Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia

Abstract

The rapid spread of COVID-19 in Ethiopia was attributed to joint effects of multiple factors such as low adherence to face mask-wearing, failure to comply with social distancing measures, many people attending religious worship activities and holiday events, extensive protests, country election rallies during the pandemic, and the war between the federal government and Tigray Region. This study built a system dynamics model to capture COVID-19 characteristics, major social events, stringencies of containment measures, and vaccination dynamics. This system dynamics model served as a framework for understanding the issues and gaps in the containment measures against COVID-19 in the past period (16 scenarios) and the spread dynamics of the infectious disease over the next year under a combination of different interventions (264 scenarios). In the counterfactual analysis, we found that keeping high mask-wearing adherence since the outbreak of COVID-19 in Ethiopia could have significantly reduced the infection under the condition of low vaccination level or unavailability of the vaccine supply. Reducing or canceling major social events could achieve a better outcome than imposing constraints on people’s routine life activities. The trend analysis found that increasing mask-wearing adherence and enforcing more stringent social distancing were two major measures that can significantly reduce possible infections. Higher mask-wearing adherence had more significant impacts than enforcing social distancing measures in our settings. As the vaccination rate increases, reduced efficacy could cause more infections than shortened immunological periods. Offsetting effects of multiple interventions (strengthening one or more interventions while loosening others) could be applied when the levels or stringencies of one or more interventions need to be adjusted for catering to particular needs (e.g., less stringent social distancing measures to reboot the economy or cushion insufficient resources in some areas).

COVID‐19 vaccination strategies in Africa: A scoping review of the use of mathematical models to inform policy

“…Two studies calibrated their transmission rates to vary over time to account for public health measures without directly modelling specific NPIs due to inconsistencies in implementation and compliance across countries [20, 43]. The NPIs explored in these studies were mainly social distancing, face masks, quarantine, travel restrictions, isolation and testing [21, 25, 34, 39, 43, 45]. For example, Diarra et al studied the dates of relaxing NPIs and concluded the best time to relax NPIs is a few days after the last peak of the pandemic when 20% of the population was vaccinated [36].…”

“…We found that 50% ( n = 16) of the studies had policy intent. We identified four major themes of how the authors hoped their findings could be used to inform policy (Table 2): (i) prioritising or selecting interventions ( n = 5) [18, 22, 30, 37, 45], (ii) pandemic planning and response ( n = 5) [19, 23, 30, 36, 43], (iii) vaccine distribution and optimisation strategies ( n = 5) [20, 29, 32, 36, 40] and (iv) understanding transmission dynamics of COVID‐19 ( n = 4) [18, 24, 35, 49].…”

“…Two studies calibrated their transmission rates to vary over time to account for public health measures without directly modelling specific NPIs due to inconsistencies in implementation and compliance across countries [20,43]. The NPIs explored in these studies were mainly social distancing, face masks, quarantine, travel restrictions, isolation and testing [21,25,34,39,43,45].…”

“…Okyere [42], Wamalwa [19], Kinyili [39], Amouch [28], Agossou [33], Dashtbali [21], Gozzi [29], Edholm [43] Compare vaccination and NPIs 9 (28%) Diarra [36], Dembek [20], Zhu [45], Oloniiju [34], Kinyili [39], Boutayeb [25], El Hadj Moussa [47], Dashtbali [21],…”

“…Jemal [18], Diagne [30], Zhu [45], Benahmdi [37], Eroglu [22] Pandemic planning and response 5 (16%) Cabore [23], Diarra [36], Wamalwa [19], Edholm [43], Diagne [30] Vaccine distribution and optimisation strategies 5 (16%) Reddy [32], Rasambainarivo [40], Dembek [20], Gozzi [29], Diarra [36] Understanding transmission dynamics 4 (13%) Suthar [35], Xue [49], Mukandavire [24], Jemal [18] Equity/heterogeneity factors a Age 8 (25%) Cabore [23], Rasambainarivo [40], Oshinubi [38], Dashtbali [21], Diarra [36], Reddy [32], Gozzi [29], Matrajt [46] Comorbidities 4 (13%) Cabore [23], Jemal [18], Okyere [42], Dashtbali [21] Location (rural vs. urban) 2 (6%) Boutayeb [25], Cabore [23] a Some studies had more than one theme for the key vaccination questions, policy intent and equity/heterogeneity factors.…”