Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis.

BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. RESULTS: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above -0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. CONCLUSIONS: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.

One-year temporal changes in long COVID prevalence and characteristics: a systematic review and meta-analysis.

OBJECTIVES: This study aimed to explore the 1-year temporal change in prevalence, variety, and potential risk factors of long COVID symptoms, and to further predict the prognostic trends of long COVID. METHODS: We searched electronic databases for related studies published from January 2020 to February 2022, and conducted one group meta-analysis and locally weighted regression explore the monthly temporal change in the prevalence of each long COVID symptom in 1-year follow-up period. RESULTS: A total of 137 studies were included in meta-analysis, including 134,093 participants. The temporal change of any long COVID symptom showed a steep decrease initially (from 92% at acute phase to 55% at 1-month follow-up), followed by stabilization at approximately 50% during 1-year follow-up. Six months or more after the acute phase, the odds ratio (OR) of population characteristic factors increased, such as female gender (from 1.62 to 1.82), while the OR value of acute phase-related factors (severe/critical and hospitalization) decreased. As for specific symptoms, about two-thirds of the symptoms did not significantly reduce during the 1-year follow-up, and the neuropsychiatric symptoms showed a higher long-term prevalence (approximately 25%) and longer persistence than physical-symptoms. CONCLUSIONS: The temporal changes in the prevalence and characteristics speculate that long COVID may persist longer than expected. In particular, we should pay more attention to neuropsychiatric symptoms and other symptoms for which there is no significant downward trend in prevalence. The influence of acute phase-related factors for long COVID gradually decreases over time, while the influence of population characteristic-related gradually increases.

Quantitative Analysis of the Effectiveness of Antigen- and Polymerase Chain Reaction-Based Combination Strategies for Containing COVID-19 Transmission in a Simulated Community

The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible–latent–infectious–recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

Quantitative Analysis of the Effectiveness of Antigen- and Polymerase Chain Reaction-Based Combination Strategies for Containing COVID-19 Transmission in a Simulated Community.

The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

Healthcare Workers' Attitudes toward Influenza Vaccination: A Behaviour and Social Drivers Survey.

This study aimed to understand the intention and correlation of receiving and recommending influenza vaccine (IV) among healthcare workers (HCWs) in China during the 2022/2023 season using the behavior and social drivers (BeSD) tools. A self-administered electronic survey collected 17,832 participants on a media platform. We investigated the willingness of IV and used multivariate logistic regression analysis to explore its associated factors. The average scores of the 3Cs' model were compared by multiple comparisons. We also explored the factors that potentially correlated with recommendation willingness by partial regression. The willingness of IV was 74.89% among HCWs, and 82.58% of the participants were likely to recommend it to others during this season. Thinking and feeling was the strongest domain independently associated with willingness. All domains in BeSD were significantly different between the hesitancy and acceptance groups. Central factors in the 3Cs model were significantly different among groups (p < 0.01). HCWs' willingness to IV recommendation was influenced by their ability to answer related questions (r = 0.187, p < 0.001) after controlling for their IV willingness and perceived risk. HCWs' attitudes towards IV affect their vaccination and recommendation. The BeSD framework revealed the drivers during the decision-making process. Further study should classify the causes in detail to refine HCWs' education.

Long-term outcomes in COVID-19 patients who recovered from the first wave of the pandemic.

This cross-sectional study evaluated the long-term health effects of coronavirus disease 2019 (COVID-19) in Jianghan District (Wuhan, China). The results showed that 61.4% of COVID-19 patients reported at least one symptom and 8.8% had depressive symptoms at the 17-month follow-up. The proportion of patients with chest radiographic abnormalities in Fangcang shelter hospitals and designated COVID-19 hospitals was 31.6% and 41.1%, respectively, and the proportion of patients with impaired pulmonary diffusion capacity in these hospitals was 52.8% and 60.9%, respectively. Female sex (odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.16-1.88), severe disease (OR = 1.46, 95% CI: 1.01-2.10) and a higher number of initial symptoms (OR = 1.31, 95% CI: 1.23-1.40) were associated with the development of sequelae symptoms at 17 months. This study involving community-dwelling COVID-19 adults may help determine the long-term effects of COVID-19 during the first pandemic wave. Nonetheless, larger follow-up studies are needed to characterize the post-COVID-19 condition.

Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19.

More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.

Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens' quality of life and ensure an organization's seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.

Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19

More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.