Number Of COVID-19 Infections Research Articles

Factors affecting the number of influenza patients before and during COVID-19 pandemic, Thailand.

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014-2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (β) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted β = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted β = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted β = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted β = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted β = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted β = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted β = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted β = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted β = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

Using Regression Model to Study the Significant Differences in the Number of Covid-19 Infections

Multiple linear regression modeling was used to analyze Covid-19 data, which represents the number of infections in Iraq for the year 2019, in order to find occupational discrepancies between the dependent variable (age) and the independent factors under discussion. This was carried out in addition to the correlation value. Following statistical analysis, a significant correlation was discovered between the independent factors and the dependent variable, age. The age dependent variable and the research's independent variables are evidently different from one another in a substantial degree, as indicated by the analysis of variance table. This is in addition to comparing the number of infections between boys and females for each of the study's variables (blood pressure, oxygen rate, sugar rate, and D-Dimer), and we discovered that there are no appreciable variations in the quantity of infections between males and females based on the factorial experiment analysis.

The worldwide COVID-19 pandemic caused a decline in sonographic examinations - is this a continuing trend?

Due to the increasing number of COVID-19 infections since spring 2020 the patient care workflow underwent changes in Germany. To minimize face-to-face exposure and reduce infection risk, non-time-critical elective medical procedures were postponed. Since ultrasound examinations include non-time-critical elective examinations and often can be substituted by other imaging modalities not requiring direct patient contact, the number of examinations has declined significantly. The aim of this study is to quantify the baseline number of ultrasound examinations in the years before, during, and in the early post-pandemic period of the COVID-19 pandemic (since January 2015 to September 2023), and to measure the number of examinations at different German university hospitals. The number of examinations was assessed based on a web-based database at all participating clinics at the indicated time points. N = 288 562 sonographic examinations from four sites were included in the present investigation. From January 2020 to June 2020, a significantly lower number of examinations of n = 591.21 vs. 698.43 (p = 0.01) per month and included center was performed. Also, excluding the initial pandemic period until June 2020, significantly fewer ultrasound examinations were performed compared to pre-pandemic years 648.1 vs. 698.4 (p < 0.05), per month and included center, while here differences between the individual centers were observed. In the late phase of the pandemic (n = 681.96) and in the post-pandemic phase (as defined by the WHO criteria from May 2023; n = 739.95), the number of sonographic examinations returned to pre-pandemic levels. The decline in the number of sonographic examinations caused by the COVID-19 pandemic was initially largely intentional and can be illustrated quantitatively. After an initial abrupt decline in sonographic examinations, the pre-pandemic levels could not be reached for a long time, which could be due to restructuring of patient care and follow-up treatment. In the post-pandemic phase, the pre-pandemic level has been achieved again. The reasons for a prolonged reduction in ultrasound examinations are discussed in this article. · During the pandemic, significantly fewer ultrasound examinations were performed in the included centers.. · The number of examinations could not be reach the pre-pandemic level for a long time, which could be due to restructuring of patient care and follow-up treatment.. · Identifying causes for sonographic exam reduction is crucial in pandemic preparedness to uphold healthcare quality and continuity for all patients.. · The prolonged decline in sonographic examinations during the pandemic does not represent a lasting trend, as evidenced by the return to pre-pandemic levels..

Data analysis based on COVID-19Important factors in the COVID-19 outbreak

Data analysis based on COVID-19Important factors in the COVID-19 outbreak

Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation.

Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults. Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a "non-re-positive" group and a "re-positive" group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model's goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively. We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ2 =5.916, P =0.657; the AUC was 0.881; when the threshold probability was >8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was >80%, the positive estimated value was closer to the actual number of cases. This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies tofurther validate, refine, and update this model.

Demand assessment of the number of inpatient beds in China based on the COVID-19 dynamical model

COVID-19 is a worldwide pandemic that poses a great threat to people’s health and has a huge impact on economic and social life. The epidemic is currently at a low level, but is still prevalent worldwide. Therefore, it is still important to study the prevention and control of the spread of the COVID-19 epidemic. We collected the numbers of COVID-19 infections in China during the past three years, and obtained the regional distribution of outbreaks through analysis. To assess the capacity of medical resources in these regions in response to the outbreaks of COVID-19, we developed a model of COVID-19 transmission dynamics including mild and severe cases. The simulation showed that 11 regions, Beijing, Shanghai, Heilongjiang, Yunnan, Xinjiang, Inner Mongolia, Liaoning, Jilin, Hainan, Guangdong and Fujian, all have a serious shortage of medical beds in reserve. When facing the next new outbreak, these areas will have insufficient medical resources. We simulated the effects of drug effectiveness and the proportion of asymptomatic infected individuals on the spread of the epidemic, and obtained that an accurate determination of the proportion of asymptomatic patients in the population is crucial to the impact on healthcare resources, while improving the effectiveness of drugs can significantly reduce the burden on healthcare resources. Regions should invest more in healthcare and increase more medical resources, including medical beds. Government should accelerate the development of effective COVID-19 drugs to effectively control the scale of the outbreak. The proportion of asymptomatic patients in the population has a significant impact on the allocation of healthcare resources. The monitoring of antibody levels in the population should be strengthened to better control the spread of the epidemic.

