Incidence Of Influenza Research Articles

Uncovering the burden of Influenza in children in Portugal, 2008–2018

BackgroundDespite their higher risk of developing severe disease, little is known about the burden of influenza in Portugal in children aged < 5 years old. This study aims to cover this gap by estimating the clinical and economic burden of severe influenza in children, in Portugal, during ten consecutive influenza seasons (2008/09-2017/18).MethodsWe reviewed hospitalizations in children aged < 5 years old using anonymized administrative data covering all public hospitals discharges in mainland Portugal. The burden of hospitalization and in-hospital mortality directly coded as due to influenza was supplemented by the indirect burden calculated from excess hospitalization and mortality (influenza-associated), estimated for four groups of diagnoses (pneumonia or influenza, respiratory, respiratory or cardiovascular, and all-cause), through cyclic regression models integrating the incidence of influenza. Means were reported excluding the H1N1pdm09 pandemic (2009/10).ResultsThe mean annual number of hospitalizations coded as due to influenza was 189 (41.3 cases per 100,000 children aged < 5 years old). Hospitalization rates decreased with increasing age. Nine-in-ten children were previously healthy, but the presence of comorbidities increased with age. Children stayed, on average, 6.1 days at the hospital. Invasive mechanical ventilation was used in 2.4% of hospitalizations and non-invasive in 3.1%. Influenza-associated excess hospitalizations between 2008 and 2018 were estimated at 1,850 in pneumonia or influenza, 1,760 in respiratory, 1,787 in respiratory or cardiovascular, and 1,879 in all-cause models. A total of 95 influenza-associated excess deaths were estimated in all-cause, 14 in respiratory or cardiovascular, and 9 in respiratory models. Over ten years, influenza hospitalizations were estimated to have cost the National Health Service at least €2.9 million, of which 66.5% from healthy children.ConclusionsInfluenza viruses led to a high number of hospitalizations in children. Most were previously healthy. Results should lead to a reflection on the adequate preventive measures to protect this age group.

Influenza incidence, lineages, and vaccine effectiveness estimates in Lima, Peru, 2023

Influenza incidence, lineages, and vaccine effectiveness estimates in Lima, Peru, 2023

The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022.

Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures. The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence. The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group. Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.

Forecasting influenza incidence as an ordinal variable using machine learning

Background: Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. Methods: We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 32 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). Results: The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Conclusion: Given that many natural extreme phenomena are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.

Evaluating the Effectiveness of New Criteria for Early Detection of the Start and Intensity of Influenza Epidemics in Russian Federation

Relevance. During the COVID-19 pandemic, an early determination of the start of the influenza epidemic by the incidence of influenza and SARS in total is impossible, due to the similarity of the clinical picture of SARS and lung cases of COVID-19.Aim. The goal is to calculate and test new criteria for early detection of the start of influenza epidemics and their intensity for each of the cities–reference bases (61) of the 2 WHO National Influenza Centers based on the incidence of clinically diagnosed influenza.Tasks. To evaluate the effectiveness of baseline influenza incidence and epidemic intensity thresholds for the general population and age groups of each city in the epidemic of 2022–2023. To give a retrospective assessment of the effectiveness of influenza baselines for cities, compared with the baselines of the corresponding Federal Districts, for the seasons from 2009 to 2022. To estimate the intensity of epidemics by influenza incidence over the previous epidemies of the pandemic cycle of influenza A/California/H1N1/ virus.Materials and methods. By the 2022–2023 season. baseline lines and thresholds of influenza incidence intensity were calculated using the method of moving epidemics according to clinical diagnostic data not only for federal districts, but also for each of the observed cities (61). The calculation of the baselines was carried out according to the data of the computer database of the Influenza Research Institute on the incidence of influenza by age groups in each city over the previous 5 years in the season from 2016–2017 to 2021–2022.Results. In the 2022-23 season application of new criteria for the start of epidemics (prev.- and post-epidemic baseline influenza incidence) and their intensity revealed: early onset of the influenza epidemic (07–13.11 2022); simultaneous onset in all children's age groups; geographical spread of the epidemic in federal districts; intensity of the epidemic in the general population and age groups. The thresholds for the intensity of influenza morbidity made it possible to clarify the intensity levels of influenza epidemics from 2009 to 2023 and to show that the pandemic cycle of the influenza A(H1N1) virus continues. A comparison of the effectiveness of urban baselines with federal ones in the epidemic of 2022–2023 showed that urban baseline flu incidence lines revealed the start of epidemics 1–3 weeks earlier: among the general population in 12 cities, persons over 15 years old – in 9, children 3–6 years old – in 6 and 7–14 years old – in 5. A retrospective assessment of the effectiveness of city and federal influenza baselines (from 2009 to 2022) showed their effectiveness both in the seasons from 2009 to 2016 (before the baseline calculation period) and after. The effectiveness of urban baselines for early detection of the start of epidemics depended on the etiology of the epidemic – more with influenza A(H3N2) than with influenza A(H1N1), the level of intensity of influenza diseases and the age group of the population (more in children with low intensity and in adults with an average level).Conclusion. The results obtained on the basis of population epidemiological data on the incidence of influenza, namely, new criteria for detecting the start of an epidemic in cities, can be used in health management bodies in cities and subjects of the Russian Federation for early detection of epidemics and management decisions, timely introduction of anti-epidemic measures, creating a stock of medicines. The expected effect of the method of early epidemiological diagnosis of epidemics is a decrease in morbidity, etc.

