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Background: The first case of COVID-19 in sub-Saharan Africa (SSA) was reported by Nigeria on February 27, 2020. While case counts in the entire region remain considerably less than those being reported by individual countries in Europe, Asia, and the Americas, SSA countries remain vulnerable to significant COVID morbidity and mortality due to systemic healthcare weaknesses, less financial resources and infrastructure to address the new crisis, and untreated comorbidities. Variation in preparedness and response capacity as well as in data availability has raised concerns about undetected transmission events. Methods: Confirmed cases reported by SSA countries were line-listed to capture epidemiological details related to early transmission events into and within countries. Data were retrieved from publicly available sources, including institutional websites, situation reports, press releases, and social media accounts, with supplementary details obtained from news articles. A data availability score was calculated for each imported case in terms of how many indicators (sex, age, travel history, date of arrival in country, reporting date of confirmation, and how detected) could be identified. We assessed the relationship between time to first importation and overall Global Health Security Index (GHSI) using Cox regression. K-means clustering grouped countries according to healthcare capacity and health and demographic risk factors. Results: A total of 2417 confirmed cases of COVID-19 were reported by 40 countries in sub-Saharan Africa during the 30 days after the first known introduction to the region. Out of the 876 cases for which information was publicly available, 677 (77.3%) were considered importation events. At the regional level, imported cases tended to be male (67.3%), were a median 43.0 years old (Range: 6 weeks - 88 years), and most frequently had recent travel history from Europe (43.3%). The median time to reporting an introduction was 19 days; a countrys time to report its first importation was not related to GHSI. Mean data availability scores were lowest for countries that had, on average, the highest case fatality rates, lowest healthcare capacity, and highest probability of premature death due to non-communicable diseases. Conclusion: Countries with systemic, demographic, and pre-existing health vulnerabilities to severe COVID-related morbidity and mortality are less likely to report any cases or are reporting with limited public availability of information. Reporting of information on COVID detection and response efforts, as well as on trends in non-COVID illness and care-seeking behavior, is critical to assessing direct and indirect consequences and capacity needs in resource-constrained settings. Such assessments aid in the ability to make data-driven decisions about interventions, country priorities, and risk assessment.

Typhoid conjugate vaccine (TCV) has been shown to be costeffective in some settings, but to make the most of limited healthcare funds, the WHO recommends that decisions about when and where to introduce TCV should be based on local evidence of transmission. Further, Gavi, The Vaccine Alliance, recommends that countries request TCV funding based on epidemiological data from within-country. Since few countries have reliable case reporting, we considered environmental surveillance as a decision-support tool for vaccine introduction choices. Environmental surveillance methods are still in development and this analysis assesses its potential as a decision support tool. We combined a disease transmission simulation model with an economic model, to quantify the value of surveillance. The disease transmission model is fit to historical case reporting from Blantyre, Malawi. The economic model takes a social perspective and incorporates the cost of environmental surveillance, vaccination, and both direct and indirect cost of care for acute typhoid cases. We find that the use of time-limited environmental\ud surveillance to differentiate low- from high-endemicity areas is low cost relative to potential savings, especially if it is paired with a locally targeted vaccination campaign. These findings are robust to the uncertainty of cost model parameters and underlying endemicity.

Additional file 1:. Supplementary Figures for “SARS-CoV-2 infection risk during delivery of childhood vaccination campaigns: a modelling study.”

Additional file 1:. Supplementary Figures for “SARS-CoV-2 infection risk during delivery of childhood vaccination campaigns: a modelling study.”