Socioeconomic and environmental patterns behind H1N1 spreading in Sweden

Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.

National Influenza Annual Report, Canada, 2020–2021, in the global context

During the 2020–2021 Canadian influenza season, no community circulation of influenza occurred. Only 69 positive detections of influenza were reported, and influenza percent positivity did not exceed 0.1%. Influenza indicators were at historical lows compared with the previous six seasons, with no laboratory-confirmed influenza outbreaks or severe outcomes being reported by any of the provinces and territories. Globally, influenza circulation was at historically low levels in both the Northern and the Southern Hemispheres. The decreased influenza activity seen in Canada and globally is concurrent with the implementation of non-pharmaceutical public health measures to mitigate the spread of the coronavirus disease 2019 (COVID-19). Although it is difficult to predict when influenza will begin to re-circulate, given the increased COVID-19 vaccination and the relaxation of public health measures, an influenza resurgence can be expected and may be more severe or intense than recent seasons. Influenza vaccination, along with non-pharmaceutical public health measures, continues to remain the best method to prevent the spread and impact of influenza. Public health authorities need to remain vigilant, maintain surveillance and continue to plan for heightened seasonal influenza circulation.

Clustering Analysis Influenza Disease to Identify Spatio-Temporal Spread Pattern in Thailand

The pandemics of influenza in Nonthaburi province was investigated by using autoregression and found the influenza spread pattern by autocorrelation (Moran's I). Population density, temperature, relative humidity, and rainfall are the factors used in the analysis. The influenza quantitative cross-section retrospective research design was employed from 2003-2010. Three seasons are classified as: hot, rainy, and winter season. The study found that influenza outbreaks in the rainy season was R2=0.45 and population density apparently affected the spread of influenza incidence with statistical significance coefficient (p-value <0.05). From the distribution pattern, the highest Moran's I values were related with the highest population density in 4 sub-districts: Suenyai, Taladkhwun, Bangkhen, and Bangkruay sub-district.

Molecular epidemiologic characteristics of hemagglutinin from five waves of avian influenza A (H7N9) virus infection, from 2013 to 2017, in Zhejiang Province, China

There have been five waves of influenza A (H7N9) epidemics in Zhejiang Province between 2013 and 2017. Although the epidemiological characteristics of the five waves have been reported, the molecular genetics aspects, including the phylogeny, evolution, and mutation of hemagglutinin (HA), have not been systematically investigated. A total of 154 H7N9 samples from Zhejiang Province were collected between 2013 and 2017 and sequenced using an Ion Torrent Personal Genome Machine. The starting dates of the waves were 16 March 2013, 1 July 2013, 1 July 2014, 1 July 2015, and 1 July 2016. Single-nucleotide polymorphisms (SNPs) and amino acid mutations were counted after the HA sequences were aligned. The evolution of H7N9 matched the temporal order of the five waves, among which wave 3 played an important role. The 55 SNPs and 14 amino acid mutations with high frequency identified among the five waves revealed the dynamic occurrence of mutation in the process of viral dissemination. Wave 3 contributed greatly to the subsequent epidemic of waves 4 and 5 of H7N9. Compared with wave 1, wave 5 was characterized by more mutations, including A143V and R148K, two mutations that have been reported to weaken the immune response. In addition, some amino acid mutations were observed in wave 5 that led to more lineages. It is necessary to strengthen the surveillance of subsequent H7N9 influenza outbreaks.

Lymphocyte monocyte ratio is an effective and simple predictor for nosocomial influenza outbreaks

