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Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word ``colds'' appears frequently in web data. The web data after the appearance of the word ``colds'' can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient’s lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor’s diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.

COVID-19 is one of the most disastrous respiratory diseases (after the 1918 influenza outbreak) spreading in the community. So far, it has killed 7,37,417 individuals. High variability in the viral genome and its greater ability to spread in the human community are badly affecting the comorbid individuals. Although infected individuals mainly possess respiratory issues, neurological manifestations in these individuals cannot be overlooked. The literature search is based on the recent development in the concerned field. We searched databases like PubMed, Google Scholar, and ScienceDirect using the keywords “COVID-19”, “neurological manifestations”, “CNS”, and “PNS”. The major neurological complications observed in these patients are encephalitis, necrotising haemorrhagic encephalopathy, Guillain-Barré Syndrome, smell/taste impairment, epileptic seizures, and abnormal states of consciousness. COVID-19 infection is just more than a cough, fever, and respiratory illness; it can cause indirect neurological complications in infected patients. It is therefore advised to treat and have a careful observation of the COVID-19 patients for neurological manifestations.

This article will argue that the memory of the great ‘Spanish’ influenza pandemic of 1918–19 played a significant role in the preparedness and response of Australia and New Zealand to the COVID-19 pandemic, and may help to explain their success compared with Europe and North America. An obvious alternative explanation for the success of Asian states such as China, Singapore, Hong Kong, South Korea, Taiwan, and Japan would be their experience of the SARS outbreak in 2002 and the H7N9 influenza outbreak of 2013. However, this explanation does not apply to Australia or New Zealand. All of these states had pandemic plans, initially developed with encouragement from the World Health Organization after the SARS outbreak, but only Australia and New Zealand appear to have directly incorporated ‘lessons’ from 1918–19 into their pandemic plans.

Our objective was to evaluate risk factors of nosocomial influenza (NI) in an university hospital during the 2015/2016 influenza season. All hospitalized patients with influenza-like illness associated with laboratory confirmation by polymerase chain reaction were included in a prospective observational study. We identified 44 cases (19%) of NI among the 233 cases of influenza: 38/178 (21%) in adults and 6/55 (11%) in children. Among adults, hospitalization in a double or multi-occupancy room was independently associated with NI (adjusted Odds Ratio, 3.42; 95% CI, 1.29-9.08; p = 0.013). The results of the study underline the importance of single room to prevent NI.

Background The 2017 A/H3N2 influenza season was the most severe season since the 2009 influenza pandemic. There were over 591 influenza outbreaks in institutions across the state of New South Wales (NSW) in Australia. Aim To describe the epidemiology of influenza outbreaks in nine Sydney aged care facilities in 2017. Methods Study data were collected from nine Sydney aged care facilities for 2017 influenza season. Descriptive epidemiological analysis was conducted. Results From the nine sites included, with a total of 716 residents, four sites reported laboratory-confirmed influenza outbreaks during the study period, with an attack rate in residents ranging from 6% to 29%. The outbreaks resulted in lockdowns in two facilities and hospitalisation of seven residents. No deaths were reported as a result of influenza infection. Influenza A was the most common influenza type reported across the facilities. The duration of outbreak lasted for 1-4 weeks varied by site. Conclusion The 2017 season was a severe influenza season recorded in Australia. About half of the facilities studied experienced outbreaks of influenza, with a high attack rate among residents. Infection prevention and control measures and outbreak management plans are crucial for aged care facilities, including vaccination of staff and visitors to prevent outbreaks among the vulnerable residents.

The influenza pandemic is a wide-ranging threat to people’s health and property all over the world. Developing effective strategies for predicting the influenza outbreak which may prevent or at least get ready for a new influenza pandemic is now a top global public health priority. Owing to the complexity of influenza outbreaks that are usually involved with spatial and temporal characteristics of both biological and social systems, however, it is a challenging task to achieve the real-time monitoring of influenza outbreaks. In this study, by exploring the rich dynamical information of the city network during influenza outbreaks, we developed a computational method, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to identify the tipping point or critical stage prior to the influenza outbreak. With historical records of influenza outpatients between 2009 and 2018, the MST-DNM strategy has been validated by accurate predictions of the influenza outbreaks in three Japanese cities/regions, respectively, i.e., Tokyo, Osaka, and Hokkaido. These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak. The results show that our method is of considerable potential in the practice of public health surveillance.

The influenza outbreak that occurred during 1918-1920 was a defining moment in the history of the world and osteopathic medicine. Despite the tremendous loss of human life, osteopathic physicians also observed greater success in the treatment of patients with the disease, in contrast with their allopathic counterparts. Osteopathic physicians also succumbed to the deadly influenza effects while treating patients. A list of osteopathic physicians who died of influenza or related complications during the pandemic, obtained from osteopathic journals from that time, is provided, along with the historical context of the pandemic.

