Prevalence of Trachoma in Unity State, South Sudan: Results from a Large-Scale Population-Based Survey and Potential Implications for Further Surveys

Large parts of South Sudan are thought to be trachoma endemic but baseline data, required to initiate interventions, are few. District-by-district surveys, currently recommended by the World Health Organization (WHO), are often not financially or logistically viable. We therefore adapted existing WHO guidelines and combined eight counties (equivalent to districts) of Unity State into one survey area, randomly sampling 40 villages using a population-based survey design. This decision was based on a trachoma risk map and a trachoma rapid assessment, both identifying the state as likely to be highly endemic. The survey confirmed trachoma as being hyperendemic throughout Unity State, meaning that large-scale intervention should be initiated now. Simulation studies were conducted to determine the likely outcome if fewer (n = 20) or more (n = 60) villages had been sampled, confirming that precision decreased or increased, respectively. Importantly, simulation results also showed that all three sample sizes would have led to the same conclusion, namely the need for large-scale intervention. This finding suggests that district-by-district surveys may not be required for areas where trachoma is suspected to be highly prevalent but that are lacking baseline data; instead districts may be combined into a larger survey area

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

Proportional QoS over OBS networks

Optical Burst Switching (OBS) is considered as an efficient switching technique for building the next generation optical Internet. An offset-time based scheme has recently been proposed in order to provide quality-of-service (QoS) in OBS networks. Unfortunately, the proposed service differentiation has several problems. The aim of this paper is to address these problems and introduce the concept of proportional QoS into this OBS paradigm. An intentional dropping scheme is proposed so as to give a controllable burst loss probability for different service classes. In order to achieve flexible packet delay differentiation, we extend the well-known waited-time-priority (WTP) scheduler to form a burst assembling scheme. Simulations are conducted to evaluate the performance of our proportional QoS provisioning within OBS networks in terms of burst loss probability and packet delay

Risk factors for trichiasis (TT) in those aged 15 years and above.

a<p>Univariate effects adjusted for between-household and between-village variation.</p>b<p>p-value from likelihood ratio test comparing random effects logistic regression models adjusting for between-household variation with, and without, characteristic of interest.</p>c<p>Adjusted for variables included in final multivariable regression model as shown and between-household variation.</p>d<p>Variables modelled as continuous measures.</p>e<p>Protected source: handpump or well; unprotected: river, stream, pond or swamp.</p>f<p>Self-reported.</p>g<p>Observed by fieldworker.</p>h<p>Observed as flies in and around the living quarters (excluding areas around cattle) and, or faces of children; no if recorded as none or few (1–5); yes if recorded as 5 or more flies.</p

Results from sampling simulations in Unity State.

<p>Sampling simulations were repeated 1000 times, assuming 500 children were enrolled in each cluster (village) and 50 children sampled from each cluster.</p

Risk factors for Follicular trachoma (TF) in children aged 1–9 years.

a<p>Univariate effects adjusted for between-household and between-village variation.</p>b<p>p-value from likelihood ratio test comparing random effects logistic regression models adjusting for between-household and between-village variation with, and without, characteristic of interest.</p>c<p>Adjusted for variables included in final multivariable regression model as shown, between-household variation, between-village variation and county.</p>d<p>Variables modelled as continuous measures.</p>e<p>Protected source: handpump or well; unprotected: river, stream, pond or swamp.</p>f<p>Self-reported.</p>g<p>Observed by fieldworker.</p>h<p>Observed as flies in and around the living quarters (excluding areas around cattle) and, or faces of children; no if recorded as none or few (1–5); yes if recorded as 5 or more flies.</p

Prevalence of Trachoma Signs.

<p>Data are presented on percentage scale.</p><p>TF = trachomatous inflammation - follicular, TI = trachomatous inflammation, AT = active trachoma (TF and, or TI), TS = trachomatous scarring, TT = trachomatous trichiasis, CO = corneal opacity.</p>a<p>exact binomial confidence interval.</p>b<p>adjusted for age, sex, county, between-village variation and between-household variation using random effects regression models; adjusted estimates not obtained where prevalence very low (≤4%).</p

Filled density plots showing the results of the sampling simulations.

<p>Simulations were run on realisations with the median (A), minimum (B) and maximum (C) overall prevalence.</p

Study Population.

a<p>Twenty households sampled per village (except Pariang where 21 households were sampled in two villages).</p>b<p>Proportion of households per village with each attribute calculated and the mean (SD) of village proportions obtained as the county level summary measure.</p>c<p>Protected source: handpump or well; unprotected: river, stream, pond or swamp.</p>d<p>Mean (SD) of village proportions of households observed to have more than five flies in or around living areas or on children's faces, excluding around cattle.</p>e<p>Six missing values for age in males, three missing values for age in females, four missing values for sex; these 13 individuals were excluded from all analyses.</p