ABSTRACT
Background: COVID-19 has emerged as a serious pandemic that emerged during since the end of 2019. The dissemination and survival of coronaviruses have been demonstrated to be affected by ambient temperature in epidemiological and laboratory research. The goal of this investigation was to see if temperature plays a role in the infection produced by this novel coronavirus. Methods: Between March 29, 2020, and September 29, 2020, daily confirmed cases and meteoro-logical parameters in many Gulf countries were collected. Using a generalized additive model, we investigated the nonlinear relationship between mean temperature and COVID-19 confirmed cases.. To further investigate the association, we employed a piecewise linear regression. Results: According to the exposure-response curves, the association between mean temperature and COVID-19 cases was nearly linear in the window of 21 - 30C while it is almost flat beyond that window. When the number was below 21C (lag 0-14), each 1C increase was associated with a 4.861 percent (95 percent CI: 3.209 - 6.513) increase in mean temperature (lag 0-14). Our sensitiv-ity analysis confirmed these conclusions. Conclusions: Our findings show a positive linear association between mean temperature and the number of COVID-19 cases with a threshold of 21C. There is little evidence that COVID-19 case numbers would rise as the weather becomes colder, which has important consequences for making health strategy and decision.
ABSTRACT
Since the coronavirus pandemic started, many people have died due to the disease. The epidemic has been challenging to predict, as it progresses and spreads throughout the world. We used Auto-Regressive Integrated Moving Average (ARIMA) models to predict the outbreak of COVID-19 in the upcoming months in Morocco. In this work, we measured the effective reproduction number using the real data and the forecasted data produced by the two commonly used approaches, to reveal how effective the measures taken by the Moroccan government have been in controlling the COVID-19 outbreak. The prediction results for the next few months show a strong evolution in the number of confirmed and death cases in Morocco. We study the spread of COVID-19 in Morocco to see how many cases are discovered, recovered, and dead, and the forecasting of further cases is used as a basic novel method. It is based on time series models. We used coronavirus outbreak data from March 02, 2020, to August 04, 2021. ARIMA (Autoregressive integrated moving average) and Prophet time-series models are used to forecast the development of COVID-19, which is not a novel method. The mean absolute error, root mean square error, and coefficient of determination R2 were computed to assess the model's performance. Our study aims to provide a better understanding of the infectious disease outbreak that affected Morocco. It also provides information on the disease outbreak's epidemiology. Our study shows that the FBProphet model is more accurate in predicting the prevalence of COVID-19. It can help guide the government's efforts to prevent the virus' spread. © 2022 The Author(s).
ABSTRACT
Coronavirus (COVID-19) has continued to be a global threat to public health. When the coronavirus pandemic began early in 2020, experts wondered if there would be waves of cases, a pattern seen in other virus pandemics. The overall pattern so far has been one of increasing cases of COVID-19 followed by a decline, and we observed a second wave of increased cases and yet we are still exploring this pandemic. Hence, updating the prediction model for the new cases of COVID-19 for different waves is essential to monitor the spreading of the virus and control the disease. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for predicting new cases of coronavirus (COVID-19). We propose a deterministic method to predict the basic reproduction number Ro of first and second wave transition of COVID-19 cases in Kuwait and also to forecast the daily new cases and deaths of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holt's method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait. The results show that support vector regression has achieved the best performance for prediction while a simple exponential model without trend gives good optimal results for forecasting of Kuwait COVID-19 data.
ABSTRACT
In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. After smoothing our data set, analysis based on functional principal components method was performed. Then, a clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map. We also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models. © 2022 the Author(s), licensee AIMS Press.
ABSTRACT
This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.
ABSTRACT
Background: The challenge posed by the COVID-19 pandemic may represent an overwhelmingly stressful event for ankylosing spondylitis (SpA) patients and impact their treatment adherence. In response to the COVID-19 pandemic, Tunisia, have adopted community containment to manage the spread of the virus. However, COVID-19 restrictions can alter psychological wellbeing and limit access to treatment for SpA patients. Objectives: This study aimed to evaluate the impact of COVID-19 pandemic on psychological health and treatment adherence on Tunisian SpA patients. Methods: This is a cross sectional study including patients with SpA (ASAS criteria). A survey comprising questions about adherence to stay home warnings;the obligation to go outside for work;satisfaction with the medical support or information received for COVID-19;showing up to medical check-ups, proper use of the medications;medications that the patient stopped taking. Anxious and depressive symptoms were assessed using the Arabic version of Hospital Anxiety and Depression Scale (HADS) questionnaire. Results: We included thirty patients. the average age was: 39,7 years-old and the sex ratio was: 13,3. 75 % of patients were married. The SpA was axial in 25%, peripheral in 20%, and both in 55 %. Most patients had a moderate activity and the mean activity scores were: BASDAI = 2.60, ASDAScrp:2.65 38% of patients were on biologics, 36 % on sulfasalazine and NSAIDs and 26 % on NSAIDs only. It seemed that significant number of patients strictly adhered to stay home warnings (> 89%) only 11% were obliged to go out for work during general lockdown while only 24 % adhered to it after general lockdown. Most of the patient 78 % were not satisfied with the medical support or information about COVID 19. 88% of patients requested information from TV while 10 % requested it from social media and 2 % from relatives and friends working in health care field. After the outbreak, 23% of the patients who had a scheduled chek-up visit attended the appointment as it was before.The remaining either 'did not want to come' (43%), wanted to come but could not contact anyone in the hospital (11%), was advised to postpone their visits (10%), or couldn't find means of transport (13%).A significant number of patients decreased or skipped their dose (69%), while only 13% continued their medications and 16%stopped taking NSAIDs. Biological DMARDs(anti-TNF agents) were the most frequent drugs which patients decreased their dose, skipped or stopped taking 33%. sulfasalazine and NSAIDs were least likely 17% to be skipped or stopped. 43% of patients Had a HADS anxiety level more than or equal to 11: 87 % women and 13 % men. The highest anxiety scores were found among patients aged less than 45 years old (87%) married with children .32% of patients had a HADS depression level more than or equal to 11: 54 % women and 44 % men. the highest depression scores were found among patients aged less than 45 years old married with children. No significant relationship was found between anxiety and depression levels regarding biologic treatment. Conclusion: Our results suggest that patients with SpA were less likely to comply strictly to 'stay home' restrictions, most probably due to the male predominance and relatively younger age. Additionally, we noticed that SpA patients treated with anti-TNF agents were the patients that regular drug use had been considerably disrupted. COVID 19 pandemic has heightened the need to care for patients with SpA in an increasingly virtual environment. Additionally, we found that being female, having a lower level of education, having a child, living in a crowded family is correlated to higher levels of anxiety and depression.