT in the German multi-party systems, forecasting four elections (2009, 2013, 2017, 2021). Thereby, we make several contributions: First, we present one of the first attempts to predict a multi-party election using GT and highlight the specific challenges that originate from this setting. In doing so, we also provide a comprehensive and systematic overview of prior research. Second, we develop a framework that allows for fine-grained variation of the GT data window both in terms of its width and distance to the election. Subsequently, we test the predictive accuracy of several thousand models resulting from those fine-grained specifications. Third, we compare the predictive power of different model classes that are purely GT data based but also incorporate polling data as well as previous elections. Finally, we provide a systematic overview of the challenges one faces in using GT data for predictions part of which have been neglected in prior research.

N-Glycan Profiles of Neuraminidase from Avian Influenza Viruses.

The cleavage of sialic acids by neuraminidase (NA) facilitates the spread of influenza A virus (IV) descendants. Understanding the enzymatic activity of NA aids research into the transmission of IVs. An effective method for purifying NA was developed using p-aminophenyloxamic acid-modified functionalized hydroxylated magnetic particles (AAMPs), and from 0.299 to 0.401 mg of NA from eight IV strains was isolated by 1 mg AAMP. A combination of lectin microarrays and MALDI-TOF/TOF-MS was employed to investigate the N-glycans of isolated NAs. We found that more than 20 N-glycans were identified, and 16 glycan peaks were identical in the strains derived from chicken embryo cultivation. Multi-antennae, bisected, or core-fucosylated N-glycans are common in all the NAs. The terminal residues of N-glycans are predominantly composed of galactose and N-acetylglucosamine residues. Meanwhile, sialic acid residue was uncommon in these N-glycans. Further computational docking analysis predicted the interaction mechanism between NA and p-aminophenyloxamic acid.

Cost effectiveness of preemptive school closures to mitigate pandemic influenza outbreaks of differing severity in the United States

BackgroundNonpharmaceutical interventions (NPIs) may be considered as part of national pandemic preparedness as a first line defense against influenza pandemics. Preemptive school closures (PSCs) are an NPI reserved for severe pandemics and are highly effective in slowing influenza spread but have unintended consequences.MethodsWe used results of simulated PSC impacts for a 1957-like pandemic (i.e., an influenza pandemic with a high case fatality rate) to estimate population health impacts and quantify PSC costs at the national level using three geographical scales, four closure durations, and three dismissal decision criteria (i.e., the number of cases detected to trigger closures). At the Chicago regional level, we also used results from simulated 1957-like, 1968-like, and 2009-like pandemics. Our net estimated economic impacts resulted from educational productivity costs plus loss of income associated with providing childcare during closures after netting out productivity gains from averted influenza illness based on the number of cases and deaths for each mitigation strategy.ResultsFor the 1957-like, national-level model, estimated net PSC costs and averted cases ranged from $7.5 billion (2016 USD) averting 14.5 million cases for two-week, community-level closures to $97 billion averting 47 million cases for 12-week, county-level closures. We found that 2-week school-by-school PSCs had the lowest cost per discounted life-year gained compared to county-wide or school district–wide closures for both the national and Chicago regional-level analyses of all pandemics. The feasibility of spatiotemporally precise triggering is questionable for most locales. Theoretically, this would be an attractive early option to allow more time to assess transmissibility and severity of a novel influenza virus. However, we also found that county-wide PSCs of longer durations (8 to 12 weeks) could avert the most cases (31–47 million) and deaths (105,000–156,000); however, the net cost would be considerably greater ($88-$103 billion net of averted illness costs) for the national-level, 1957-like analysis.ConclusionsWe found that the net costs per death averted ($180,000-$4.2 million) for the national-level, 1957-like scenarios were generally less than the range of values recommended for regulatory impact analyses ($4.6 to 15.0 million). This suggests that the economic benefits of national-level PSC strategies could exceed the costs of these interventions during future pandemics with highly transmissible strains with high case fatality rates. In contrast, the PSC outcomes for regional models of the 1968-like and 2009-like pandemics were less likely to be cost effective; more targeted and shorter duration closures would be recommended for these pandemics.

