Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

This study seeks to determine an appropriate statistical technique for forecasting the cumulated confirm cases of Coronavirus in Ghana. Cumulated daily data spanning from March 12, 2020, to August 04, 2020, was retrieved from the Center for Systems Science and Engineering at Johns Hopkins University. Four statistical forecasting techniques: Autoregressive Integrated Moving Average, Artificial Neural Network, Exponential smoothing and Autoregressive Fractional Integrated Moving Average were fitted to the COVID-19 series. Their respective forecast accuracy measures were compared to select the appropriate technique for forecasting the COVID-19 cases. Our findings revealed that the ARFIMA technique was a suitable statistical model for predicting COVID-19 cases in Ghana. The "best" model for forecasting is ARFIMA (2, 0.49, 4) which passed all the needed diagnostic tests. An unequal weight was estimated to derive a combined model for all four forecasting techniques. A 149-cumulated daily forecast from the "best" model and the combined model revealed that the number of confirmed COVID-19 cases would increase slightly until the end of this year

The burden of drug resistance tuberculosis in Ghana; results of the First National Survey.

Resistance to Tuberculosis drugs has become a major threat to the control of tuberculosis (TB) globally. We conducted the first nation-wide drug resistance survey to investigate the level and pattern of resistance to first-line TB drugs among newly and previously treated sputum smear-positive TB cases. We also evaluated associations between potential risk factors and TB drug resistance. Using the World Health Organization (WHO) guidelines on conducting national TB surveys, we selected study participants from 33 health facilities from across the country, grouped into 29 clusters, and included them into the survey. Between April 2016 and June 2017, a total of 927 patients (859 new and 68 previously treated) were enrolled in the survey. Mycobacterium tuberculosis complex (MTBC) isolates were successfully cultured from 598 (65.5%) patient samples and underwent DST, 550 from newly diagnosed and 48 from previously treated patients. The proportion of patients who showed resistance to any of the TB drugs tested was 25.2% (95% CI; 21.8-28.9). The most frequent resistance was to Streptomycin (STR) (12.3%), followed by Isoniazid (INH) (10.4%), with Rifampicin (RIF), showing the least resistance of 2.4%. Resistance to Isoniazid and Rifampicin (multi-drug resistance) was found in 19 (3.2%; 95% CI: 1.9-4.9) isolates. Prevalence of multidrug resistance was 7 (1.3%; 95% CI: 0.5-2.6) among newly diagnosed and 12 (25.0%; 95% CI: 13.6-39.6) among previously treated patients. At both univariate and multivariate analysis, MDR-TB was positively associated with previous history of TB treatment (OR = 5.09, 95% CI: 1.75-14.75, p = 0.003); (OR = 5.41, 95% CI: 1.69-17.30, p = 0.004). The higher levels of MDR-TB and overall resistance to any TB drug among previously treated patients raises concerns about adherence to treatment. This calls for strengthening existing TB programme measures to ensure a system for adequately testing and monitoring TB drug resistance

Comparison of outlier detection techniques in non-stationary time series data

This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data

Determinants of a mobile phone-based Interactive Voice Response (mIVR) system for monitoring childhood illnesses in a rural district of Ghana: Empirical evidence from the UTAUT model.

BackgroundThe use of a mobile phone-based Interactive Voice Response (mIVR) System for real time monitoring of childhood illnesses provides an opportunity to improve childhood survival and health systems. However, little is known about the factors that facilitate its use. This study sought to identify key determinants and moderators of mIVR system use among caregivers in a rural district of Ghana using the Unified Theory of Acceptance and Use of Technology (UTAUT) model.MethodsThe mIVR system was designed to provide real-time data on common symptoms of childhood illnesses after answering several questions by caregivers with sick children. A structured questionnaire with closed questions was used to collect data from 354 caregivers of children under-five living in rural communities, four (4) months after introducing the system. Regression analysis was used to identify key determinants and moderating factors that facilitate the use of the system based on the UTAUT model.ResultsA total of 101 (28.5%) caregivers had used the system and 328 (92.7%) had intention to use the mIVR system. Caregivers' level of education and household wealth were associated with use of the mIVR systems (pConclusionThe perceived usefulness of the mIVR system, ease of use, social influences, and facilitating conditions are key determinants of users' attitude and use of mIVR system. These relationships are significantly moderated by users' phone experience and wealth status

Profile and Predictors of Adolescent Tobacco use in Ghana: Evidence from the 2017 Global Youth Tobacco Survey (GYTS). : Profile and Predictors of Adolescent Tobacco use in Ghana