ObjectivesNosocomial influenza outbreak detection remains challenging. We evaluated the diagnostic utility of blood cell parameters, along with their capacity to differentiate between hospital acquired influenza and coronavirus disease 2019 (COVID-19).MethodsWe retrospectively analyzed patients diagnosed with nosocomial influenza from January 2017 to December 2019, and patients with COVID-19 in early 2020 at a tertiary teaching hospital in Beijing, China. We compared the differences between blood cell count and ratios (lymphocyte-to-monocyte ratio [LMR], neutrophil-to-lymphocyte ratio [NLR], lymphocyte-to-platelet ratio [LPR]) at symptom onset, before (admission), and after (recovery) nosocomial influenza. We also compared the abovementioned parameters between influenza and COVID-19 patients.ResultsLymphocyte count, LMR, and LPR were significantly lower in the symptom onset than in the admission and recovery groups (p < 0.001), while NLR was higher (p < 0.001). LMR and NLR exhibited similar and consistent tendencies among different subgroups of patients with nosocomial influenza (p < 0.001). The area under the receiver operating curve (AUC) of LMR, NLR, LPR, and lymphocyte count were 0.914, 0.872, 0.806, and 0.866, respectively. The optimal LMR cut-off value was 2.50, with specificity and sensitivity of 92.0% and 81.3%, respectively. Peripheral blood cell ratios can help diagnose nosocomial influenza significantly earlier than conventional methods. For differentiating influenza and COVID-19, the AUCs of LMR was 0.825.ConclusionsLMR effectively predicts nosocomial influenza outbreaks, particularly during the COVID-19 pandemic when simultaneous transmission can be a substantial threat.

948 Feasibility Study of a Drive-Through Influenza Vaccination Clinic for Patients-At-Risk & Their Contacts

Background There are 3-5 million cases of severe influenza-like illness globally each year, and up to 650,000 related deaths. This high prevalence rate proves to be a heavy burden on the healthcare system with &gt;3,000 hospitalisations and &gt;150 ICU admissions annually. Immunisation is gold-standard for the prevention of influenza outbreaks. The HSE Influenza Immunisation Strategy allocates vaccines to be administered in primary care to patients at-risk and their contacts. However, due to the COVID-19 pandemic, this has become a logistical challenge. We aimed to design and test a drive-through influenza vaccine clinic at a large GP practice in Cork. Method We designed and implemented an online booking system for at-risk patients and their contacts. 1-minute drive-through time slots were available to book for up to 6 people per vehicle. The primary measurement was the number of patients vaccinated with a secondary measurement of time spent vaccinating these patients. Results The pilot clinic occurred on 10/10/2020 with over 600 patients-at-risk & their contacts receiving the influenza vaccination over a time period of 10 hours. The capacity of this clinic was limited by the supply of vaccines. We estimate that 1,800 people could be vaccinated over the same time period with adequate vaccine supply. Conclusions A drive-through influenza vaccination clinic can be efficiently run using an online booking system and serves as a safe, efficient, and convenient way for patients-at-risk & their contacts to receive vaccinations. This system can be rolled out efficiently each winter for influenza vaccination and could be expanded to deliver mass vaccination for SARS-CoV-2.

A novel data-driven methodology for influenza outbreak detection and prediction

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.

Global Transmission of SARS-COV-2 in Schools, Religious Centres and Markets: An Exploratory Review

Context: The transmission of COVID-19 was reported to have started at a Seafood Market in Wuhan, China predominantly through droplets from coughing and sneezing. Gatherings like schools, religious and worship centers as well as market places are usually densely populated and congested thereby facilitating the spread of the virus via droplets. This research aims to explore the transmission of COVID-19 in schools, religious gatherings and markets. Evidence Acquisition: Literature search of available evidences was conducted on biomedical databases such as PubMed and Google Scholar using keywords, and articles that met inclusion criteria were selected. Results: Results show that transmission of SARS-CoV-2 has been recorded in schools, religious centres and market places in different countries and regions. Transmission was found to be less prevalent among school children unlike in influenza outbreaks due to some notable factors highlighted in the articles. Numerous evidences stated cases of transmission of SARS-CoV-2 linked to intimacy and close contacts in religious gatherings. Transmission in market place marked the genesis of the pandemic at Huanan Seafood Wholesales Market, Wuhan although only limited evidences are available about transmission in other market places in the world. Conclusions: Although these gatherings are seen to be vital to our daily lives, they are risk settings for SARS-CoV-2 transmission. It is important for government to ensure strict compliance to the COVID-19 protocols in order mitigate the spread of the virus causing the current pandemic.