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

Spatial and spatio-temporal models and application

Ce travail porte sur les séries spatiales. On étudie les phénomènes dont l’observation est un processus aléatoire indexé par un ensemble spatial. Dans cette thèse on s’intéresse aux données bidimensionnelles régulièrement dispersées dans l’espace, on travaille alors dans un rectangle régulier (sur Z2) . Cette modélisation vise donc à construire des représentations des systèmes suivant leurs dimensions spatiales et à ses applications dans de nombreux domaines tels que la météorologie, l’océanographie, l’agronomie, la géologie, l’épidémiologie, ou encore l’économétrie etc. La modélisation spatiale permet d’aborder la question importante de la prédiction de la valeur d’un champ aléatoire en un endroit donné d’une région. On suppose que la valeur à prédire dépend des observations dans les régions voisines. Ceci montre la nécessité de tenir compte, en plus de leurs caractéristiques statistiques, des relations de dépendance spatiale entre localisations voisines, pour rendre compte de l’ensemble des structures inhérentes aux données. Dans la plupart des champs d’applications, on est souvent confronté du fait que l’une des sources majeures de fluctuations est la saisonnalité. Dans nos travaux on s’intéresse particulièrement à ce phénomène de saisonnalité dans les données spatiales. Faire une modélisation mathématique en tenant en compte l’interaction spatiale des différents points ou localités d’une zone entière serait un apport considérable. En effet un traitement statistique qui prendrait en compte cet aspect et l’intègre de façon adéquat peut corriger une perte d’information, des erreurs de prédictions, des estimations non convergentes et non efficaces.This thesis focuses on the time series in addition to being observed over time, also have a spatial component. By definition, a spatiotemporal phenomenon is a phenomenon which involves a change in space and time. The spatiotemporal model-ling therefore aims to construct representations of systems taking into account their spatial and temporal dimensions. It has applications in many fields such as meteorology, oceanography, agronomy, geology, epidemiology, image processing or econometrics etc. It allows them to address the important issue of predicting the value of a random field at a given location in a region. Assume that the value depends predict observations in neighbouring regions. This shows the need to consider, in addition to their statistical characteristics, relations of spatial dependence between neighbouring locations, to account for all the inherent data structures. In the exploration of spatiotemporal data, refinement of time series models is to explicitly incorporate the systematic dependencies between observations for a given region, as well as dependencies of a region with neighboring regions. In this context, the class of spatial models called spatiotemporal auto-regressive models (Space-Time Autoregressive models) or STAR was introduced in the early 1970s. It will then be generalized as GSTAR model (Generalized Space-Time Autoregressive models). In most fields of applications, one is often confronted by the fact that one of the major sources of fluctuations is seasonality. In our work we are particularly interested in the phenomenon of seasonality in spatiotemporal data. We develop a new class of models and investigates the properties and estimation methods. Make a mathematical model taking into account the spatial inter-action of different points or locations of an entire area would be a significant contribution. Indeed, a statistical treatment that takes into account this aspect and integrates appropriate way can correct a loss of information, errors in predictions, non-convergent and inefficient estimates

