Intimate partner violence (IPV) is a pervasive social challenge with severe health and demographic consequences. Global statistics indicate that more than a third of women have experienced IPV at some point in their lives. In South Africa, IPV is considered a significant contributor to the country’s broader problem with violence and a leading cause of femicide. Consequently, IPV has been the major focus of legislation and research across different disciplines. The present article aims to contribute to the growing scholarly literature by predicting factors that are associated with the risk of experiencing IPV. We used the 2016 South African Demographic and Health Survey dataset and restricted our analysis to 1,816 ever-married women who had complete information on the variables that were used to generate IPV. Prior research has mainly used regression analysis to identify correlates of IPV; however, while regression analysis can test a priori specified effects, it cannot capture unspecified inter-relationship across factors. To address this limitation, we opted for machine learning methods, which identify hidden and complex patterns and relationships in the data. Our results indicate that the fear of the husband is the most critical factor in determining the experience of IPV. In other words, the risk of IPV in South Africa is associated more with the husband or partner’s characteristics than the woman’s. The models developed in this study can be used to develop interventions by different stakeholders such as social workers, policymakers, and or other interested partners.

PurposeThe coronavirus disease 2019 (COVID-19) pandemic altered business and personal activities globally especially stimulating contactless financial transactions. However, despite the similar national lockdowns in cash-based economies, the adoption of contactless transactions through the widely available mechanism, mobile wallets, remained low. This research aimed to identify the factors surrounding this peculiarity.Design/methodology/approachThe study was investigated using a composite model based on the diffusion of innovation theory (DIT), technology acceptance model (TAM) and information systems success model (ISSM). Data were collected from 621 Cameroonian mobile wallet users and analyzed using partial least squares structural equation (PLS-SEM) modeling.FindingsThe key findings revealed that the usage of mobile wallets, in the current form, were not affected by the perceived ease of use and did not match the existing lifestyle of users in Cameroon (no compatibility). The branding of mobile wallets (image) which was based on global messaging did not appeal to Cameroonians; in fact, the branding gave mobile wallets a negative image.Originality/valueThese key findings reveal the dangers of assuming that global strategies which have been effective in dealing with the pandemic will be effective in low-income or cash-based economies. The findings suggest that considering essential contextual dispositions is critical.

Background The association between girl child marriage and education is widely acknowledged; however, there is no large body of demographic studies from Zimbabwe that have addressed this aspect. This study aimed to examine the extent to which child marriage affects one academic milestone, i.e. completion of the Ordinary Level, the first cycle of high school, which is also the most critical indicator of educational achievement in Zimbabwe. Methods We used the 2015 Zimbabwe Demographic and Health Survey and extracted 2380 cases of ever-married women aged between 20–29 years. We applied a propensity score-based method, which allowed us to mimic a hypothetical experiment and estimate outcomes between treated and untreated subjects. Results Our results suggest that child age at first marriage is concentrated between the ages of 15–22, with the typical age at first marriage being 18 years. Both logistic regression and PSM models revealed that early marriage decreased the chances of completing the first cycle of high school. Regression adjustment produced an estimate of prevalence ratio (PR) of 0.446 (95% CI: 0.374–0.532), while PSM resulted in an estimate (PR = 0.381; 95% CI: 0.298–0.488). Conclusion These results have implications for Zimbabwe’s development policy and suggest that girl-child marriage is a significant barrier to educational attainment. If not addressed, the country will most likely fail to meet sustainable development Goal 4.2 and 5.3. Social change interventions that target adults and counter beliefs about adolescent sexuality and prepubescent marriage should be put in place. Moreover, interventions that keep teenage girls in school beyond the first cycle of high school should be prioritised.

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Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.