Modelling of the Explanatory Factors of the Number of COVID-19 Deaths for a Panel of Countries

This research attempts to explain the number of COVID-19 deaths. We use in this sense non-spatial and spatial econometric modelling for 14 socio-economic and geographic variables for a panel of 112 countries. The number of hospital beds and doctors in a country has a negative impact on the number of COVID-19 deaths. The proportion of the population aged 65 and over in a country, the number of tourist arrivals and its COVID-19 infections have a positive effect on COVID-19 deaths. The number of COVID-19 deaths in a country depends on the observable characteristics of its neighbours. When the number of COVID-19 infections in neighbouring countries of a given country increases and the proportion of their populations aged 65 and over is high, the number of COVID-19 deaths in the affected country increases. There is no substantial impact of diabetes prevalence, stringency index, even temporally lagged and population density on the reduction in the number of COVID-19 deaths. This research highlights the significant impact of five explanatory variables among the 14 variables selected on the number of COVID-19 deaths. There is a spatial autocorrelation for the variables selected. The spatial models show the existence of an exogenous interaction effect.

The response of gold to the COVID-19 pandemic

PurposeThis study aims to investigate how the gold return and its volatility respond to the COVID-19 pandemic.Design/methodology/approachStochastic volatility (SV) models are conducted to examine the response of gold to the number of new confirmed cases and deaths.FindingsThe results indicate that an increase in the change rate of the number of COVID-19 infections or fatalities leads to heightened volatility in gold prices. Moreover, the results suggest that gold volatility is more sensitive to the impacts from high-income countries than by those from middle- and low-income countries. In addition, the asymmetric effect is detected in the gold price volatility, which is contrary to the typical asymmetric effect seen in the stock market. Furthermore, the results remain robust after accounting for the US dollar and the volatility index in relation to gold returns.Practical implicationsThis study presents whether and to what extent gold is incorporated in the information related to the number of COVID-19 cases and deaths.Originality/valueThis study augments the existing literature by exploring how the number of COVID-19 infections and fatalities influences gold prices. In addition, it examines the day-of-the-week and asymmetric effects that may contribute to the volatility of gold prices. To the best of the authors’ knowledge, the evolution of gold has not yet been investigated using SV models.

COVID-19 endemic phase in Finland: An analysis of health policies and vaccination strategy

ObjectivesTo analyze how the vaccines and various measures to control the pandemic affected epidemiological, health and socioeconomic outcomes of COVID-19 in Finland. The focus of the analysis is on the endemic phase of the COVID-19 pandemic. MethodsThe paper provides an overview of Finland's healthcare system, trends in COVID-19 morbidity, mortality and vaccination coverage data, political considerations, interventions to control the pandemic, as well as the economic impact of the pandemic in the endemic phase. Data were collected from various sources, including previous studies, government reports, national statistics and registers and general media. ResultsIn Finland, the total number of COVID-19 infections increased significantly during 2022, but the number of serious forms of the disease decreased. The implementation of the vaccination strategy caused a diversity of opinions among authorities and experts. The governing of the pandemic was fully decentralized. Overall, there is a good vaccination coverage of the population. In the endemic phase society returned to live without restrictions. ConclusionsIt seems clear that vaccines played an important role in controlling the pandemic. Overall mortality increased substantially in 2022 causing life expectancy to fall. Moreover, different “excess death” indicators show an increase in 2021 and 2022, but the timing and magnitude of the effect varies. It is relatively safe to conclude that at least part of increase can be attributed to the pandemic, but a more exact conclusion calls for a comprehensive study. Similarly, understanding long covid and designing required intervention calls for more research.

Engaging Community-Based Organizations to Address Barriers in Public Health Programs: Lessons Learned From COVID-19 Vaccine Acceptance Programs in Diverse Rural Communities.

Factors such as geography, community hesitancy, the political landscape, and legislative efforts to limit public health authority have contributed to a disproportionate number of COVID-19 infections and deaths in US rural communities. Community-based organizations are trusted entities that provide social and educational services in the communities where they live and have proven to be effective public health partners in response to the COVID-19 pandemic. Recognizing the unique challenges faced by rural communities, coupled with higher rates of vaccine hesitancy, the CDC Foundation awarded grants to 21 community-based organizations serving rural communities in 7 Midwest states to support the equitable uptake and distribution of COVID-19 vaccines. In this case study, 2 grantees, the Missouri Center for Public Health Excellence and the Hmong American Center, provide case studies that document their experiences, challenges, and strategies for overcoming barriers during the implementation of COVID-19 vaccine acceptance projects in diverse rural communities. These case studies provide key lessons learned that can be applied to future public health emergency and nonemergency responses to ensure that all members of communities are served well and protected.

