An analysis of the accuracy of COVID-19 country transmission classification.

Accurate epidemiological classification guidelines are essential to ensure implementation of adequate public health and social measures. Here, we investigate two frameworks, published in March 2020 and November 2020 by the World Health Organization (WHO) to categorise transmission risks of COVID-19 infection, and assess how well the countries' self-reported classification tracked their underlying epidemiological situation. We used three modelling approaches: an ordinal longitudinal model, a proportional odds model and a machine learning One-Rule classification algorithm. We applied these models to 202 countries' daily transmission classification and epidemiological data, and study classification accuracy over time for the period April 2020 to June 2021, when WHO stopped publishing country classifications. Overall, the first published WHO classification, purely qualitative, lacked accuracy. The incidence rate within the previous 14 days was the best predictor with an average accuracy throughout the period of study of 61.5%. However, when each week was assessed independently, the models returned predictive accuracies above 50% only in the first weeks of April 2020. In contrast, the second classification, quantitative in nature, increased significantly the accuracy of transmission labels, with values as high as 94%.

Demographics, epidemiology and the impact of vaccination campaigns in a measles-free world - Can elimination be maintained?

INTRODUCTION: All six WHO regions currently have goals for measles elimination by 2020. Measles vaccination is delivered via routine immunization programmes, which in most sub-Saharan African countries reach children around 9months of age, and supplementary immunization activities (SIAs), which target a wider age range at multi-annual intervals. In the absence of endemic measles circulation, the proportion of individuals susceptible to measles will gradually increase through accumulation of new unvaccinated individuals in each birth cohort, increasing the risk of an epidemic. The impact of SIAs and the financial investment they require, depend on coverage and target age range. MATERIALS AND METHODS: We evaluated the impact of target population age range for periodic SIAs, evaluating outcomes for two different levels of coverage, using a demographic and epidemiological model adapted to reflect populations in 4 sub-Saharan African countries. RESULTS: We found that a single SIA can maintain elimination over short time-scales, even with low routine coverage. However, maintaining elimination for more than a few years is difficult, even with large (high coverage/wide age range) recurrent SIAs, due to the build-up of susceptible individuals. Across the demographic and vaccination contexts investigated, expanding SIAs to target individuals over 10years did not significantly reduce outbreak risk. CONCLUSIONS: Elimination was not maintained in the contexts we evaluated without a second opportunity for vaccination. In the absence of an expanded routine program, SIAs provide a powerful option for providing this second dose. We show that a single high coverage SIA can deliver most key benefits in terms of maintaining elimination, with follow-up campaigns potentially requiring smaller investments. This makes post-campaign evaluation of coverage increasingly relevant to correctly assess future outbreak risk.

Implementation of an extended ZINB model in the study of low levels of natural gastrointestinal nematode infections in adult sheep.

BACKGROUND: In this study, two traits related with resistance to gastrointestinal nematodes (GIN) were measured in 529 adult sheep: faecal egg count (FEC) and activity of immunoglobulin A in plasma (IgA). In dry years, FEC can be very low in semi-extensive systems, such as the one studied here, which makes identifying animals that are resistant or susceptible to infection a difficult task. A zero inflated negative binomial model (ZINB) model was used to calculate the extent of zero inflation for FEC; the model was extended to include information from the IgA responses. RESULTS: In this dataset, 64% of animals had zero FEC while the ZINB model suggested that 38% of sheep had not been recently infected with GIN. Therefore 26% of sheep were predicted to be infected animals with egg counts that were zero or below the detection limit and likely to be relatively resistant to nematode infection. IgA activities of all animals were then used to decide which of the sheep with zero egg counts had been exposed and which sheep had not been recently exposed. Animals with zero FEC and high IgA activity were considered resistant while animals with zero FEC and low IgA activity were considered as not recently infected. For the animals considered as exposed to the infection, the correlations among the studied traits were estimated, and the influence of these traits on the discrimination between unexposed and infected animals was assessed. CONCLUSIONS: The model presented here improved the detection of infected animals with zero FEC. The correlations calculated here will be useful in the development of a reliable index of GIN resistance that could be of assistance for the study of host resistance in studies based on natural infection, especially in adult sheep, and also the design of breeding programs aimed at increasing resistance to parasites.

APC(ste9/srw1) promotes degradation of mitotic cyclins in G(1) and is inhibited by cdc2 phosphorylation.

Fission yeast ste9/srw1 is a WD-repeat protein highly homologous to budding yeast Hct1/Cdh1 and Drosophila Fizzy-related that are involved in activating APC/C (anaphase-promoting complex/cyclosome). We show that APC(ste9/srw1) specifically promotes the degradation of mitotic cyclins cdc13 and cig1 but not the S-phase cyclin cig2. APC(ste9/srw1) is not necessary for the proteolysis of cdc13 and cig1 that occurs at the metaphase-anaphase transition but it is absolutely required for their degradation in G(1). Therefore, we propose that the main role of APC(ste9/srw1) is to promote degradation of mitotic cyclins when cells need to delay or arrest the cell cycle in G(1). We also show that ste9/srw1 is negatively regulated by cdc2-dependent protein phosphorylation. In G(1), when cdc2-cyclin kinase activity is low, unphosphorylated ste9/srw1 interacts with APC/C. In the rest of the cell cycle, phosphorylation of ste9/srw1 by cdc2-cyclin complexes both triggers proteolysis of ste9/srw1 and causes its dissociation from the APC/C. This mechanism provides a molecular switch to prevent inactivation of cdc2 in G(2) and early mitosis and to allow its inactivation in G(1).

The puc1 cyclin regulates the G1 phase of the fission yeast cell cycle in response to cell size.

Eukaryotic cells coordinate cell size with cell division by regulating the length of the G1 and G2 phases of the cell cycle. In fission yeast, the length of the G1 phase depends on a precise balance between levels of positive (cig1, cig2, puc1, and cdc13 cyclins) and negative (rum1 and ste9-APC) regulators of cdc2. Early in G1, cyclin proteolysis and rum1 inhibition keep the cdc2/cyclin complexes inactive. At the end of G1, the balance is reversed and cdc2/cyclin activity down-regulates both rum1 and the cyclin-degrading activity of the APC. Here we present data showing that the puc1 cyclin, a close relative of the Cln cyclins in budding yeast, plays an important role in regulating the length of G1. Fission yeast cells lacking cig1 and cig2 have a cell cycle distribution similar to that of wild-type cells, with a short G1 and a long G2. However, when the puc1(+) gene is deleted in this genetic background, the length of G1 is extended and these cells undergo S phase with a greater cell size than wild-type cells. This G1 delay is completely abolished in cells lacking rum1. Cdc2/puc1 function may be important to down-regulate the rum1 Cdk inhibitor at the end of G1.