The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Estimation of the lifetime distribution from fluctuations in Bellman-Harris processes

The growth of a population is often modeled as branching process where each individual at the end of its life is replaced by a certain number of offspring. An example of these branching models is the Bellman-Harris process, where the lifetime of individuals is assumed to be independent and identically distributed. Here, we are interested in the estimation of the parameters of the Bellman-Harris model, motivated by the estimation of cell division time. Lifetimes are distributed according a Gamma distribution and we follow a population that starts from a small number of individuals by performing time-resolved measurements of the population size. The exponential growth of the population size at the beginning offers an easy estimation of the mean of the lifetime. Going farther and describing lifetime variability is a challenging task however, due to the complexity of the fluctuations of non-Markovian branching processes. Using fine and recent results on these fluctuations, we describe two time-asymptotic regimes and explain how to estimate the parameters. Then, we both consider simulations and biological data to validate and discuss our method. The results described here provide a method to determine single-cell parameters from time-resolved measurements of populations without the need to track each individual or to know the details of the initial condition

Estimation of the lifetime distribution from fluctuations in Bellman-Harris processes

The growth of a population is often modeled as branching process where each individual at the end of its life is replaced by a certain number of offspring. An example of these branching models is the Bellman-Harris process, where the lifetime of individuals is assumed to be independent and identically distributed. Here, we are interested in the estimation of the parameters of the Bellman-Harris model, motivated by the estimation of cell division time. Lifetimes are distributed according a Gamma distribution and we follow a population that starts from a small number of individuals by performing time-resolved measurements of the population size. The exponential growth of the population size at the beginning offers an easy estimation of the mean of the lifetime. Going farther and describing lifetime variability is a challenging task however, due to the complexity of the fluctuations of non-Markovian branching processes. Using fine and recent results on these fluctuations, we describe two time-asymptotic regimes and explain how to estimate the parameters. Then, we both consider simulations and biological data to validate and discuss our method. The results described here provide a method to determine single-cell parameters from time-resolved measurements of populations without the need to track each individual or to know the details of the initial condition