This study investigates the effectiveness of multiple COVID-19 vaccinations on daily confirmed cases in Seoul City. Utilizing comprehensive data on vaccinated individuals and confirmed cases sourced from the official website of the Korean Ministry of the Interior and Safety, we conducted detailed statistical analyses to assess the impact of each vaccination dose. The study covers data from April 21, 2021, to September 29, 2022. Statistical multiple linear regression was employed to analyze the relationship between daily confirmed cases (positive outcomes from PCR tests) and multiple vaccine doses, using p-values as the criteria for determining the effectiveness of each dose. The analysis included data from four vaccination doses. The analysis reveals that the first, second, and third doses of the COVID-19 vaccines have a statistically significant positive effect associated with the daily confirmed cases. However, the study finds that the fourth dose does not show a statistically significant impact on the reduction of daily confirmed cases. This suggests that while the initial three doses are crucial for establishing and maintaining high levels of immunity, the incremental benefit of subsequent doses may diminish.

During the global COVID-19 pandemic, densely populated megacities engaged in active international exchanges have faced the most severe impacts from both the disease and the associated infodemic. This study examines the factors influencing public participation behavior on government microblogs in these megacities during the pandemic. It guides megacities in disseminating epidemic information, promoting knowledge on epidemic prevention, managing public opinion, and addressing related matters.
Utilizing the elaboration likelihood model\'s central and peripheral routes, drawing on an empirical analysis of 6,677 epidemic-related microblogs from seven Chinese megacities, this study analyses the influence mechanisms influencing public participation behavior and reveals the regulatory role of confirmed case numbers. Meanwhile,a qualitative comparative analysis examines and discusses diferent confgurations of ixn fuential factors.
The study reveals that microblog content richness demonstrates a U-shaped impact on public participation behavior. Conversely, content interaction, content length, and the number of fans positively impact participation, while update frequency has a negative impact. Additionally, the number of new confrmed cases positively regulates the impact of microblog content and publisher characteristics on public participation behavior. Public participation behavior also varies based on publishing time and content semantic features. This study further revealed the different confgurations of influential factors by QCA method.
This study reveals the impact mechanism of the microblog content and publisher characteristics on public participation behavior. It also demonstrates the regulatory role of newly confrmed cases in the way content and publishers\' characteristics influence public participation behavior. This study is of great significance for the operation of government microblogs, the release of emergency information, and the promotion of public participation.

Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which % of the infections are ascribed to Nigeria. This disease affects mostly the productive age and hence a proper understanding of the dynamics of this disease will help in formulating policies that would help in curbing the spread of LF. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models in. Data between between 7th January 2018 2018 and 17th December, 2022 on suspected cases, confirmed cases and deaths resulting from LF were retrieved from the Nigeria Centre for Disease Control (NCDC). The data obtained were fitted to quantile regression models (QRM) at 25, 50 and 75% as well as to Machine learning models. The response variable being confirmed cases and mortality due to Lassa fever in Nigeria while the independent variables were total confirmed cases, the week, month and year. Result showed that the highest monthly mean confirmed cases (56) and mortality (9) from LF were reported in February. The first quarter of the year reported the highest cases of both confirmed cases and deaths in Nigeria. Result also revealed that for the confirmed cases, quantile regression at 50% outperformed the best of the MLM, Gaussian-matern5/2 GPR (RMSE=10.3393 vs. 11.615), while for mortality, the medium Gaussian SVM (RMSE=1.6441 vs. 1.8352) outperformed QRM. Quantile regression model at 50% better captured the dynamics of the confirmed cases of LF in Nigeria while the medium Gaussian SVM better captured the mortality of LF in Nigeria. Among the features selected, confirmed cases was found to be the most important feature that drive its mortality with the implication that as the confirmed cases of Lassa fever increases, is a significant increase in its mortality. This therefore necessitates a need for a better intervention measures that will help curb Lassa fever mortality as a result of the increase in the confirmed cases. There is also a need for promotion of good community hygiene which could include; discouraging rodents from entering homes and putting food in rodent proof containers to avoid contamination to help hart the spread of Lassa fever in Nigeria.

UNASSIGNED: Rabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques.
UNASSIGNED: Monthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset.
UNASSIGNED: The findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18-30). In contrast, SARIMA projections averaged 277 cases annually (range: 208-214).
UNASSIGNED: This research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities.

The COVID-19 virus has impacted all facets of our lives. As a global response to this threat, vaccination programmes have been initiated and administered in numerous nations. The question remains, however, as to whether mass vaccination programmes result in a decrease in the number of confirmed COVID-19 cases. In this study, we aim to predict the future number of COVID-19 confirmed cases for the top ten countries with the highest number of vaccinations in the world. A well-known Deep Learning method for time series analysis, namely, the Long Short-Term Memory (LSTM) networks, is applied as the prediction method. Using three evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), we found that the model built by using LSTM networks could give a good prediction of the future number and trend of COVID-19 confirmed cases in the considered countries. Two different scenarios are employed, namely: \'All Time\', which includes all historical data; and \'Before Vaccination\', which excludes data collected after the mass vaccination programme began. The average MAPE scores for the \'All Time\' and \'Before Vaccination\' scenarios are 5.977% and 10.388%, respectively. Overall, the results show that the mass vaccination programme has a positive impact on decreasing and controlling the spread of the COVID-19 disease in those countries, as evidenced by decreasing future trends after the programme was implemented.

