Identifying factors associated with the issuance of coronavirus-related stay-at-home orders in the Middle East and North Africa Region

COVID-19 risk perception and public compliance with preventive measures: Evidence from a multi-wave household survey in the MENA region

This study investigates the association between individuals' concern about contracting COVID-19 and their compliance with recommended preventive and mitigation measures, namely wearing face masks, maintaining social distancing and handwashing, in the context of the Middle East and North Africa (MENA) region. The empirical analysis is based on a panel dataset from the Combined COVID-19 MENA Monitor Household Survey, which was carried out in Jordan, Morocco, Sudan, Tunisia and Egypt. Applying a probit estimation technique, a positive and statistically significant association was found between the level of COVID-19 worries and individuals' compliance with the mitigation measures. Notably, the results revealed that this association followed a "first-up-then-down" trend, showing that compliance with the three mitigation measures rose as individuals' worries about contracting the virus increased, and then markedly decreased after they had been infected. Socio-demographic characteristics contributing to lower levels of compliance included being male, being over 60, having lower levels of education and having a lower household income. A cross-country analysis revealed remarkable differences between the five countries, with the strongest association between COVID-19 concerns and adherence to mitigation measures observed in Tunisia and Sudan, and the weakest association seen in Jordan and Morocco. Policy implications are outlined for effective risk communication and management during disease outbreaks and public health emergencies to encourage appropriate public health behaviours.

Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research

During the COVID-19 pandemic, many countries have issued stay-at-home orders (SAHOs) to reduce viral transmission. Because of their social and economic consequences, SAHOs are a politically risky decision for governments. Researchers typically attribute public health policymaking to five theoretically significant factors: political, scientific, social, economic, and external. However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights. This research employs machine learning to shift the focus from theory to data to generate hypotheses and insights "born from the data" and unconstrained by current knowledge. Beneficially, this approach can also confirm the extant theory. We apply machine learning in the form of a random forest classifier to a novel and multiple-domain data set of 88 variables to identify the most significant predictors of the issuance of a COVID-19-related SAHO in African countries (n = 54). Our data set includes a wide range of variables from sources such as the World Health Organization that cover the five principal theoretical factors and previously ignored domains. Generated using 1000 simulations, our model identifies a combination of theoretically significant and novel variables as the most important to the issuance of a SAHO and has a predictive accuracy using 10 variables of 78%, which represents a 56% increase in accuracy compared to simply predicting the modal outcome.

Identifying factors related to school closures due to COVID-19 in the Middle East and North Africa region

The COVID-19 pandemic has had devastating effects on the Middle East and North Africa (MENA) region, and MENA states have taken dramatic steps in response. This study focuses on school closures, an intervention that all MENA states adopted, some much earlier than others. It seeks to identify policy factors related to MENA governments' decisions to close schools during the first wave of the pandemic. Results suggest external issues regarding temporal and geographic diffusion played the largest role. They also indicate that factors related to disease risk, the economy, political institutions, and women's position in society mattered as well, all of which suggest the decisions were complex.

Prioritizing public health? Factors affecting the issuance of stay-at-home orders in response to COVID-19 in Africa

COVID-19 has sickened and killed millions of people globally. Conventional non-pharmaceutical interventions, particularly stay-at-home orders (SAHOs), though effective for limiting the spread of disease have significantly disrupted social and economic systems. The effects also have been dramatic in Africa, where many states are already vulnerable due to their developmental status. This study is designed to test hypotheses derived from the public health policymaking literature regarding the roles played by medical and political factors as well as social, economic, and external factors in African countries' issuance of SAHOs in response to the early stages of the COVID-19 pandemic. Using event history analysis, this study analyzed these five common factors related to public health policy to determine their impact on African states' varying decisions regarding the issuance of SAHOs. The results of this analysis suggest that medical factors significantly influenced decisions as did factors external to the states, while the role of political factors was limited. Social and economic factors played no discernible role. Overall, this study suggests how African leaders prioritized competing factors in the early stages of a public health crisis.