Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Mixed-methods Feasibility Trial of an Adaptive Electronic Learning Curriculum for Tanzanian Pediatric Health care Workers in the Time of COVID-19

Background: Millions of children in low- and middle-income countries (LMICs) die each year from preventable illness. Evidence-based guidelines (EBGs) from the World Health Organization reduce this amenable burden of disease, but utilization among healthcare workers is variable. Existing inperson training strategies like Emergency Triage Assessment and Treatment (ETAT) improve provider knowledge, adherence to EBG, and patient outcomes but are limited by labor intensity and implementation costs. Leveraging increasing mobile internet access in LMICs could speed dissemination of EBG to medical providers in a way that overcomes the limitations of in-person training. Adaptive electronic learning (AEL), which uses digital algorithms to deliver custom activities to individual learners, is shown to outperform traditional training among healthcare workers in high-income countries but is yet to be evaluated in LMICs. We propose to address the existing gap in LMIC healthcare worker training through a mixed-methods feasibility trial of an AEL curriculum designed to deliver EBG training to medical providers in Tanzania. Methods: Curriculum development: We sought to create a multi-module AEL course addressing context-specific gaps in healthcare worker training. A review of leading regional causes of pediatric mortality was performed to identify priority content areas. Source material was. selected to reflect EBG use at our study site. Training modules were created by pediatricians with expertise in both AEL and EBG. Module approval occurred through an iterative process of review by local stakeholders and international EBG experts. Mixed-methods feasibility trial: We are undertaking a parallelgroup, double-blinded randomized trial to evaluate our AEL curriculum (Figure 1). 30 medical interns will be randomized to either an adaptive or a non-adaptive electronic learning curriculum. The primary outcome is knowledge acquisition, defined by standard mean difference in pre- and postknowledge assessments scores between groups. Qualitative evaluation of the implementation process will be based on normalization process theory. All aspects of recruitment, quantitative, and qualitative data collection will be done remotely in accordance with local social distancing standards and international travel restrictions. Results: Curriculum publication: Our process of content identification, topic selection, and module development yielded an 11-module AEL curriculum. Priority content areas include the triage of acutely-ill children as well as the assessment, diagnosis, and management of pediatric pneumonia and hypovolemic shock based on current World Health Organization and Tanzanian guidelines (Figure 2). Mixed-methods feasibility trial: At present, we have enrolled 17 medical interns. Pre-knowledge assessment scores range from 6-60%. One intern has completed the curriculum to date and experienced a 30% increase in knowledge. Conclusion: We expect to complete this feasibility trial by August of 2021. Findings will inform the design of a large-scale implementation trial that will support the development of innovative solutions and low-cost implementation strategies for improving the care of seriously-ill children worldwide.

Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels.

As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population-e.g., they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis.