SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals

Lau, Krystal; Dorigatti, Ilaria; Miraldo, Marisa; Hauck, Katharina

doi:10.1016/j.ijid.2021.01.070

Cited by 7 publications

(6 citation statements)

References 22 publications

(12 reference statements)

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“…Non-pharmaceutical interventions are the methods to combat the ongoing COVID-19 pandemic's adverse impact, apart from taking medicines, or getting vaccinated, which communities and ordinary people can take as preventive measures to help minimize the rapid spread of the infectious disease. The past studies on the Spanish influenza 1918 (H1N1) pandemic showed that close settings, army barracks, college and university dormitories, early patients' identification, and isolation of the infected patients did not stop the infection transmission ( 18 ). However, it was helpful to decrease rapid spread, especially with restrictions on social gatherings and travel bans to and from the adjacent communities ( 111 ).…”

Section: Npis (Non-pharmaceutical Interventions) In the Absence Of Co...mentioning

confidence: 99%

“…The WHO declared the outbreak as an "International Concern" related to the Public Health Emergency on January 30, 2020. WHO later called it a global pandemic on March 3, 2020 (12)(13)(14)(15). The rapid spread of COVID-19 has massively infected people worldwide and caused public anxiety similar to the 1918 H1N1 influenza plague instigated by an "H1N1 Influenza A" viral disease (16)(17)(18). The Spanish influenza pandemic persisted from February 1918 to April 1920.…”

Section: Introductionmentioning

confidence: 99%

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Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

et al. 2022

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The new identified virus COVID-19 has become one of the most contagious diseases in human history. The ongoing coronavirus has created severe threats to global mental health, which have resulted in crisis management challenges and international concerns related to health issues. As of September 9, 2021, there were over 223.4 million patients with COVID-19, including 4.6 million deaths and over 200 million recovered patients reported worldwide, which has made the COVID-19 outbreak one of the deadliest pandemics in human history. The aggressive public health implementations endorsed various precautionary safety and preventive strategies to suppress and minimize COVID-19 disease transmission. The second, third, and fourth waves of COVID-19 continue to pose global challenges to crisis management, as its evolution and implications are still unfolding. This study posits that examining the strategic ripostes and pandemic experiences sheds light on combatting this global emergency. This study recommends two model strategies that help reduce the adverse effects of the pandemic on the immune systems of the general population. This present paper recommends NPI interventions (non-pharmaceutical intervention) to combine various measures, such as the suppression strategy (lockdown and restrictions) and mitigation model to decrease the burden on health systems. The current COVID-19 health crisis has influenced all vital economic sectors and developed crisis management problems. The global supply of vaccines is still not sufficient to manage this global health emergency. In this crisis, NPIs are helpful to manage the spillover impacts of the pandemic. It articulates the prominence of resilience and economic and strategic agility to resume economic activities and resolve healthcare issues. This study primarily focuses on the role of social media to tackle challenges and crises posed by COVID-19 on economies, business activities, healthcare burdens, and government support for societies to resume businesses, and implications for global economic and healthcare provision disruptions. This study suggests that intervention strategies can control the rapid spread of COVID-19 with hands-on crisis management measures, and the healthcare system will resume normal conditions quickly. Global economies will revitalize scientific contributions and collaborations, including social science and business industries, through government support.

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Section: Npis (Non-pharmaceutical Interventions) In the Absence Of Co...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

et al. 2022

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“…According to this model, they found that the network search index combining with temperature could be used to predict in uenza outbreak [20]. The SARIMA model constructed by Krystal Lau et al indicated that hospitalizations did not lag infections for young people (< 24 years old) during the H1N1 pandemic but lagged by one or more weeks for all ages in post-pandemic periods [21]. However, few studies have compared the model effects at different forecast points and for different prediction periods.…”

Section: Discussionmentioning

confidence: 99%

Comparative study on influenza time series prediction models in a megacity from 2010 to 2019: Based on SARIMA and deep learning hybrid prediction model

Yang

et al. 2022

Preprint

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Background: It is very important to establish more accurate influenza prediction models in megacities. The purpose of this study was to compare the different time series prediction models for influenza from 2010 to 2019 in Beijing, China. Methods: We took the influenza-like illness rate (ILI%), the influenza positive rate and the product of ILI% and influenza positive rate as dependent variables respectively. Subsequently, and model performances of summer point, peak bottom point and peak rising point were analyzed. After selecting the best prediction point, we compared the model performances of different parameters at that point using the SARIMA model. Then, the best model selected by SARIMA was compared with the hybrid LSTM model. Results: Between the 26th week of 2010 and the 25th week of 2019, there were 6,753,116(1.24%) ILI patients, 15,883(16.75%) of which were positive for influenza.The trends and the peak times of ILI%, the influenza positive rate and the product of ILI% and influenza positive rate were roughly the same. The SARIMA model of the peak rising point was better than those of the summer point and peak bottom point. The hybrid LSTM model performed better than the selected best SARIMA model in terms of ILI%, influenza positive rate and the product of ILI% and influenza positive rate. Also, the hybrid LSTM model could maintain a good prediction effect from the 1st to the 26th week.. On the contrary, the prediction effect of the SARIMA model decreased significantly with the extension of the prediction period. Conclusions: Our results suggested that the prediction effect of the hybrid LSTM model was better than the SARIMA model, in terms of ILI%, influenza positive rate and the product of ILI% and influenza positive rate. SARIMA was more suitable for short-term prediction, while the hybrid LSTM model showed obvious advantages in long-term prediction. Our research could help to improve the prediction and early warning of influenza and other respiratory infectious diseases.

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“…Thus, the simple moving average method is applied to modeling COVID-19 in Iraq [26], USA [27], Pakistan [28], Italy and Spain [29], China [30], etc. Also popular are such derivatives of the method as the exponential moving average [31][32][33], ARIMA (autoregressive moving average) [34][35][36], and SARIMA (seasonal autoregressive moving average) models [37][38][39].…”

Section: Statistical Modelsmentioning

confidence: 99%

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Chumachenko

2022

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The paper is devoted to an overview of the current state of research on the modeling of epidemic processes. The classification of mathematical and simulation models of epidemic processes is carried out. The disadvantages of classical models are revealed. Specific characteristics inherent in epidemic processes have been determined, which must be taken into account when constructing mathematical and simulation models. A review of deterministic compartment models is carried out. Various methods and approaches to the construction of statistical models of epidemic processes are considered. The types of problems that are solved using machine learning are analyzed.

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SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals

Cited by 7 publications

References 22 publications

Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

Comparative study on influenza time series prediction models in a megacity from 2010 to 2019: Based on SARIMA and deep learning hybrid prediction model

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

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