When Spanish influenza broke out in Harrisburg in 1918, it naturally concerned City Beautiful reformers who had underscored clean water and advocated public health. This study of the outbreak and responses to it through documents and new digital tools sheds light on the local spatial and demographic patterns of those affected by the disease in Pennsylvania's capital city.

We report an influenza outbreak in a 75‐bed rehabilitation centre and present the detailed microeconomic impact that it had during the season 2016/2017. The direct medical, direct non‐medical and indirect costs were calculated. The outbreak included 18 patients with influenza and 8 contact patients, leading to 86 days with isolation precautions. During the outbreak month, 25 (15%) employees were absent from work for 89 days (mean 3.6 days, SD ± 1.8), and during the entire influenza season 33 for 175 (5.3 ± SD 4.6) days, respectively. The economic burden related to the outbreak was 114 373 CHF (106 890 €, 112 131 $).

Background: The threat of pandemic A/H1N1 influenza is still a matter of considerable public concern. Influenza outbreak in 2009 underlined the importance of rapid production of a sufficient vaccine reserve for pandemic and interpandemic periods. One promising way to allay this concern is development of cell culture-derived live attenuated influenza vaccines (LAIV), because this technology makes it possible to produce a considerable amount of vaccine over a short period of time.

To elucidate the social effects of an influenza outbreak, the World Health Organization recommends a concept for excess mortality attributable to an influenza outbreak. However, because several models exist to estimate excess mortality, we would like to ascertain the most appropriate of three models: the Center for Disease Control and Prevention (CDC) model, the seasonal autoregressive integrated moving average (SARIMA) model, and the National Institution of Infectious Diseases (NIID) model. Excess mortality is defined as the difference between the actual number of deaths and the epidemiological threshold. The epidemiological threshold is defined as upper bound of 95% confidence interval (CI) of the baseline. The actual number of deaths might be less than the baseline, which implies inconsistent with the definition of baseline. Especially, actual deaths fewer than the lower bound of 95% CI of baseline suggest the inappropriateness of a model of excess mortality. Among 123 months during epidemic periods, the NIID model found excess mortality in 56 months, CDC model in 31 months, and SARIMA model in 35 months. Conversely, the NIID model found negative excess mortality in only 2 months, but the CDC model and SARIMA model found it respectively for 10 and 33 months. Negative excess mortality represents the logical inconsistency of the model. Therefore, NIID model might be the best among the three models considered.

Summary During the 2017/18 influenza season, the authors' virology laboratory implemented the cobas® Influenza A/B & RSV (Roche Molecular Diagnostics, Pleasanton, CA, USA) for influenza outbreak management in two scenarios: initial outbreak investigation or at outbreak conclusion to avoid prolonged measures. Twenty-seven investigations were conducted, including declaration of 11 influenza A/B outbreaks. Thirty percent of investigations would have missed the standard batched daily laboratory-developed respiratory polymerase chain reaction (PCR), and delayed outbreak confirmation until the following day. The average reduction in turnaround time for influenza A/B testing was 10.2 h. A rapid molecular PCR in specific outbreak scenarios improved timely management of influenza outbreaks.

Individual and/or raw data obtained in this study are available on request from the corresponding author.; International audience; In autumn/winter 2016–2017, HPAI-H5N8 viruses belonging to the A/goose/Guandong/1/1996 (Gs/Gd) lineage, clade 2.3.4.4b, were responsible for outbreaks in domestic poultry in Europe, and veterinarians were requested to reinforce surveillance of pigs bred in HPAI-H5Nx confirmed mixed herds. In this context, ten pig herds were visited in southwestern France from December 2016 to May 2017 and serological analyses for influenza A virus (IAV) infections were carried out by ELISA and hemagglutination inhibition assays. In one herd, one backyard pig was shown to have produced antibodies directed against a virus bearing a H5 from clade 2.3.4.4b, suggesting it would have been infected naturally after close contact with HPAI-H5N8 contaminated domestic ducks. Whereas pigs and other mammals, including humans, may have limited sensitivity to HPAI-H5 clade 2.3.4.4b, this information recalls the importance of implementing appropriate biosecurity measures in pig and poultry farms to avoid IAV interspecies transmission, a prerequisite for co-infections and subsequent emergence of new viral genotypes whose impact on both animal and human health cannot be predicted.