This study applies OLS, panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019 (Covid-19) outbreak on the global equity markets during the early stage of the pandemic. We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1% significance level. Furthermore, the pandemic has a more significant impact on the European countries than on the East Asian economies. The results have three main implications. Firstly, policy makers should react fast to mitigate the impact of a crisis. Secondly, investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly. Furthermore, the Covid-19 outbreak results in a shift of power from the west to the east.

Forecasting the COVID-19 confirmed cases, deaths, and recoveries demands time to know the severity of the novel coronavirus. This research aims to predict all types of COVID-19 cases (verified people, deaths, and recoveries) from the deadliest 3rd wave data of the COVID-19 pandemic in Bangladesh. We used the official website of the Directorate General of Health Services as our data source. To identify and predict the upcoming trends of the COVID-19 situation of Bangladesh, we fit the Auto-Regressive Integrated Moving Average (ARIMA) model on the data from Mar. 01, 2021 to Jul. 31, 2021. The finding of the ARIMA model (forecast model) reveals that infected, deaths, and recoveries number will have experienced exponential growth in Bangladesh to October 2021. Our model reports that confirmed cases and deaths will escalate by four times, and the recoveries will improve by five times at a later point in October 2021 if the trend of the three scenarios of COVID-19 from March to July lasts. The prediction of the COVID-19 scenario for the next three months is very frightening in Bangladesh, so the strategic planner and field-level personnel need to search for suitable policies and strategies and adopt these for controlling the mass transmission of the virus.

UNASSIGNED: Over a million confirmed cases of the coronavirus disease (COVID-19) across 16 European countries were observed during the first wave of the pandemic. Epidemiological measures like the case fatality rate (CFR) are generally used to determine the severity of the illness. The aim is to investigate the impact of the age structure of reported cases on the reported CFR and possibilities of its demographic adjustment for a better cross-country comparison (age-standardized CFRs, time delay between cases detection and death).
UNASSIGNED: This longitudinal study uses prospective, population-based data covering 150 days, starting on the day of confirmation of the 100th case in each country. COVerAGE-DB and the Human Mortality Database were used in this regard. The age-standardized CFRs were calculated with and without the time delay of the number of deaths after the confirmation of the cases.
UNASSIGNED: The observed decline in the CFRs at the end of the first wave is partly given by the changes in the age structure of confirmed cases. Using the adjusted (age-standardized) CFRs with time delay, the risk of death among confirmed cases is much more stable in comparison to crude (observed) CFRs.
UNASSIGNED: Preventing the spread of COVID-19 among the elderly is an important way to positively influence the overall fatality rate, decrease the number of deaths, and not overload the health systems. The crude CFRs (still often presented) are not sufficient for a proper evaluation of the development across populations nor as a means of identifying the influencing factors.

UNASSIGNED: Lassa fever is a viral haemorrhagic fever with non-specific symptoms that has shown an upward trend in Nigeria and other West African countries, which is depicted by high incidence and case fatality in recent years. There are different reports on the yearly case burden of Lassa fever from the Federal Ministry of Health in Nigeria, through the regulatory body - Nigeria Centre for Disease Control (NCDC). Being the epicentre of the disease, Lassa fever has been exported from Nigeria to both neighbouring and distant countries.Methods: The aim of this review was to carry out a retrospective analysis from January 2015 to 26 September 2021 of the weekly and yearly outbreak of Lassa fever in Nigeria based on selected publications. The focus was on timely diagnosis, treatment option, public health interventions and progress of clinical trials for vaccine candidates, and to identify proactive measures that can be sustained to curb periodic outbreaks. The review was done using percentages, cross-tabulation and graphical charts.
UNASSIGNED: The predominant age group infected was 21 to 40 years with a male to female ratio of 1:0.8. A total of 3311 laboratory-confirmed Lassa fever cases out of 20,588 suspected cases were identified from 29 states. Edo, Ondo, Taraba, Ebonyi, Bauchi, Plateau and Nasarawa had yearly Lassa fever incidence over the time frame considered. Contact tracing was done on over 33,804 individuals with about 90% completing follow-up. Case fatality rate within the period ranged from 9.3% to 29.2%. There is a sharp decline in the epidemiological trend of Lassa fever in the yearly seasonal peaks from weeks 1 to 13 with about 75% reduction in incidence between 2020 and 2021.
UNASSIGNED: The effective management of Lassa fever needs the implementation of preventive methods, prompt laboratory diagnosis, timely treatment, provision of personal protective equipment, cross-border surveillance, contact tracing, community awareness and vector control in order to minimise spread.

Coronavirus is a new pandemic disease that has emerged in Wuhan, China, and then spreads around the world. The cases number of the COVID-19, which have been daily reported in Iraq, has risen slowly. However, no confirmed study has been undertaken to evaluate the situation of the COVID-19 in concerning the confirmed cases, death cases, and recovered.
The current study is undertaken to describe and assess the COVID-19 of the present situation in Iraq out of the range of the confirmed, deaths and recovered cases from the date 21 February to 30 April 2020 in Iraq.
The study findings have revealed that there is a gradual increase of COVID-19 cases onwards until the top peak in 7th Apr. in which the cases reach 684, then decrease regularly. The total infected people of the study scope is 2085 persons according to the Ministry of Health in Iraq, while the World Health Organization (WHO) states 2003 person. The spatial distribution quantile map showed the hot spots in the province of Babylon, Maysan, and Diyala. However, less was found in three provinces (Nineveh, Salahaddin, and Al Anbar). The result shows that 39% recovered and 3% death cases out of total infected people.
COVID-19 in Iraq comes to be limited via the procedures of Iraqi government. However, the infected people will be increased gradually and many international reports that predict the end of this pandemic in the world will be doubtful as there are many vaccines developed and under development which led to reduce to effect of this pandemic.