Étude de modèles spatiaux et spatio-temporels

This thesis focuses on the time series in addition to being observed over time, also have a spatial component. By definition, a spatiotemporal phenomenon is a phenomenon which involves a change in space and time. The spatiotemporal model-ling therefore aims to construct representations of systems taking into account their spatial and temporal dimensions. It has applications in many fields such as meteorology, oceanography, agronomy, geology, epidemiology, image processing or econometrics etc. It allows them to address the important issue of predicting the value of a random field at a given location in a region. Assume that the value depends predict observations in neighbouring regions. This shows the need to consider, in addition to their statistical characteristics, relations of spatial dependence between neighbouring locations, to account for all the inherent data structures. In the exploration of spatiotemporal data, refinement of time series models is to explicitly incorporate the systematic dependencies between observations for a given region, as well as dependencies of a region with neighboring regions. In this context, the class of spatial models called spatiotemporal auto-regressive models (Space-Time Autoregressive models) or STAR was introduced in the early 1970s. It will then be generalized as GSTAR model (Generalized Space-Time Autoregressive models). In most fields of applications, one is often confronted by the fact that one of the major sources of fluctuations is seasonality. In our work we are particularly interested in the phenomenon of seasonality in spatiotemporal data. We develop a new class of models and investigates the properties and estimation methods. Make a mathematical model taking into account the spatial inter-action of different points or locations of an entire area would be a significant contribution. Indeed, a statistical treatment that takes into account this aspect and integrates appropriate way can correct a loss of information, errors in predictions, non-convergent and inefficient estimates.Ce travail porte sur les séries spatiales. On étudie les phénomènes dont l’observation est un processus aléatoire indexé par un ensemble spatial. Dans cette thèse on s’intéresse aux données bidimensionnelles régulièrement dispersées dans l’espace, on travaille alors dans un rectangle régulier (sur Z2) . Cette modélisation vise donc à construire des représentations des systèmes suivant leurs dimensions spatiales et à ses applications dans de nombreux domaines tels que la météorologie, l’océanographie, l’agronomie, la géologie, l’épidémiologie, ou encore l’économétrie etc. La modélisation spatiale permet d’aborder la question importante de la prédiction de la valeur d’un champ aléatoire en un endroit donné d’une région. On suppose que la valeur à prédire dépend des observations dans les régions voisines. Ceci montre la nécessité de tenir compte, en plus de leurs caractéristiques statistiques, des relations de dépendance spatiale entre localisations voisines, pour rendre compte de l’ensemble des structures inhérentes aux données. Dans la plupart des champs d’applications, on est souvent confronté du fait que l’une des sources majeures de fluctuations est la saisonnalité. Dans nos travaux on s’intéresse particulièrement à ce phénomène de saisonnalité dans les données spatiales. Faire une modélisation mathématique en tenant en compte l’interaction spatiale des différents points ou localités d’une zone entière serait un apport considérable. En effet un traitement statistique qui prendrait en compte cet aspect et l’intègre de façon adéquat peut corriger une perte d’information, des erreurs de prédictions, des estimations non convergentes et non efficaces

Statistical properties of the seasonal fractionally integrated separable spatial autoregressive model

In this paper we introduce a new model called Fractionally Integrated Separable Spatial Autoregressive processes with Seasonality and denoted Seasonal FISSAR. We focus on the class of separable spatial models whose correlation structure can be expressed as a product of correlations. This new modelling allows taking into account the seasonality patterns observed in spatial data. We investigate the properties of this new model providing stationary conditions, some explicit form of the autocovariance function and the spectral density. We also establish the asymptotic behaviour of the spectral density function near the seasonal frequencies.Keywords: Seasonality; Spatial short memory; Seasonal long memory; Two dimensional data; Separable process; Spatial stationary process; Spatial autocovarianc

Tumeur de Buschke-Lowenstein à localisation ano-périnéale à propos dun cas (Ano-perineal Buschke-Lowenstein tumor: A case report)

La tumeur de Buschke-Lowenstein (TBL) ou le condylome acuminé géant (CAG) est une entité clinique rare atteignant moins de 0,1 % de la population générale. Transmise essentiellement par voie sexuelle, elle est liée à linfection à Human Papilloma Virus (HPV). Elle peut dégénérer en carcinome épidermoïde de lanus plus particulièrement chez les personnes infectées par le VIH. Nous rapportons lobservation dun patient porteur de VIH, avec notion dhomosexualité, reçu pour lésions condylomateuses acuminées ano-périnéales géantes. Lobjectif est de décrire les aspects cliniques, thérapeutiques et évolutifs de cette affection

Univariate and multivariate risk factors analysis according to "symptomatic/asymptomatic" status for Period 2 (from June 26 to October 31^st, 2020).

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Spatial distribution of COVID-19 positive cases at district level.

Spatial distribution of COVID-19 positive cases at district level.</p

Epidemiological parameters comparison according to different periods.

Epidemiological parameters comparison according to different periods.</p

Heatmap of symptom frequency and histogram of asymptomatic patients by age group.

A. Heatmap of symptom frequency by age group. Symptoms are listed in rows and age groups in columns. Values in cells indicate the frequency of patients from the corresponding age group manifesting the corresponding symptom. The more the red color is accentuated, the more the symptom is frequent. The blue box targets the "cough" symptom, one of the most involved symptoms in transmission due to the dispersed micro-droplets. This symptom is less frequent in young patients (most active sub-population) compared to adult (less active sub-population). B. Histogram of asymptomatic patients by age group. Black vertical bars represent the standard errors.</p

Confirmed COVID-19 cases according to their travel history.

A. Histogram of COVID-19 cases with and without travel history. Dates of onset are represented in the x-axis and number of tested cases on the y-axis. Grey part of the bars represents cases without travel history, black bars cases circulated inside Senegal, green bars cases from the rest of the world, red bars cases from Europe and blue bars cases from other African countries. B. Country’s origin of imported COVID-19 cases.</p