Machine-learning-based analysis of biomedical time-series data: the monitoring and prediction of disease progression

Abstract This study examines the spatial and temporal patterns of influenza and malaria incidence using an ARMA-BP combination model. The approach employs the dynamic series method to identify epidemic patterns of these diseases while assessing serial autocorrelation coefficients, performing randomness tests, and establishing a forecasting model. Additionally, it evaluates the impact of seasonal and meteorological factors on the epidemiology of influenza and malaria to ascertain the model’s efficacy in predicting incidence rates and trends. The findings indicate that the peak period for influenza incidence typically occurs during the transition from winter to spring, specifically between weeks 2 and 14. The correlation coefficients between temperature variables and malaria incidence generally ranged from 0.7 to 0.9. The ARMA-BP model demonstrated robust short-term predictive capabilities for influenza, showing a high degree of concordance in predictions for 2021 and 2022, though it performed less satisfactorily for 2023. For malaria, the predicted and actual incidence trends were largely consistent, with prediction errors consistently below 0.01. Consequently, this underscores the need for enhanced data collection on factors influencing disease dynamics. This research provides valuable decision-making support, scientific insights, and theoretical guidance for enhancing disease monitoring and prediction strategies.

Assessment of farm size, gender dynamics, and biosecurity practices in the incidence of Newcastle disease and avian influenza in indigenous chicken and Guinea fowl smallholder farms in Northern Benin

Assessment of farm size, gender dynamics, and biosecurity practices in the incidence of Newcastle disease and avian influenza in indigenous chicken and Guinea fowl smallholder farms in Northern Benin

Mortality trends in people with disabilities before and during the COVID-19 pandemic in South Korea, 2017-2022.

To investigate temporal trends in mortality rates and underlying causes of death in persons with disabilities before and during the coronavirus disease 2019 (COVID-19) pandemic. Annual mortality rates and causes of death were analyzed using data covering the 2017-2022 period. The mortality rate among people with disabilities increased from 2017 to 2022; the rate was five times higher during COVID-19 in this population than in the general population. When analyzing the cause of death, the incidence of infectious diseases and tuberculosis decreased after COVID-19. In contrast, the incidence of other bacillary disorders (A30-A49) increased. The incidence of respiratory system diseases (J00-J99), influenza and pneumonia (J09-J18), and other acute lower respiratory infections (J20-J22) decreased before COVID-19, while the incidence of lung diseases due to external agents (J60-J70), other respiratory diseases principally affecting the interstitium (J80-J84), and other diseases of the pleura (J90-J94) increased during the pandemic. The risk of COVID-19 death among people with disabilities was 1.1-fold higher for female patients (95% CI = 1.06-1.142), 1.41-fold for patients aged 70 years and older (95% CI = 1.09-1.82), and 1.24-fold higher for people with severe disabilities (95% CI = 1.19-1.28). The mortality rate in people with disabilities significantly increased during COVID-19, compared with that before the pandemic. People with disabilities had a higher mortality rate during COVID-19 compared with the general population. Risk factors must be reduced to prevent high mortality rates in this population.

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.

Surveillance of seasonal influenza viruses during the COVID-19 pandemic in Tokyo, Japan, 2018-2023, a single-center study.

