Number Of Influenza-like Illness Cases Research Articles

Epidemiological modeling of Influenza-Like Illness (ILI) transmission in Jakarta, Indonesia through cumulative generating operator on SLIR model

Influenza-Like Illness (ILI) constitutes a significant global health concern characterized by its high infection rates and widespread distribution worldwide. While influenza viruses, primarily types A and B, are primary contributors to ILI cases, other respiratory viruses also play a role in its prevalence. Jakarta, Indonesia’s largest and densely populated city, has consistently reported a notable weekly number of ILI cases from 2016 to mid-2022. Intriguingly, this pattern of cases is irregular and does not exhibit a direct association with seasonal climate fluctuations. In response to this complex scenario, we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function, obtained from weekly cumulative data to derive model solutions. A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 weeks. Our correlation analysis unveiled distinct temporal relationships within the model, revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment, along with a 2-week lag marking the incubation period between the latent and infected compartments. Furthermore, the effective reproduction number displayed recurrent fluctuations around a threshold of 1, indicating the endemic characteristics where infection persists within the population. This in-depth comprehension of ILI transmission dynamics and effective reproduction numbers plays a significant role in devising control measures and informed policy-making decisions.

Population-based study of influenza and invasive meningococcal disease among Greek children during the COVID-19 pandemic

BackgroundAiming to the containment of the coronavirus disease 2019 (COVID-19) pandemic, governments worldwide have implemented a series of non-pharmaceutical interventions. Many of them and especially school closures have impacted the...

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.

Evaluation of influenza vaccine effectiveness in 2017-2018 influenza season based on community children cohort study

Objective: To assess the effectiveness of influenza vaccine in children aged 6-72 months. Methods: The cohort study was conducted based on community child vaccination clinics in Yiwu and Yongkang counties of Zhejiang province. From October 2017 to December 2017, a total of 1 752 children aged 6-72 months were enrolled from 10 child vaccination clinics. The questionnaire survey was conducted after the written consents were obtained from the parents or legal guardians of the children. Then, a follow up was conducted for enrolle children until 30 April 2018, the influenza vaccination status and the number of influenza-like illness (ILI) cases, hospital visit due to ILI, self-medication due to ILI were observed and recorded every month. Vaccine effectiveness (VE) was estimated by using the generalized linear model (GLM) where dependent variables were the number of ILI cases, hospital visit and self-medication respectively. Results: Of the 1 752 children, 925 (52.80%) were boys and the median age was 30.00 months. The cumulative observation was 308 166 person days at the end of 2017-2018 season, with 5.27 ILI cases per 1 000 person days, 3.41 hospital visit due to ILI per 1 000 person days, 1.45 self-medication due to ILI per 1 000 person days. Of the 1 752 children, 643 received the influenza vaccination in 2017-2018 season. Compared with unvaccinated children, the VE was 23.5% against ILI case number (95%CI: 15.1%-31.1%), 19.3% against hospital visit due to ILI (95%CI: 8.2%-29.1%) and 25.8% against self-medication due to ILI (95%CI: 9.3%- 39.3%). Modeling splitting 643 children with 2017-2018 vaccination into those before and after vaccination, the influenza VE was 31.9% against ILI case number (95%CI: 12.7%-46.9%), 32.6% against hospital visit due to ILI (95%CI: 8.6%-50.3%) and 44.3% against self-medication due to ILI (95%CI: 11.9%-64.8%) in children aged 36-72 months. However, the children aged 6-35 months showed no significant VEs. For the VE analysis in children with different vaccination status, the VEs were significant if they received vaccination in both 2016-2017 season and 2017-2018 season or only in 2017-2018 seasons. The VE was not demonstrated among the children who were immunized only in 2016-2017 season. Conclusion: Influenza vaccination is moderate effective in preventing the incidence of ILI and hospital visit and self-medication in children in influenza season, the protection effect in children aged 36-72 months is better than that in children aged 6-35 months.

Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk.

The impact of air pollution on people's health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi'an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model's dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

BackgroundInfluenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.ObjectiveDescribe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.MethodsWe describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You).ResultsWISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information.ConclusionsWhile the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.