Background: Understanding of predictive factors for tobacco use initiation among adolescents is critical for effective intervention and prevention. In this regard, we aimed to determine the profile, examine associated factors, and show the regional disparities in the use of tobacco products among the youth in Ghana. Method: The study used the 2017 Ghana Global Youth Tobacco Survey (GYTS) to obtain tobacco-related information among adolescents in  Junior High Schools across the country. The study used a 2-stage cluster randomized sampling technique to obtain nationally representative data. Weighted univariate and multivariate logistic regression analysis was used to assess the association of participant’s characteristics and use of tobacco.   Results: Of 6039 targeted respondents, 5664 (93.8%) participated, - 2707 males, 2929 females, and 28 missing with gender. From the unadjusted analysis, age (p=0.001), pocket money (p<0.001), and exposure to SHS at home (p<0.001) were significantly associated with tobacco use.  In the adjusted analysis, age (p=0.002), pocket money (p<0.001), exposure to SHS at home (p<0.001), and being taught about the dangers of tobacco use (p=0.043) were significantly associated with tobacco use. The regional disparities in the use of any tobacco product were 28.3%, 7.0%, and 4.8% in the Savanna/northern zone, middle/forest zone, and Coastal zone respectively. Conclusion: Multiple factors including age, pocket money, exposure to secondhand smoke (SHS) are identified to be associated with tobacco use among the youth in Ghana. Promoting anti-smoking campaigns in early adolescence, as well as programs targeting early tobacco use can guard the youth against initiating tobacco use

Cohort profile: Research on Obesity and Diabetes among African Migrants in Europe and Africa Prospective (RODAM-Pros) cohort study

PURPOSE: The Research on Obesity and Diabetes among African Migrants (RODAM) prospective (RODAM-Pros) cohort study was established to identify key changes in environmental exposures and epigenetic modifications driving the high burden of cardiovascular disease (CVD) risk among sub-Saharan African migrants. PARTICIPANTS: All the participants in the RODAM cross-sectional study that completed the baseline assessment (n=5114) were eligible for the follow-up of which 2165 participants (n=638 from rural-Ghana, n=608 from urban-Ghana, and n=919 Ghanaian migrants in Amsterdam, the Netherlands) were included in the RODAM-Pros cohort study. Additionally, we included a subsample of European-Dutch (n=2098) to enable a comparison to be made between Ghanaian migrants living in the Netherlands and the European-Dutch host population. FINDINGS TO DATE: Follow-up data have been collected on demographics, socioeconomic status, medical history, psychosocial environment, lifestyle factors, nutrition, anthropometrics, blood pressure, fasting blood, urine and stool samples. Biochemical analyses included glucose metabolism, lipid profile, electrolytes and renal function, liver metabolism and inflammation. In a subsample, we assessed DNA methylation patterns using Infinium 850K DNA Methylation BeadChip. Baseline results indicated that migrants have higher prevalence of CVD risk factors than non-migrants. Epigenome-wide association studies suggest important differences in DNA methylation between migrants and non-migrants. The follow-up study will shed further light on key-specific environmental exposures and epigenetic modifications contributing to the high burden of CVD risk among sub-Saharan African migrants. FUTURE PLANS: Follow-up is planned at 5-year intervals, baseline completed in 2015 and first follow-up completed in 2021

Epidemiological profile of SARS-CoV-2 among selected regions in Ghana: A cross-sectional retrospective study.

BackgroundGlobal cases of COVID-19 continue to rise, causing havoc to several economies. So far, Ghana has recorded 48,643 confirmed cases with 320 associated deaths. Although summaries of data are usually provided by the Ministry of Health, detailed epidemiological profile of cases are limited. This study sought to describe the socio-demographic features, pattern of COVID-19 spread and the viral load dynamics among subjects residing in northern, middle and part of the southern belt of Ghana.MethodsThis was a cross-sectional retrospective study that reviewed records of samples collected from February to July, 2020. Respiratory specimens such as sputum, deep-cough saliva and nasopharyngeal swabs were collected from suspected COVID-19 subjects in 12 regions of Ghana for laboratory analysis and confirmation by real-time reverse transcription polymerase chain reaction (RT-PCR).ResultsA total of 72,434 samples were collected during the review period, with majority of the sampled individuals being females (37,464; 51.9%). The prevalence of SARS-CoV-2 identified in the study population was 13.2% [95%CI: 12.9, 13.4). Males were mostly infected (4,897; 51.5%) compared to females. Individuals between the ages 21-30 years recorded the highest number of infections (3,144, 33.4%). Symptomatic subjects had higher viral loads (1479.7 copies/μl; IQR = 40.6-178919) than asymptomatic subjects (49.9; IQR = 5.5-3641.6). There was significant association between gender or age and infection with SARS-CoV-2 (pConclusionThis study has described the epidemiological profile of COVID-19 cases in northern, middle and part of the southern belt of Ghana, with males and younger individuals at greater risk of contracting the disease. Health professionals should be conscious of individuals presenting with anosmia since this was seen as the strongest predictor of virus infection