COVID-19 pandemic led to significant reductions in influenza detection worldwide, fueling debates on whether influenza truly ceased circulating in communities. The number of influenza cases decreased significantly in Japan, raising concerns about the potential risk of decreased immunity to influenza in the population. Our single-center study aimed to investigate influenza trends before and during the COVID-19 pandemic in Tokyo, Japan. This cross-sectional study included patients of all ages who visited Tokyo Shinagawa Hospital between April 1, 2018, and March 31, 2023. Influenza and COVID-19 tests were conducted using Quick Navi-Flu2 and polymerase chain reaction (PCR). We analyzed data from before and during the COVID-19 epidemic, based on patient background, hospitalization, and deaths, collected from medical records. A total of 12 577 influenza tests were conducted, with approximately 100 tests consistently performed each month even in the influenza off-season. Throughout the observation period, 962 positive cases were identified. However, no cases were observed for 27 months between March 2020 and November 2022. Influenza A cases were reobserved in December 2022, followed by influenza B cases in March 2023, similar to the influenza incidence reports from Tokyo. The positivity rate during the 2022-2023 winter season was lower than before the COVID-19 epidemic and decreased in elderly patients, with no hospitalizations or deaths observed. This single-center study provided actual trend data for influenza patients before and during COVID-19 outbreaks in Tokyo, which could offer insights into the potential impact and likelihood of influenza virus infection in Japan.

Seasonal influenza in children: complications, treatment, prevention (subject publication review).

Aim: To study the specificity of seasonal flu in children, in particular, in young children, as well as treatment, prevention and complications of seasonal flu. Materials and Methods: For the methodological justification of the article, we used the pool of research technologies. Methods of theoretical analysis, system-analytical, comparative methods provided us with the opportunity to characterize the features of influenza incidence in children. Conclusions: A distinctive feature of influenza is the high lability of the genes of the infectious agent. In this regard, it is extremely important to timely update information about new strains of the pathogen, creation of new types of vaccines and antiviral drugs, as well as changes in the course of the disease. Our literature review is intended to improve the medical community.

Análisis sistémico del programa de vacunación en Argentina

Introduction: The paper discusses the historical evolution and importance of vaccines in public health. The key role of vaccines and drinking water in preventing infectious diseases since the 19th century is highlighted. It mentions how epidemics such as poliomyelitis in Argentina boosted vaccine research and development. In addition, the shared responsibility between scientists, vaccine developers and the population is emphasized, highlighting the importance of public health policies and awareness.Methods: A qualitative study was conducted with a systematic review of scientific literature. Sources included data from WHO, the Argentine Ministry of Health, scientific papers, and surveys, with a focus on sociodemographic parameters and living conditions. The analysis focused on a historical-logical approach to interpret the data.Results: The results show significant advances in the Argentine vaccination program, especially in the pediatric population. There was a notable reduction in the number of unvaccinated children from 2021 to 2022, and increases in DTP and HPV vaccine coverage. There was a historic decrease in the incidence of influenza and in the infant mortality rate since 1990. The measles outbreak in 2020 was associated with low vaccination coverage in 2019. During the COVID-19 pandemic, there was a low post-vaccination infection rate and zero deaths in fully vaccinated individuals. In addition, an increase in life expectancy since 1990 was noted.Conclusions: Vaccines are fundamental in public health, evidenced by the improvement in vaccination coverage and the decrease in the prevalence of infectious diseases and infant mortality in Argentina. Awareness campaigns and public health policies have been crucial. However, misinformation remains a challenge. Global collaboration, such as that of WHO, is key to improving vaccination coverage and addressing emerging challenges such as COVID-19 and Ebola. Immunization remains an essential tool to prevent disease and save lives worldwide.

Routine Childhood Immunization: Its health, developmental, economic and social benefits

Introduction: Immunization plays a pivotal role in public health. It is one of the most cost-effective inventions that have helped bring down a significant amount of burden of diseases among under-five children, some of which are now on the verge of extinction. Objective: To assess the diverse benefits of routine childhood immunization. Methodology: A literature review was conducted using electronic database to understand the broader benefits of immunization. Results: Studies have shown that routine childhood immunization have been able to reduce the incidence of influenza and other targeted diseases (diphtheria, Haemophilus influenzae type b, measles, mumps, polio, and rubella) by 17% and 100% respectively. Conclusion: Vaccines are known to have protective and positive health benefit which extends beyond infancy and childhood. It is therefore one of the best strategies in public health to reduce morbidity and mortality and enable child survival.

