Assessment of the socioeconomic impact of COVID-19 in Rwanda: Findings from a country-wide community survey (preprint)

The COVID-19 pandemic disrupted socioeconomic situation worldwide, and particularly in Rwanda which was rebuilding its economy in the aftermath of the 1994 Genocide against the Tutsi. Recent studies documented the macro-level socio-economic pandemic impact but the impact on a household’s daily life has been scarcely documented especially in low-and-middle income countries. This work reports a country-wide longitudinal community survey and describes the interplay between multiple factors to assess the socio-economic impact of COVID-19 on the Rwandan population at micro-level (household). The survey was conducted in Rwanda between December 2021 and March 2022 and data used comprised a total of 26,412 response forms received from around 4400 participants surveyed in 6 recurrent bi-weekly phases. This study revealed that the income of 57.7% of respondents has decreased and 15.5% of respondents received support to overcome the consequences. The univariate analysis results indicate that the decrease in income is more seen for females than males. The other most affected group is of daily laborer or small business (77.1%), people living in urban area (63.7%), retired people (66.4%), and people with primary school education level (62.0%). The multivariable findings highlighted that vulnerable groups: income-poor households with low socio-economic categories and females living in rural regions are among the most impacted in terms of food security, electricity, water and transport. The findings from this research will be used by policy makers to design and implement preventive and responsive measures for future pandemics that should be multifactorial and tailored to transversal parameters like gender and residence.

Digital mHealth and Virtual Care Use During COVID-19 in 4 Countries: Rapid Landscape Review.

BACKGROUND: As a result of the COVID-19 pandemic, providing health care while maintaining social distancing has resulted in the need to provide care remotely, support quarantined or isolated individuals, monitor infected individuals and their close contacts, as well as disseminate accurate information regarding COVID-19 to the public. This has led to an unprecedented rapid expansion of digital tools to provide digitized virtual care globally, especially mobile phone-facilitated health interventions, called mHealth. To help keep abreast of different mHealth and virtual care technologies being used internationally to facilitate patient care and public health during the COVID-19 pandemic, we carried out a rapid investigation of solutions being deployed and considered in 4 countries. OBJECTIVE: The aim of this paper was to describe mHealth and the digital and contact tracing technologies being used in the health care management of the COVID-19 pandemic among 2 high-income and 2 low-middle income countries. METHODS: We compared virtual care interventions used for COVID-19 management among 2 high-income countries (the United Kingdom and Canada) and 2 low-middle income (Kenya and Rwanda) countries. We focused on interventions used to facilitate patient care and public health. Information regarding specific virtual care technologies was procured from a variety of resources including gray literature, government and health organization websites, and coauthors' personal experiences as implementers of COVID-19 virtual care strategies. Search engine queries were performed to find health information that would be easily accessible to the general public, with keywords including "COVID-19," "contact-tracing," "tool-kit," "telehealth," and "virtual care," in conjunction with corresponding national health authorities. RESULTS: We identified a variety of technologies in Canada, the United Kingdom, Rwanda, and Kenya being used for patient care and public health. These countries are using both video and text message-based platforms to facilitate communication with health care providers (eg, WelTel and Zoom). Nationally developed contact tracing apps are provided free to the public, with most of them using Bluetooth-based technology. We identified that often multiple complimentary technologies are being utilized for different aspects of patient care and public health with the common purpose to disseminate information safely. There was a negligible difference among the types of technologies used in both high-income and low-middle income countries, although the latter implemented virtual care interventions earlier during the pandemic's first wave, which may account for their effective response. CONCLUSIONS: Virtual care and mHealth technologies have evolved rapidly as a tool for health care support for both patient care and public health. It is evident that, on an international level, a variety of mHealth and virtual care interventions, often in combination, are required to be able to address patient care and public health concerns during the COVID-19 pandemic, independent of a country's economic standing.

Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of COVID-19 in Africa (preprint)

Background The COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise <3% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection underascertainment may be equally responsible. Methods We seek to quantify magnitude of underascertainment in COVID-19's cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in African nations since March 2020. Results Multiplicative factors derived from serology data - in a subset of 12 nations - suggested a range of COVID-19 reporting rates, from 1 in 630 infections reported in Kenya (May 2020) to 1 in 15 infections reported in South Africa (November 2021). The largest multiplicative factor, 3,795, corresponded to Malawi (June 2020), suggesting <0.05% of infections captured. A similar set of multiplicative factors for all nations derived from postmortem data points toward the same conclusion: reported COVID-19 cases are unrepresentative of true infections, suggesting a key reason for low case burden in many African nations is significant underdetection and underreporting. Conclusions While estimating COVID-19's exact burden is challenging, the multiplicative factors we present provide incidence curves reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing severe discrepancies between reported cases, projected infections, and deaths.

SARS-CoV-2 genomic surveillance in Rwanda: Introductions and local transmission of the B.1.617.2 (Delta) variant of concern (preprint)

The emergence of the SARS-CoV-2 Delta variant of concern (lineage B.1.617.2) in late 2020 resulted in a new wave of infections in many countries across the world, where it often became the dominant lineage in a relatively short amount of time. We here report on a novel genomic surveillance effort in Rwanda in the time period from June to September 2021, leading to 201 SARS-CoV-2 genomes being generated, the majority of which were identified as the Delta variant of concern. We show that in Rwanda, the Delta variant almost completely replaced the previously dominant A.23.1 and B.1.351 (Beta) lineages in a matter of weeks, and led to a tripling of the total number of COVID-19 infections and COVID-19-related fatalities over the course of only three months. We estimate that Delta in Rwanda had an average growth rate advantage of 0.034 (95% CI 0.025-0.045) per day over A.23.1, and of 0.022 (95% CI 0.012-0.032) over B.1.351. Phylogenetic analysis reveals the presence of at least seven local Delta transmission clusters, with two of these clusters occurring close to the border with the Democratic Republic of the Congo, and another cluster close to the border with Tanzania. A smaller Delta cluster of infections also appeared close to the border with Uganda, illustrating the importance of monitoring cross-border traffic to limit the spread between Rwanda and its neighboring countries. We discuss our findings against a background of increased vaccination efforts in Rwanda, and also discuss a number of breakthrough infections identified during our study. Concluding, our study has added an important collection of data to the available genomes for the Eastern Africa region, with the number of Delta infections close to the border with neighboring countries highlighting the need to further strengthen genomic surveillance in the region to obtain a better understanding of the impact of border crossings on lowering the epidemic curve in Rwanda.

Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Sharing, Accessing and Analyzing SARS-COV-2/COVID-19 Data in Rwanda (LAISDAR Project): Study design and rationale (preprint)

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.

A strategy for finding people infected with SARS-CoV-2: optimizing pooled testing at low prevalence (preprint)

Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals, on an ongoing basis. RT-PCR (reverse transcription polymerase chain reaction) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups. We propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof of concept experiments in which a positive subsample was detected even when diluted a hundred-fold with negative subsamples. Using these methods, the costs of mass testing could be reduced by a large factor. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the cost of regular, mass testing. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2. Field trials of our approach are now under way in Rwanda.