Highly pathogenic influenza H5: risks of a new pandemic strain developing

The review provides brief historical information on the highly pathogenic influenza A (H5) and presents data on massive outbreaks caused by this subtype. It includes historical background, data on outbreaks, and the formation of potentially pandemic viruses. The abstract also highlights the current state of the problem, such as the spread of H5 influenza in new territories and its ability to infect mammals and humans. The issues of vaccination for poultry and the challenges of implementing it globally are addressed. Furthermore, the risks of further spread of the virus and the emergence of pandemic strains are being discussed.

Spread of avian influenza among poultry specialists in England during winter 2022/23: National poultry housing order and environmental drivers

PurposeTo examine the impact of the national poultry housing order the UK government introduced on 7 November 2022 on the spreading of the avian influenza virus among poultry premises. MethodsA longitudinal design with 15 weeks of infected poultry specialist incidence rates per 100 poultry specialists during the 2022/23 winter for 8 English regions. A multilevel regression model was used to analyse repeated measurements. Time was level-1 unit and regions level-2 unit resulting in 120 observations. Random intercept models included interactions between housing order and weekly infected wild birds, poultry density, or weekly average temperatures divided into terciles. In models where these variables were not included as an interaction term they were introduced as confounders. ResultsAfter the introduction of the housing order, it took 3 weeks for a considerable reduction in poultry specialist incidence rates. Reduction in incidence rates was strongest in regions with highest poultry density, from 1.27 (95%CI 0.99 to 1.56) to 0.30 (95%CI 0.09 to 0.52). Considerable reductions were also seen in regions with most detected infected wild birds. ConclusionThe housing order was successful in reducing infected poultry specialist incidence rates three weeks after its introduction. Strongest impact in regions with highest poultry density.

CURRENT SITUATION WITH AVIAN INFLUENZA, ASSESSMENT OF RISKS FOR TRANSMISSION AND SPREAD IN THE REPUBLIC OF BELARUS

The article presents the current situation with avian influenza, showing the global spread of this disease, a high degree of morbidity and mortality not only among poultry and wild birds, but also among various species of wild and domestic, aquatic and terrestrial mammals. Increasing cases of disease among the population caused by both highly pathogenic and low pathogenic strains of the avian influenza virus have been described. An analysis and assessment of the importance of wild fauna in the spread of the avian influenza pathogen was carried out, and the level of risk for introduction and spread of avian influenza in the territory of the Republic of Belarus was assessed.

The epizootic situation of avian influenza in the Kaluga region

The article discusses the problems associated with increased virulence of strains due to the high representativeness of the sample of susceptible organisms, the spread of avian influenza and its natural reservoirs. Currently, avian influenza virus strains are divided into HPAI (highly pathogenic) and LPAI (low pathogenic). Registered outbreaks among wild and domestic birds and cases of human infection with influenza viruses of subtypes H5N8, H7N9 and H9N2 indicate an active mutation of the virus and the appearance of “aggressive” strains that can infect not only poultry, but also humans. This article analyzes the current epizootic situation of avian influenza in the Kaluga region for 2022-2023.

A label-free H1N1 influenza virus immunosensor based on an N-LIG/Au laser induced graphene microelectrode.

A label-free immunosensor based on an N-doped laser direct graphene (N-LIG)/Au electrode was proposed for H1N1 influenza virus detection. By utilizing the instantaneous high temperature of laser irradiation, N atoms are generated by the decomposition of melamine dripped onto the surface of an LIG electrode to obtain N-LIG with higher conductivity. The doping of N atoms provides a large number of active sites for LIG microelectrodes. Combined with the electrodeposition of Au NPs, and covalently crosslinking antibodies, a simple, highly sensitive and stable immunosensing interface is constructed. The proposed H1N1 influenza virus immunosensor has a detection range of 0.01 fg mL-1 to 10 ng mL-1 with a detection limit as low as 0.004 fg mL-1. The constructed sensor has ultra-high sensitivity and good selectivity and can be used for complex biological sample analysis, with potential application prospects in preventing the large-scale spread of influenza. Taking advantage of N-LIG electrode's properties will provide opportunities for developing portable electrochemical biosensors for health and environmental applications.

Analysis of factors influencing influenza outbreaks in schools in Taicang City, China.