Increased Incidence of Influenza: Effects of Dense Housing Occupancy, Behaviour and Demographic Factors in Coastal Areas

Introduction: Influenza is an acute respiratory infection that is a seasonal pandemic and can be prevented by healthy behaviors. However, the fact is that influenza cases are still often outbreaks, even epidemics, both nationally and globally. Purpose: To assess demographic determinants, occupancy density, and preventive behavior with influenza incidence in the coastal area of Bungkutoko sub-district of Kendari city, in one of the islands of Southeast Sulawesi, Indonesia. Methods: A cross-sectional study involving 95 respondents selected by simple random sampling was used as a research design. Structured interviews used valid and reliable questionnaires to obtain data on age, sex, preventive behavior, and occupancy density. The chi-square test was used to analyze bivariates with a significant value of 0.05. Results: More than half (53.7%) of influenza cases were found out of 95 respondents. Age (p=0.002; OR= 3.251), gender (p=0.041; OR=2.133), preventive behavior (p=0.047; OR=2.163), and dense housing occupancy (p=0.000; OR= 5.775) was significantly associated with influenza (α 0.05). Conclusion: Influenza cases increased associated with risk factors for age, sex, preventive behavior, and dense housing occupancy in coastal areas in this study. Thus, indicating increased education on influenza prevention by paying attention to risk factors by community and interprofessional nurses is needed as early awareness of the potential for an epidemic.Latar belakang: Influenza merupakan penyakit infeksi saluran pernapasan akut yang pandemic musiman dan dapat dicegah dengan prilaku hidup sehat. Namun, kenyataanya kasus influenza masih sering menjadi outbreak, bahkan epidemic baik nasional maupun global. Tujuan: Untuk menilai determinan demografi, kepadatan hunian, dan prilaku pencegahan dengan kejadian influenza di wilayah pesisir kelurahan Bungkutoko kota Kendari, di salah satu kepulauan Sulawesi Tenggara, Indonesia. Metode: Studi cross-sectional digunakan sebagai desain penelitian, melibatkan 95 responden yang dipilih dengan simple random sampling. Wawancara terstruktur menggunakan kuesioner yang valid dan reable untuk memperoleh data usia, jenis kelamin, perilaku sehat, dan kepadatan hunian. Uji chi squre digunakan untuk menganalisis bivariat dengan nilai signifikan 0,05. Hasil: Ditemukan setengah lebih (53,7%) kasus influenza dari 95 responden. Usia (p=0,002; OR= 3,251), jenis kelamin (p=0,041; OR=2,133), perilaku hidup sehat (p=0,047; OR=2,163), dan kepadatan hunian (p=0,000; OR= 5,775) berhubungan secara signifikan terhadap kejadian inluenza (α 0,05). Simpulan: Kasus influenza terjadi peningkatan berhubungan dengan faktor risiko usia, jenis kelamin, perilaku pencegahan, dan kepadatan hunian di daerah pesisir pada studi ini. Sehingga, mengindikasikan peningkatan edukasi pencegahan kejadian influenza dengan memperhatikan faktor risiko oleh perawat komunitas dan interprofessional sangat diperlukan sebagai kewaspadaan dini potensi terjadi epidemic.

Age-Specific Resurgence in Invasive Pneumococcal Disease Incidence in the COVID-19 Pandemic Era and Its Association With Respiratory Virus and Pneumococcal Carriage Dynamics: A Time-Series Analysis.

Using multiple national surveillance systems, we found an increase in the incidence of invasive pneumococcal disease during after the relaxation of non-pharmaceutical interventions against COVID-19, which strongly varied by age. Age groups with higher incidence of respiratory syncytial virus and influenza also experienced higher increase in invasive pneumococcal disease incidence, with no change in pneumococcal carriage.

A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China.

Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks. Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA, ETS, and support vector regression (SARIMA-ETS-SVR) hybrid model. The prediction performance was determined by comparing mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) indices. The optimum SARIMA model was SARIMA (0,1,0) (0,0,1)12. Error trend and seasonality (ETS) (M,A,M) was the SARIMA optimal model. For the fitting performance, the SARIMA-ETS-SVR hybrid model achieved the lowest values of MAE, MSE, and RMSE, in addition to the MAPE. In terms of predictive performance, the SARIMA-ETS-SVR hybrid model had the lowest MAE, MSE, MAPE, and RMSE values among the three models. The study demonstrated that the SARIMA-ETS-SVR hybrid model provides better generalization ability than a single SARIMA model and a single ETS model, and the predictions will provide a useful tool for preventing this infectious disease.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019.

