Objectives: We aimed to systematically produce empirical evidence on the WPV against health care workers in Africa through the review of relevant literature.
Method: We sourced for evidence through the following databases: PubMed, Science direct and Scopus from 30th November to 31st December 2019 as well as the reference list of the studies included. A total of 22 peer reviewed articles were included in the review (8065 respondents). Quality appraisal of the included studies was assessed using critical appraisal tools for cross-sectional studies.
Result: Across the studies, diverse but high prevalence of WPV ranging from 9% to 100% was reported with the highest in South Africa (54%-100%) and Egypt (59.7%-86.1%). The common types were verbal, physical, sexual harassment and psychological violence. The correlates of WPV reported were gender, age, shift duty, emergency unit, psychiatric unit, nursing, marital status and others. Various impacts were reported including psychological impacts and desire to quit nursing. Patients and their relatives, the coworkers and supervisors were the mostly reported perpetrators of violence. Doctors were mostly implicated in the sexual violence against nurses. Policy on violence and management strategies were non-existent across the studies.
Conclusion: High prevalence of WPV against healthcare workers exists in Africa but there is still paucity of research on the subject matter. However, urgent measures like policy formulation and others must be taken to address the WPV as to avert the impact on the healthcare system.
METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.