ABSTRACT
Background: Anti-SARS-CoV-2 antibody tests are increasingly used for sero-epidemiological purposes to provide a better understanding of the extent of the infection in the community, and to monitor the progression of the COVID-19 epidemic. A sero-prevalence study was conducted to estimate prior infections with SARS-CoV-2 in Addis Ababa. Methods: A cross-sectional study was conducted from April 23 to 28, 2020 among 301 randomly selected residents of Addis Ababa;sub-city health offices, health facilities and health extension workers were contacted, to obtain a population profile and to conduct the random selection of study participants. Participants were selected, who had not been in direct contact with people who had contracted COVID-19, to maintain consistency among the study population. Interviews on socio demographic and behavioural risk factors, followed by serological tests were performed for SARS-CoV-2 IgM, and IgG antibodies, using the COVID-19 IgG/IgM Rapid Test Cassette. Based on the manufacturer information, the test has a sensitivity of 87.9% and specificity of 100% for IgM;and a sensitivity of 97.2% and specificity of 100% for IgG. A Polymerase chain reaction (RT-PCR) test was also done on combined nasopharyngeal and oropharengeal swabs. Findings: The unadjusted antibody-based crude SARS-CoV-2 prevalence was 7.6% and the adjusted (weighted average) SARS-CoV-2 prevalence was estimated at 8.8% (95% CI 5.5%-11.6%) for the study population. Higher sero-prevalence were observed for males (9.0%), age below 50 years (8.2%), students and unemployed (15.6%), as well as those with primary education (12.1%), educated above high school (37.9%), non- smokers (78.7%), with no history of regular alcohol (53.8%), no chat (70.8%), and no shisha use (94.7%). According to the findings, a significantly higher number of individuals had been infected in Addis Ababa as compared to what was being detected and reported by the RT-PCR test, which is suggestive of community transmission.
ABSTRACT
Pandemics originated by Covid-19 has caused many reactions in the world. Each country has reacted with different health policies. In this work, we analyze the publications of African citizens on Social networks (Twitter) to understand the pandemic scenario during the study period using a text mining approach. The study also adopted a statistical model to quantify the impact of daily COVID-19 cases on the frequency of tweets. Findings show that African tweets are related to awareness creation and policies to control the spread of COVID-19.