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The monitor and prediction of influenza through conventional modes seem obsolete in the Internet era. The flu prediction using machine learning methods is more resourceful for both economically as well as logistically, than traditional methods. The machine learning methods exploit freely available data on social media platforms. The main objective of this paper is to harness the power of publicly available data through machine learning methods. This work involves the scraping of textual data from six social media platforms and training of five machine learning predictors. The paper also proposes a three-layered prediction method based on support-vector-regression for predicting trends of influenza spread. The results obtained from proposed model are validated and analysed against other four machine learning methods. The results reveal that proposed model has better predictive accuracy in terms of least prediction errors among the five methods. Thus, it can provide effective means to control and to prevent the flu outbreak.

The large volume of geotagged Twitter streaming data on flu epidemics provides chances for researchers to explore, model, and predict the trends of flu cases in a timely manner. However, the explosive growth of data from social media makes data sampling a natural choice. In this paper, we develop a method for influenza prediction based on the real-time tweet data from social media, and this method ensures real-time prediction and is applicable to sampling data. Specifically, we first simulate the sampling process of flu tweets, and then develop a specific partial differential equation (PDE) model to characterize and predict the aggregated flu tweet volumes. Our PDE model incorporates the effects of flu spreading, flu recovery, and active human interventions for reducing flu. Our extensive simulation results show that this PDE model can almost eliminate the data reduction effects from the sampling process: It requires lesser historical data but achieves stronger prediction results with a relative accuracy of over 90% on the 1% sampling data. Even for the more aggressive data sampling ratios such as 0.1% and 0.01% sampling, our model is still able to achieve relative accuracies of 85% and 83%, respectively. These promising results highlight the ability of our mechanistic PDE model in predicting temporal–spatial patterns of flu trends even in the scenario of small sampling Twitter data.

Background Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

Aim: We propose a simple supervised method for forecasting the expansion of the geographical range of non-indigenous species without any detailed information on the pathway mechanisms. The models currently used, such as metapopulation models, population dynamics [e.g., the susceptible-infected–recovered (SIR) model] with diffusion process, and models using individual-based mechanisms, require specific parameters according to the assumed mechanisms. These parameters are difficult to obtain immediately after the first infestation is detected, although subsequent pandemics of novel pathogens and non-indigenous pest organisms can be fatal to humans and damage agriculture and native ecosystems. Innovation: A simple machine-learning approach was developed. The future distribution of the target species was predicted by composing the spreading patterns of the previous range expansions of various alien species similar to the target species. A matrix representing the early-late relationship of infestation between regions was used to compare and compose various range expansion patterns. The eigenvector of the composed matrix gives the predicted order of infestation. Spreading speed was considered using the standard deviation of infestation dates, and future infestation dates were calculated by a reduced major axis with the eigenvector. In the feasibility assessment, the study area (Japan) was divided into many small regions (prefectures), and the dates of infestation in these regions were predicted. Our method successfully predicted the infestation dates of various alien species and the novel H1N1 influenza pandemic in 2009. Main conclusions: Our method is less complex and requires simpler information than other methods and is applicable to all organisms. Constructing a database of the range expansions of alien species, including novel pathogens and agricultural and environmental pests, at the global level and with high spatial resolution, such as at the county or city level, will facilitate the prediction of future range expansions of hazardous organisms.

Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms \"influenza AND (forecast* OR predict*)\", excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea. Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson's correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons. The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05). In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.

Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's \"tweets,\" or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.

Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

Background Currently available anti-influenza drugs are often associated with limitations such as toxicity and the appearance of drug-resistant strains. Therefore, there is a pressing need for the development of novel, safe and more efficient antiviral agents. In this study, we evaluated the antiviral activity of zinc oxide nanoparticles (ZnO-NPs) and PEGylated zinc oxide nanoparticles against H1N1 influenza virus. Methods The nanoparticles were characterized using the inductively coupled plasma mass spectrometry, x-ray diffraction analysis, and electron microscopy. MTT assay was applied to assess the cytotoxicity of the nanoparticles, and anti-influenza activity was determined by TCID50 and quantitative Real-Time PCR assays. To study the inhibitory impact of nanoparticles on the expression of viral antigens, an indirect immunofluorescence assay was also performed. Results Post-exposure of influenza virus with PEGylated ZnO-NPs and bare ZnO-NPs at the highest non-toxic concentrations could be led to 2.8 and 1.2 log10 TCID50 reduction in virus titer when compared to the virus control, respectively ( P  < 0.0001). At the highest non-toxic concentrations, the PEGylated and unPEGylated ZnO-NPs led to inhibition rates of 94.6% and 52.2%, respectively, which were calculated based on the viral loads. There was a substantial decrease in fluorescence emission intensity in viral-infected cell treated with PEGylated ZnO-NPs compared to the positive control. Conclusions Taken together, our study indicated that PEGylated ZnO-NPs could be a novel, effective, and promising antiviral agent against H1N1 influenza virus infection, and future studies can be designed to explore the exact antiviral mechanism of these nanoparticles.

Highly Pathogenic Avian Influenza (HPAI) threatens wild and domestic birds, mammals, and humans. The global spread of HPAI through wild birds requires timely, spatially accurate detection for enhanced preventive measures. However, detecting outbreaks in wildlife is challenging due to reliance on opportunistic sampling and testing of small numbers of wild animals, resulting in insufficient data on the temporal and geographical distribution of infections. We aimed to create a HPAI hotspot prediction model for the Netherlands, using wild bird mortality reports and confirmed HPAI incidents from 2016–2022. Variables for human, wild bird, and sampling density were used as statistical adjusters in various combinations. The Bayesian binomial model employed a case-crossover design, where HPAI incidents in wild birds were cases, and controls used the same date and location as the case but transposed to 2019, when no HPAI was reported in the Netherlands. We tested 24 models in a ten-fold cross-validation design. The best model had an area under the curve score of 0.68, a sensitivity of 0.47, and a specificity of 0.79, and included wild bird mortality and density. The yearly spatial distribution of predicted wild bird HPAI outbreak areas generally matched laboratory-confirmed HPAI cases, especially from 2020 onward, except for an atypically intensively sampled area west of Amsterdam before 2020. Most HPAI outbreak predictions occurred from October through March. Our model highlights potential HPAI outbreak areas over time and can be used to direct sampling efforts, potentially increasing both effectiveness and timeliness.