This study aims to analyze the awareness of influenza prevention and control and the behavioral attitudes toward the work among parents and staff in schools in Taicang City and the impact of the vaccination rate among students on influenza outbreaks in schools. The findings can provide references for the development of effective control strategies for the spread of influenza. An anonymous questionnaire survey was conducted on 10,962 students from 20 schools in Taicang City, with class as the unit of analysis. The survey investigated their awareness of influenza prevention and control, their attitudes, and the vaccination coverage. From January to June 2023, a total of 388 influenza outbreaks were reported in schools in Taicang City, involving 77 schools. There were 3,475 confirmed cases, with an average infection rate of 18.53%. In schools where influenza outbreaks had occurred, the incidence rate of those who received influenza vaccine was significantly lower than those who did not, and the vaccine protection rate was 28.22%. The knowledge awareness rates of "the main transmission routes of influenza" and "influenza vaccination can prevent influenza" among parents of students were 95.49 and 93.16%, respectively. The differences between schools involved in the epidemic and non-epidemic were statistically significant (p < 0.05). The correct attitudes of parents toward "actively reporting relevant symptoms to teachers when their children show symptoms" and "avoiding classes with diseases when their children are suspected to be sick" are 98.80 and 96.26%, respectively. The differences between schools with and without epidemic are statistically significant (p < 0.05). The correct attitudes of the class teacher toward "correct management and control of students with flu like symptoms in the class" and "taking correct prevention and control measures in the event of a flu epidemic in the class" were 89.36 and 92.55%, respectively. The differences between epidemic related and non-epidemic related classes were statistically significant (p < 0.05). Enhance the knowledge level of influenza prevention and control among parents of students, Strengthening the training for class teachers in emergency response to infectious diseases and increasing vaccination coverage among students can effectively reduce the incidence of influenza and thereby the occurrence of cluster outbreaks in schools.

Mitigating Risk: Predicting H5N1 Avian Influenza Spread with an Empirical Model of Bird Movement

Mitigating Risk: Predicting H5N1 Avian Influenza Spread with an Empirical Model of Bird Movement

Global avian influenza situation (2019–2022). Host range expansion asevidence of high pathogenicity avian influenza virus evolution

High pathogenicity avian influenza has a significant negative impact on poultry farming, international trade and health of wild bird populations, therefore the infection requires the utmost attention of the entire international community. The article investigates the evolutionary and epidemic processes observed in recent years in many countries of the world where avian influenza outbreaks occur; describes the ways of the infection spread; the prevalence of the virus types for the last several years, as well as the expansion of the host range, including among representatives of the Mammalia class. The change in the ratio between the virus types starting from 2020, when H5N8 subtype was responsible for the overwhelming number of the disease outbreaks reported, until 2022, when an obvious predominance of H5N1 subtype was detected is demonstrated. A noticeable expansion of the disease-affected areas in Central and South America, the influence of migration, anthropogenic and other factors on influenza spread are highlighted. The conditions facilitating the occurrence of the infection outbreaks affecting mammals, wild animals and livestock, zoo and companion animals are described. Cases of mammals’ infection on the North American and Eurasian continents in zoos, nature parks, backyards and fur farms, asa rule, coincide in time with the infection outbreaks in waterfowl populations. The WAHIS data were analyzed and the high ability of the virus to spillover from birds to mammals, such as martens (minks, otters, ferrets, badgers), cats (domestic cats, cougars, leopards, lynxes), pinnipeds (common seals, grey seals), bears (brown, grizzly, American black), bottlenose dolphins, skunks, foxes, opossums, raccoons was demonstrated. Changes in the habitats of both migratory birds and mammals, including due to some human economic activities, add an ecological and urban component to the complex task of the control and prevention of the epidemic, also posing a potential threat to humans.