Influenza and COVID-19 pandemics are among the deadliest respiratory diseases recorded in the history of humanity. Influenza infections are difficult to diagnose and nearly impossible to predict because the disease symptoms changes as the pathogen(s) evolve. The U.S. was the case study for this research (Latitude: 37.0902°N and Longitude: 95.7129°W). The principle employed in the design of the influenza forecast model was “The Integrated Independent System of Disease Prediction (IISDP)”, where the functionality of one forecast model depends on the predictive capacity of one or more forecast models. It was predicted that 49,734,427 individuals will be infected in 2021 out of the current estimation of 322,900.000 U.S. citizens (only about 1.5% of the total population). The situation was estimated by Etaware-Pred-2021 to worsen in 2050 (300,803,433 infected individuals out of the estimated 400,000,000 U.S. citizens), with a geometric increase in the ratio of infected individuals from 1.5% (in 2021) to 75.2% (in 2050), if necessary health precautions fail. unless there are potent influenza vaccines in circulation, total compliance to influenza vaccination and a high level of personal hygiene among U.S. citizens, influenza infection will become a global threat to humanity. These situations can be averted if the U.S. citizens act accordingly.

Previous exposure to influenza viruses confers cross-immunity against future infections with related strains. However, this is not always accounted for explicitly in mathematical models used for forecasting during influenza outbreaks. We show that, if an influenza outbreak is due to a strain that is similar to one that has emerged previously, then accounting for cross-immunity explicitly can improve the accuracy of real-time forecasts. To do this, we consider two infectious disease outbreak forecasting models. In the first (the "1-group model"), all individuals are assumed to be identical and cross-immunity is not accounted for. In the second (the "2-group model"), individuals who have previously been infected by a related strain are assumed to be less likely to experience severe disease, and therefore recover more quickly, than immunologically naive individuals. We fit both models to estimated case notification data (including symptomatic individuals as well as laboratory-confirmed cases) from Japan from the 2009 H1N1 influenza pandemic, and then generate synthetic data for a future outbreak by assuming that the 2-group model represents the epidemiology of influenza infections more accurately. We use the 1-group model (as well as the 2-group model for comparison) to generate forecasts that would be obtained in real-time as the future outbreak is ongoing, using parameter values estimated from the 2009 epidemic as informative priors, motivated by the fact that without using prior information from 2009, the forecasts are highly uncertain. In the scenario that we consider, the 1-group model only produces accurate outbreak forecasts once the peak of the epidemic has passed, even when the values of important epidemiological parameters such as the lengths of the mean incubation and infectious periods are known exactly. As a result, it is necessary to use the more epidemiologically realistic 2-group model to generate accurate forecasts. Accounting for cross-immunity driven by exposures in previous outbreaks explicitly is expected to improve the accuracy of epidemiological modelling forecasts during influenza outbreaks.

In this paper, a SEIAR (Susceptible, Exposed, Infected, Asymptomatic and Recovered individuals) model of influenza which consists of 5 states is considered. Then, considering the nonlinearities in the model, the Linear Parameter Varying (LPV) model is generated and by using two control factors including vaccination and antiviral treatment, we try to control and prevent the influenza outbreak. Next, an appropriate controller is designed using LMI (Linear Matrix Inequality) formulations. The suggested controller is applied to the simulated model and it is seen that it can perfectly control the system and prevent divergent behavior of the Influenza disease.

Introduction Influenza is a globally occurring viral respiratory infection that can lead to hospitalizations and death. An influenza outbreak can interfere with combat readiness in a military setting, as the infection can incapacitate soldiers. Vaccination remains the most effective tool to prevent and mitigate seasonal influenza. Although influenza vaccinations for U.S. Army soldiers can be monitored through military health systems, those systems cannot capture DoD civilians and Army dependents who may not use military health services. This study aims to gauge flu vaccine uptake and perceptions in U.S. Army civilians and dependents. Materials and Methods An online survey was e-mailed to civilian and dependent enrollees of Landstuhl Regional Medical Center. The survey contained 24 questions pertaining to demographics, vaccine history, history of the flu, and beliefs toward vaccines. Chi-square tests, t-tests, and logistic regressions were performed to investigate the association between demographic, behavior, and belief factors with vaccine uptake. Free-text answers were coded and categorized by themes. Results Over 70% of respondents were vaccinated for the flu. There were differences between vaccinated and unvaccinated respondents regarding their perceptions of barriers to vaccination, benefits of the flu vaccine, severity of flu symptoms, and personal risk of getting ill with the flu. After controlling for confounders, flu vaccination in the previous season and healthcare worker status were associated with increased vaccine uptake, while perceived barriers to influenza vaccination were associated with decreased vaccine uptake. Conclusions Flu vaccine uptake may be increased by increasing access to vaccination, promoting vaccination and addressing concerns at the provider level, and engaging positively framed public messaging. Increasing flu vaccine uptake is of particular importance as the flu season approaches during the COVID-19 (Coronavirus disease 2019) pandemic.

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance

The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.