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Influenza incidence trend prediction based on ARIMA seasonal multiplicative model

This study utilizes a time series ARIMA seasonal multiplicative model to predict the incidence trend of influenza in China, providing valuable early warning references for influenza prevention and control. Monthly incidence data of influenza cases nationwide were collected from January 2014 to August 2022. The data from January 2014 to December 2020 were used as the training set to fit the time series model of influenza incidence. The data from January 2021 to August 2022 were utilized as the testing set to predict the influenza incidence from January 2021 to August 2022 using the fitted model. The predicted values were then compared with the testing set. Through residual white noise testing, significance testing of parameters, and examination of model fit, the final model was determined as , which demonstrated a good fit. The majority of actual data fell within the 95% confidence interval of the predicted values, and the predicted incidence trend aligned closely with the actual trend. The constructed model holds significant application value in early warning systems for influenza prevention and control, providing crucial insights for public health strategies. In practical applications, this model can be integrated with various factors such as social, natural, and geographic environments to formulate targeted prevention and control strategies, thus enhancing the efficiency and effectiveness of influenza prevention and control measures.

Interactive effect of air pollutant and meteorological factors on seasonal influenza transmission, Shanghai, China

BackgroundPrevious studies have provided evidence of seasonal influenza transmission linked to various meteorological and environmental factors. However, the joint effect from both air pollutant exposure and meteorological factors that modify the seasonal influenza incidence is not well discussed. MethodsData on weekly laboratory-confirmed influenza positive case numbers were obtained from Pudong New Area, Shanghai, China between January 1, 2011 and December 31, 2019. Weekly average weather data (absolute humidity (AH), minimum temperature (Min Temp) and particulate matter <2.5 μm (PM2.5) concentration levels) at the same period were collected from Copernicus Atmosphere Monitoring Services (CAMS).A General additive model (GAM) and distributed lag nonlinear model (DLNM) with Quasi-Poisson likelihood were used to evaluate the independent and interaction effect of weather factors and PM2.5 on seasonal influenza transmission along with lag effects. ResultsOur research showed that both AH and Min Temp had U-shape interactive relationships with PM2.5 at high PM2.5 concentration level. Interaction between PM2.5 and AH had positive relative risk (RR) effect at lag of 5 weeks when AH = 5 g/m3and 15 g/m3, as well as the same lag period when Min Temp = 5 °C and 20 °C. ConclusionOur study found that an interactive effect between air pollutant and weather factors exists and significantly affects influenza transmission. For future studies, lag interactive effects among air pollutant and temperature or humidity should be considered in the evaluation of the environment-influenza association, as such interactions might change the disease response to short-term effects.

Estimating the economic burden of influenza on the older population in Malaysia

Background Influenza is a contagious respiratory illness that can cause life-threatening complications among high-risk groups. Estimating the economic burden of influenza is essential to guide policy‐making on influenza vaccination programmes, especially in resource‐limited settings. This study aimed to estimate the economic burden of influenza on older adults (those aged ≥60 years) in Malaysia from the provider’s perspective. Methods The main data source in this study was the MY-DRG Casemix database of a teaching hospital in Malaysia. Cases with principal and secondary diagnoses coded in the International Classification of Diseases version 10 (ICD-10) as J09, J10.0, J10.1, J10.8, J11.0, J11.1, J11.8, J12.8, and J12.9, which represent influenza and its complications, were included in the study. The direct cost of influenza at all severity levels was calculated from the casemix data and guided by a clinical pathway developed by experts. The effect of the variations in costs and incidence rate of influenza for both the casemix and clinical pathway costing approaches was assessed with sensitivity analysis. Results A total of 1,599 inpatient and 407 outpatient influenza cases were identified from the MY-DRG Casemix database. Most hospitalised cases were aged &lt;18 years (90.6%), while 77 cases (4.8%) involved older people. Mild, moderate, and severe cases comprised 56.5%, 35.1%, and 8.4% of cases, respectively. The estimated average annual direct costs for managing mild, moderate, and severe influenza were RM2,435 (USD579), RM6,504 (USD1,549), and RM13,282 (USD3,163), respectively. The estimated total annual economic burden of influenza on older adults in Malaysia was RM3.28 billion (USD782 million), which was equivalent to 10.7% of the Ministry of Health Malaysia budget for 2020. The sensitivity analysis indicated that the influenza incidence rate and cost of managing severe influenza were the most important factors influencing the total economic burden. Conclusions Overall, our results demonstrated that influenza imposes a substantial economic burden on the older Malaysian population. The high cost of influenza suggested that further efforts are required to implement a preventive programme, such as immunisation for older people, to reduce the disease and economic burdens.