Mô hình dự báo khả năng tăng giá cổ phiếu ngành vận tải Việt Nam

This study aims to build a model to forecast stock price appreciation of transportation enterprises in Vietnam in the period of 2020-2021 using artificial intelligence-artificial neural network (ANN) method. The study uses a quarterly database collected from Vietstock of 93 transport enterprises. The results from ANN show that the important predictors of the stock price appreciation model include revenue (DT), previous stock price (PT-1), earnings per share, and earnings per share. share (EPS), inflation (INF), number of Covid-19 infections (CNM) and price-to-earnings (P/E) ratio. This study can also rank these predictors by impact level including revenue, previous stock price, EPS, INF, Covid-19 cases, and P/E. In addition, the study also compares the prediction ratio between the ANN model and the Logistics regression model. The results show that for the small input data set, the Logistics regression model has higher predictive accuracy than the ANN model.

The impact of the COVID-19 outbreak on intra- and inter-regional domestic travel: Evidence from Spain

This study empirically compares domestic tourists’ behavior before and after the Covid-19 outbreak. Specifically, official data are used to characterize the travel behavior of residents in Spain who traveled through this country for reasons of leisure, recreation, and vacations in 2019 and 2020. A discrete choice model is employed to unravel the main variables that influence the decision of being an inter-regional traveler. The bootstrap p-value method is used to detect significant changes in the marginal effect of the different variables after the Covid-19 outbreak. The estimation results demonstrate the following: (i) domestic inter- and intra-regional travelers have significant differences and, therefore, policy-makers should not design and implement “one size fits all” policies for domestic tourism; (ii) in addition to socio-economic factors, the regional push-pull factors (e.g., weather) and trip-related characteristics (e.g., type of accommodation) are also important in explaining inter-regional travel decisions; (iii) a high number of Covid-19 infections in the region of origin increases the probability of traveling inter-regionally; and (iv) the Covid-19 outbreak has caused an abrupt shift in domestic travelers’ profiles.

A Study of the Impact of COVID-19 on Urban Contact Networks in China Based on Population Flows

The emergence and enduring diffusion of COVID-19 has had a dramatic impact on cities worldwide. The scientific aim of this study was to introduce geospatial thinking to research related to infectious diseases, while the practical aim was to explore the impact on population movements and urban linkages in the longer term following a pandemic outbreak. Therefore, this study took 366 cities in China as the research subjects while exploring the relationship between urban contact and the outbreak of the pandemic from both national and regional perspectives using social network analysis (SNA), Pearson correlation analysis and multi-scale geographically weighted regression (MGWR) modeling. The results revealed that the number of COVID-19 infections in China fluctuated with strain variation over the study period; the urban contact network exhibited a significant trend of recovery. The pandemic had a hindering effect on national urban contact, and this effect weakened progressively. Meanwhile, the effect exhibited significant spatial heterogeneity, with a weakening effect in the eastern region ≈ northeast region &gt; central region &gt; western region, indicating a decreasing phenomenon from coastal to inland areas. Moreover, the four major economic regions in China featured border barrier effects, whereby urban contact networks constituted by cross-regional flows were more sensitive to the development of the pandemic. The geostatistical approach adopted in this study related to infectious disease and urban linkages can be used in other regions, and its findings provide a reference for China and other countries around the world to respond to major public health events.

A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.

Anxiety, depression, insomnia, and PTSD among college students after optimizing the COVID-19 response in China

BackgroundThe number of COVID-19 infections has increased sharply and quickly after optimizing the COVID-19 response in China. In the context of this population-size infection, college students' psychological response is yet to be understood. MethodsA cross-sectional study was designed to investigate anxiety, depression, insomnia, and post-traumatic stress disorder (PTSD) symptoms among college students from December 31, 2022, to January 7, 2023. The questionnaire included the Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), Insomnia Severity Index (ISI), Impact of Event Scale (IES-R), and self-designed questionnaire. ResultsOf the 22,624 respondents, the self-reported prevalence of anxiety, depression, insomnia, PTSD, and any of the four psychological symptoms appeared as 12.7 %, 25.8 %, 11.6 %, 7.9 %, and 29.7 %, respectively. The self-reported COVID-19 infection rate was 80.2 %. Changes in the place for learning, longer time online, not recovering after infection, a higher proportion of family member infection, insufficient drug reserve, worry about sequela after infection, future studies, or employment contributed to a higher risk of anxiety/depression/insomnia symptoms or PTSD symptoms. Multinomial logistic regression showed that those who spent more extended time on the Internet, recovered after infection, and had insufficient drug reserves were less likely to have PTSD than anxiety/depression/insomnia symptoms. LimitationsThe study was a non-probability sampling survey. ConclusionsAnxiety, depression, insomnia, and PTSD were common psychological symptoms among college students when infection went through a large-scale population. This study highlights the importance of continuing to care for the psychological symptoms of college students, especially timely responses to their concerns related to the epidemic situation and COVID-19 infection.