Evaluation of clinical features scoring system as screening tool for influenza A (H1N1) in epidemic situations

Influenza A (H1N1) hit the headlines in recent times and created mass hysteria and general panic. The high cost and non-availability of diagnostic laboratory tests for swine flu, especially in the developing countries underlines the need of having a cheaper, easily available, yet reasonably accurate screening test. This study was carried out to develop a clinical feature-based scoring system (CFSS) for influenza A (H1N1) and to evaluate its suitability as a screening tool when large numbers of influenza-like illness cases are suspect. Clinical-record based study, carried out retrospectively in post-pandemic period on subject's case-sheets who had been quarantined at IG International Airport's quarantine center at Delhi. Clinical scoring of each suspected case was done by studying their case record sheet and compared with the results of RT-PCR. RT-PCR was used to confirm the diagnosis (Gold Standard). We calculated sensitivity, specificity, positive and negative predictive values of the clinical feature-based scoring system (the proposed new screening tool) at different cut-off values. The most discriminant cut-off value was determined by plotting the ROC curve. Of the 638 suspected cases, 127 (20%) were confirmed to have H1N1 by RT-PCR examination. On the basis of ROC, the most discriminant clinical feature score for diagnosing Influenza A was found to be 7, which yielded sensitivity, specificity, positive, and negative predictive values of 86%, 88%, 64%, and 96%, respectively. The clinical features scoring system (CFSS) can be used as a valid and cost-effective tool for screening swine flu (influenza A (H1N1)) cases from large number of influenza-like illness suspects.

The application of time series analysis in predicting the influenza incidence and early warning

This research aimed to explore the application of ARIMA model of time series analysis in predicting influenza incidence and early warning in Jiangsu province and to provide scientific evidence for the prevention and control of influenza epidemic. The database was created based on the data collected from monitoring sites in Jiangsu province from October 2005 to February 2010. The ARIMA model was constructed based on the number of weekly influenza-like illness (ILI) cases. Then the achieved ARIMA model was used to predict the number of influenza-like illness cases of March and April in 2010. The ARIMA model of the influenza-like illness cases was (1 + 0.785B(2))(1-B) ln X(t) = (1 + 0.622B(2))ε(t). Here B stands for back shift operator, t stands for time, X(t) stands for the number of weekly ILI cases and ε(t) stands for random error. The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was 5.087, giving a P-value of 0.995. The model fitted the data well. True values of influenza-like illness cases from March 2010 to April 2010 were within 95%CI of predicted values obtained from present model. The ARIMA model fits the trend of influenza-like illness in Jiangsu province.

Evaluating Syndromic surveillance systems at institutions of higher education (IHEs): A retrospective analysis of the 2009 H1N1 influenza pandemic at two universities

BackgroundSyndromic surveillance has been widely adopted as a real-time monitoring tool for timely response to disease outbreaks. During the second wave of the pH1N1 pandemic in Fall 2009, two major universities in Washington, DC collected data that were potentially indicative of influenza-like illness (ILI) cases in students and staff. In this study, our objectives were three-fold. The primary goal of this study was to characterize the impact of pH1N1 on the campuses as clearly as possible given the data available and their likely biases. In addition, we sought to evaluate the strengths and weaknesses of the data series themselves, in order to inform these two universities and other institutions of higher education (IHEs) about real-time surveillance systems that are likely to provide the most utility in future outbreaks (at least to the extent that it is possible to generalize from this analysis).MethodsWe collected a wide variety of data that covered both student ILI cases reported to medical and non-medical staff, employee absenteeism, and hygiene supply distribution records (from University A only). Communication data were retrieved from university broadcasts, university preparedness websites, and H1N1-related on campus media reports. Regional data based on the Centers for Disease Control and Prevention Outpatient Influenza-like Illness Surveillance Network (CDC ILINet) surveillance network, American College Health Association (ACHA) pandemic influenza surveillance data, and local Google Flu Trends were used as external data sets. We employed a "triangulation" approach for data analysis in which multiple contemporary data sources are compared to identify time patterns that are likely to reflect biases as well as those that are more likely to be indicative of actual infection rates.ResultsMedical personnel observed an early peak at both universities immediately after school began in early September and a second peak in early November; only the second peak corresponded to patterns in the community at large. Self-reported illness to university deans' offices was also relatively increased during mid-term exam weeks. The overall volume of pH1N1-related communication messages similarly peaked twice, corresponding to the two peaks of student ILI cases.ConclusionsDuring the 2009 H1N1 pandemic, both University A and B experienced a peak number of ILI cases at the beginning of the Fall term. This pattern, seen in surveillance systems at these universities and to a lesser extent in data from other IHEs, most likely resulted from students bringing the virus back to campus from their home states coupled with a sudden increase in population density in dormitories and lecture halls. Through comparison of data from different syndromic surveillance data streams, paying attention to the likely biases in each over time, we have determined, at least in the case of the pH1N1 pandemic, that student health center data more accurately depicted disease transmission on campus at both universities during the Fall 2009 pandemic than other available data sources.

Dual Seasonal Patterns for Influenza, China

Dual Seasonal Patterns for Influenza, China