Novel application of capture-recapture methods to estimate the completeness of contact tracing during a large outbreak of Ebola Virus Disease, Democratic Republic of Congo, 2018-2020 (preprint)

Despite its critical role in containing outbreaks, the efficacy of contact tracing (CT), measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of CT by applying unilist capture-recapture methods on data from the 2018-2020 outbreak of Ebola virus disease in the Democratic Republic of Congo. We applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved cases with a) any contacts and b) infected contacts, to compute CT sensitivity. Geometric distributions were the best fitting models. Our results indicate that CT efforts identified almost all (n=792, 99%) of the cases with any contacts, but only half (n=207, 48%) of the cases with infected contacts, suggesting that CT efforts performed well at identifying contacts during the listing stage, but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the current COVID-19 pandemic.

Predictors of COVID-19 Epidemics in Countries of the World Health Organisation African Region (preprint)

Background: WHO African Region countries have experienced very different COVID-19 epidemics. This study aimed to identify predictors for the timing of the first COVID-19 case and the per capita mortality rate during the first and second pandemic wave in the region, and to test for any impact of countermeasures.Methods: We performed a region-wide, country-based observational study. Data on COVID-19 cases and deaths for all 47 countries in the WHO African Region were obtained from the WHO COVID-19 Dashboard. A set of predictors classified to nine categories were collected and used as explanatory variables. We applied Cox proportional hazards regression models, generalized linear mixed models and multinomial logistic regression models as appropriate.Findings: Predictors for an earlier first case were a more urban population, high volume of international air travel and more land borders, and better COVID-19 test capacity. Predictors for a high per capita mortality rate during the first wave were a more urban population, more pre-pandemic international air travel and higher prevalence of HIV. The stringency and timing of government restrictions on behaviour were not associated with a lower per capita mortality rate in the first wave. A more urban population and a higher infectious disease resilience score were associated with more stringent restrictions and/or a higher per capita mortality rate in the first wave. The predictor set for the second wave was similar, and first wave per capita mortality predicted that in the second wave. These results were not altered when measures of national testing effort were included in the models.Interpretation: COVID-19 in Africa arrived earlier and caused greater mortality in countries with more international travel and a more urban population. Mortality was exacerbated by high HIV prevalence; it is not clear whether this is a direct or indirect effect. Countries that were better prepared and judged to have more resilient health systems were worst affected, both by the disease and by the imposition of restrictions. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa.Funding Statement: National Institute for Health Research, Darwin Trust of EdinburghDeclaration of Interests: The authors disclose no conflicts of interest.