ARIMA model in predicting of COVID-19 epidemic for the Southern Africa region

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box­Ljung test. The ARIMA (11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population.

Detection of muscle fatigue, from statistical methods to software applications

Background: Muscle fatigue has become increasingly present in our daily lives. This is related to lifestyle difficulties. Several methods were proposed in order to detect the muscle fatigue. This report proposes a short review of statistical methods and processing tools extracted from MATLAB software dedicated to detection of fatigue. Methods: The first part in this study is an application of a useful electronic card named "arduino" for acquiring the electromyography signal (EMG). This latter is the perfect signal to describe fatigue in muscles. The acquisition of data is done in two steps; the first is a simple acquisition representing rest (the subject is relaxed). Then, in the next step, the subject does a series of physical exercises representing moving continuously a handlebar (in order to simulate the work of the tram's conductor). After obtaining raw data from the acquired signals, we apply statistical methods and some processing tools in order to detect fatigue.Results: For the statistical method, we apply the spectral density, which is a mathematical tool that represents the various spectral components of a signal and to perform the harmonic analysis. We deduct that 80 microvolt's is the intensity of getting fatigue (exercise of moving the handlebar). Using processing tools(FFT and STFT techniques), we obtain essential information on the fatigue's beginning time.Conclusion: A brief survey of statistical and processing tools to extract fatigue information from an EMG signal was done. A typical example of the importance of detecting fatigue was also illustrated (tram conductor). We aimed for a lot of results from this study, especially because we want to compare techniques. After studying, the STFT technique seem the best

Comparison of statistical models used for assessing factors associated with infant mortality in Nigeria

Background: Infant mortality is a public health concern especially in developing countries, particularly Nigeria. Different models had been used independently to identify factors associated with infant mortality. Some of the used models sometimes violate the underlying assumption for the models. This study was designed to compare the models that have been previously used and identify the appropriate model using standard model selection criteria to analyse risk factors for infant mortality in Nigeria.Methods: The study utilised 2008 Nigeria Demographic and Health Survey (NDHS) data with a sample size of 7107. The NDHS was a stratified two-stage cluster design where a questionnaire was used to collect data on the birth history of women aged 15-49 years. The models employed for this study were: Logit, Probit and Clog-log. The model selection criteria were Akaike Information Criterion (AIC), Residual Deviance and Vuong test. The model with the smallest criteria was considered to be the best fit.Results: The results showed that Infant Mortality in Nigeria can be appropriately modelled by Clog-log model. The models and corresponding AIC values were: Logit (6171.1), Probit (6212.6) and Clog-log (6126.6). The residual deviance included: Logit (6135.1), Probit (6176.6) and Clog-log (6090.6). Clog-log had the smallest AIC and residual deviance values; hence, it was of the best fit. Home delivery and delivery by professionals had negative significant associations with infant mortality while women's education (primary/no education) and birth order had positive significant association, (p < 0.05). Conclusion: The best model for infant mortality evaluation in Nigeria was Clog-log. Generally, improved women's education would significantly reduce Infant Mortality in Nigeria

Statistical modelling of social risk factors for sexually transmitted diseases among female youths in Nigeria

INTRODUCTION: Sexually Transmitted Diseases (STDs) are avertable, but the social risks factors connected to these infections are often unnoticed by many, particularly female youths. Previous studies on STDs among youths in Nigeria only focused on its risk factors but failed to model these risk factors as evidenced in this study.METHODOLOGY:The study is retrospective cross-sectional in design which utilized Nigeria Demographic and Health Survey, 2008. It focused on female youths aged 15-24 (n=8093) who ever had sexual intercourse (vaginal, oral and anal). Data analysis was done using Chi-square and logistic regression models. The logistic regression on the data was performed at two stages. These stages generated three and eight different models respectively.RESULTS:Data analyses revealed that the mean age of the respondents was 20.2 ± 2.5. Female youths who were aged between 20-24 years contacted STDs in the last 12 months (2.5%) than those between the ages of 15-19 months (1.4%). A year prevalence of STDs among female youths in Nigeria was 2.1%. Socio-demographic factors such as age, educational status, wealth index, marital status, toilet shared, place of residence, contraceptive use and total life-time number of sexual partners were found to be associated significant risk factors for contacting STDs (P<0.05).CONCLUSIONS:The data confirmed the considerable impact of wealth index and contraceptive use as important predictors of STDs acquisition. Constant use of condoms, abstinence and having one uninfected sexual partner can help reduce the risk of STDs transmission

The present and future course of the AIDS epidemic in Côte d'Ivoire

An assessment of the current and future mortality and morbidity from acquired immunodeficiency syndrome (AIDS) in Côte d'Ivoire was made using the results of the 1989 national survey of the prevalence of human immunodeficiency (HIV) infection in the country and the AIDS projection model developed by WHO. For 1989 it was estimated that about 25,000 AIDS cases in adults and children had occurred, although the total number of cases reported for 1989 (up to 1 July 1991) was about 13% (1:6.9) of this estimated total. It is projected that by 1994 in Côte d'Ivoire the cumulative number of cases of AIDS in adults will be 89,000, and that for infants and children the corresponding number will be 41,000. It was also projected that about 371,000 uninfected children will have been born to HIV-infected mothers in Côte d'Ivoire by 1994 and that many of these children will have been orphaned by the deaths of their mothers from AIDS