ABSTRACT
Background: SARS-CoV-2 seroprevalence data in women living with HIV (WLHIV), their infants and associated risk factors in this subpopulation remain limited. We retrospectively measured SARS-CoV-2 seroprevalence from 09/2019- 12/2021 among WLHIV and their children in the PROMOTE observational cohort in Uganda, Malawi, and Zimbabwe prior to widespread SARS-CoV-2 vaccination in those countries. Method(s): Sociodemographic, clinical data and blood were collected q6 months. Plasma stored during 3 waves of the COVID-19 pandemic in East/ Southern Africa were tested for SARS-CoV-2 specific IgG antibodies (Ab) using serological assays that detect adaptive immune responses to SARS-CoV-2 spike protein. Modified-Poisson regression models were used to calculate prevalence rate ratios (PRR) and 95% confidence intervals (CI) to identify sociodemographic and clinical risk factors. Result(s): Plasma samples from 979 PROMOTE mothers and 1332 children were analysed. We found no significant differences in baseline characteristics between participants testing positive (+) and negative (-) for SARS-CoV-2 Ab. Overall maternal SARS-CoV-2 seroprevalence was 57.6% (95%CI: 54.5-60.7) and 39.3% (95%CI: 36.7-41.9) for infants. The earliest + result was detected from a sample collected on 09/2019, in Malawi. Factors significantly associated with SARS-CoV-2 seropositivity were country of origin (reference Uganda, aPRR 1.45, 95%CI: 1.24-1.69) and non-breastfeeding mother (aPRR=1.22, 95%CI: 1.02-1.48). Children above 5 years had a 10% increased risk of SARS-CoV-2 seropositivity (aPRR=1.10, 95%CI: 0.90-1.34) when compared to younger children. We found no statistically significant association with sanitation, household density, distance to clinic, maternal employment, ART regimen or viral load. Mother/infant SARS-CoV-2 serostatuses were discordant in 373/865 (43.1%) families tested: mothers+/children- in 51.2%;mothers-/children+ in 12%;child+/sibling+ concordance was 21.4%. Conclusion(s): These SARS-CoV-2 seroprevalence data indicate that by late 2021, about half of mothers and about a third of children in a cohort of HIV-affected families in eastern/southern Africa had been infected with SARS-CoV-2. Breastfeeding was protective for mothers, likely because of the need to stay home for young children. Discordant results between children within same families underscores the need to further understand transmission dynamics within households.
ABSTRACT
Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10-5 and a minibatch size of 16. © 2022 Tech Science Press. All rights reserved.
ABSTRACT
WATERLIT Abstract: In the African context, there is a paucity of data on SARS-CoV-2 infection and associated COVID-19 in pregnancy. Given the endemicity of infections such as malaria, HIV, and tuberculosis (TB) in sub-Saharan Africa (SSA), it is important to evaluate coinfections with SARS-CoV-2 and their impact on maternal/infant outcomes. Robust research is critically needed to evaluate the effects of the added burden of COVID-19 in pregnancy, to help develop evidence-based policies toward improving maternal and infant outcomes. In this perspective, we briefly review current knowledge on the clinical features of COVID-19 in pregnancy;the risks of preterm birth and cesarean delivery secondary to comorbid severity;the effects of maternal SARS-CoV-2 infection on the fetus/neonate;and in utero mother-to-child SARS-CoV-2 transmission. We further highlight the need to conduct multicountry surveillance as well as retrospective and prospective cohort studies across SSA. This will enable assessments of SARS-CoV-2 burden among pregnant African women and improve the understanding of the spectrum of COVID-19 manifestations in this population, which may be living with or without HIV, TB, and/or other coinfections/comorbidities. In addition, multicountry studies will allow a better understanding of risk factors and outcomes to be compared across countries and subregions. Such an approach will encourage and strengthen much-needed intra-African, south-to-south multidisciplinary and interprofessional research collaborations. The African Forum for Research and Education in Health's COVID-19 Research Working Group has embarked upon such a collaboration across Western, Central, Eastern and Southern Africa