Attitudes of healthcare professionals in primary healthcare about influenza vaccination

Introduction: Influenza is a viral respiratory disease. The most effective way to counteract that disease is by vaccination. Due to aerosol transmission of the virus, its virulence, and the amount of patient–healthcare worker contact in primary care, it is highly recommended for both patient and healthcare worker to get vaccinated.Aim: The study aims to investigate attitudes about flu vaccination among primary healthcare professionals.Material and methods: Quantitative methodology was used in the descriptive cross-sectional survey. That was carried out using a survey questionnaire in 12 healthcare centers around Styria in Slovenia, where the target group of the survey was healthcare professionals in health centers. The data was processed using descriptive statistics, calculating average values, and presented in tables and graphs.Results: Vaccination among healthcare professionals in healthcare centers was found to be higher compared to other studies. They are vaccinated primarily to protect themselves, their family, and their patients. The ones not vaccinated choose to opt out due to personal beliefs regarding their health and the side effects caused by the vaccine. It was also established that free vaccination would contribute greatly to increased interest, as well as vaccination during working hours and a higher number of vaccination sites. Discussion: Based on our results, only a third of healthcare professionals in health centers are vaccinated regularly each year. As vaccination rates are low among healthcare professionals, raising awareness among them and the general population regarding the importance of flu vaccination is of great importance.Conclusion: The vaccination of healthcare professionals in health centers is quite good; however, there is some space for improvement in vaccination coverage. Further prospective research on this topic is needed to obtain a broader aspect of the vaccination problem. In addition to vaccination, we can prevent the spread of influenza through other safeguards and good hygiene habits.

Mathematical modeling of the infectious spread and outbreak dynamics of avian influenza with seasonality transmission for chicken farms

A compartmental model with a time-varying contact rate, the seasonality effect, and its corresponding nonautonomous model are investigated. The model is developed based on the six compartments: susceptible, latent, infected, asymptomatic, treated, and recovered individuals. We determine the effective reproduction number for this nonautonomous system, and analytic discussion shows that at least one positive periodic solution exists for R0>1. The model is simulated using the RK-45 numerical method, and the parameter values for the model are taken from the available literature. From the numerical results, we observe that the degree of seasonality and vaccine efficacy significantly impact the amplitude of the epidemic curve. The latent-infected phase plane shows that periodic solutions exhibit a period-doubling bifurcation as the amplitude of seasonality increases. Finally, the model outcome was compared with the actual field data and found to be consistent.

Resurgence of influenza during COVID-19 in Chongqing, China: A retrospective analysis.

To better understand the trends of influenza and the impact of public health and social measures (PHSMs) implemented during the coronavirus disease 2019 (COVID-19) period in Chongqing, China. Data from the China Influenza Surveillance Information System from January 2017 to June 2022 were extracted. Epidemiological characteristics (influenza-like illness [ILI] and ILI%) and virological characteristics (influenza positive rate and circulating (sub)types) of influenza were described and compared between the pre-COVID-19 period and the COVID-19 period. Our survey showed that the implementation of PHSMs during the COVID-19 period had a positive impact on reducing influenza transmission. However, influenza activity resurged in 2021-2022 as the PHSMs were eased. Children under 5 years old constituted the highest proportion of ILI cases. The overall influenza positive rate was 23.70%, with a higher rate observed during the pre-COVID-19 period (31.55%) compared to the COVID-19 period (13.68%). Influenza virus subtypes co-circulated and the predominant subtype varied each year, with influenza A subtypes predominated in 2018/2019, while influenza B/Victoria lineage dominated in 2020/2021. PHSMs are effective measures to mitigate the spread of influenza. The findings underscore the need for bolstering monitoring systems, advocating influenza vaccination, and implementing practical PHSMs to strengthen prevention and control measures against influenza.

Sliding dynamics and optimal control of avian influenza model with saturated incidence rate

AbstractIn this paper, we develop a susceptible‐infective‐susceptible‐infected‐recovered avian influenza model with saturated incidence rate and three control measures, which is proposed to evaluate the comprehensive effects of different control measures on the spread of avian influenza. Based on the threshold policy, the threshold level is used as the reference index of whether to take control measures. No control measures will be taken when the total number of infected birds and humans is less than the threshold level. Otherwise, the three control measures will be implemented simultaneously to mitigate the spread of the disease. Furthermore, by using the Utkin equivalent control method, the existence of sliding mode equation and its dynamics are investigated. Additionally, the method of dynamic programming is introduced to study the optimal control for the model. Through constructing and proving the existence of viscosity solution of the corresponding Hamilton–Jacobi–Bellman equation, the optimal control pair is further obtained. Finally, numerical simulations are performed to illustrate the corresponding theoretical analysis results.