Analysis of Turberculosis-COVID-19 Coinfection Using Fractional Derivatives

Fractional-order derivative modeling continues to receive great interest among researchers across the globe. In this study, Tuberculosis-COVID-19 coinfection is studied using Atangana–Baleanu fractional-order derivatives defined in Caputo sense. We confirmed the existence and singularity of the solution and investigated the model’s equilibrium points. Additionally, we examined the model’s stability in terms of the Ulam–Hyers and generalized Ulam–Hyers stability criteria. The basic reproduction number R 0 was calculated using the next-generation matrix approach. We also looked into the model’s disease-free equilibrium point’s regional stability. Numerical scheme for simulating the fractional-order system with Mittag–Leffler Kernels are presented. Numerical simulations are given to validate the model. Results of the simulation showed a decline in the number of COVID-19 infections within the population when the fractional operator was reduced.

Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic.

Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public's practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13-1.17, p<0.001). AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people's mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.

Bayesian Deep Learning Applied to LSTM Models for Predicting COVID-19 Confirmed Cases in Iraq

The COVID-19 pandemic has had a huge impact on populations around the world and has caused critical problems to medical systems. With the increasing number of COVID-19 infections, research has focused on forecasting the confirmed cases to make the right medical decisions. Despite the huge number of studies conducted to forecast the COVID-19 patients, the use of Deep Learning (DL) and Bayesian DL models are limited in this field in Iraq. Therefore, this research aims to predict the confirmed cases of COVID-19 in Iraq using classical DL models such as, Long-Short-Term-Memory (LSTM) and Bayesian LSTM models. In this study, Bayesian Deep Learning (BDL) using LSTM models was used to predict COVID-19 confirmed cases in Iraq. The motivation behind using BDL models is that they are capable to quantify model uncertainty and provide better results without overfitting or underfitting. A Monte Carlo (MC) Dropout, which is an approximation method, is added to the Bayesian-LSTM to create numerous predictions for each instance and evaluate epistemic uncertainty. To evaluate the performance of our proposed models, four evaluation measures (MSE, RMSE, R2, MAE) were used. Experimental results showed that the proposed models were efficient and provided an R2 of 0.93 and 0.92, for vanilla LSTM and Bayesian-LSTM, respectively. Furthermore, the two proposed models were optimized using ADAM and SGD optimizers, with the results revealing that optimizing with ADAM provided more accurate results. Thus, we believe that these models may assist the government in making critical decisions based on short-term predictions of confirmed cases in Iraq.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020.

Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Retrospective Analysis of Vaccinated and Unvaccinated COVID-19 Patients Treated with Monoclonal Antibodies (mAb) and Their Emergent Needs (RAVEN).

Strategies to combat COVID-19 include vaccines and Monoclonal Antibody Therapy. While vaccines aim to prevent development of symptoms, Monoclonal Antibody Therapy aims to prevent the progression of mild to severe disease. An increasing number of COVID-19 infections in vaccinated patients raised the question of whether vaccinated and unvaccinated COVID-19 positive patients respond differently to Monoclonal Antibody Therapy. The answer can help prioritize patients if resources are scarce. We performed a retrospective study to evaluate and compare the outcomes and risks for disease progression between vaccinated and unvaccinated COVID-19 patients treated with Monoclonal Antibody Therapy by measuring the number of Emergency Department visits and hospitalizations within 14 days as well as the progression to severe disease, defined through the Intensive Care Unit admissions within 14 days, and death within 28 days from the Monoclonal Antibody infusion. From 3898 included patients, 2009 (51.5%) were unvaccinated at the time of Monoclonal Antibody infusion. Unvaccinated patients had more Emergency Department visits (217 vs. 79, p < 0.0001), hospitalizations (116 vs. 38, p < 0.0001), and progression to severe disease (25 vs. 19, p = 0.016) following treatment with Monoclonal Antibody Therapy. After adjustment for demographics and comorbidities, unvaccinated patients were 2.45 times more likely to seek help in the Emergency Department and 2.70 times more likely to be hospitalized. Our data suggest the added benefit between the COVID-19 vaccine and Monoclonal Antibody Therapy.