ABSTRACT
Latin America is one of the regions in which the COVID-19 pandemic has had a stronger impact, with more than 72 million reported infections and 1.6 million deaths until June 2022. Since this region is ecologically diverse and is affected by enormous social inequalities, efforts to identify genomic patterns of the circulating SARS-CoV-2 genotypes are necessary for the suitable management of the pandemic. To contribute to the genomic surveillance of the SARS-CoV-2 in Latin America, we extended the number of SARS-CoV-2 genomes available from the region by sequencing and analyzing the viral genome from COVID-19 patients from seven countries (Argentina, Brazil, Costa Rica, Colombia, Mexico, Bolivia and Peru). Subsequently, we analyzed the genomes circulating mainly during 2021 including records from GISAID database from Latin America. A total of 1534 genome sequences were generated from seven countries, demonstrating the laboratory and bioinformatics capabilities for genomic surveillance of pathogens that have been developed locally. For Latin America, patterns regarding several variants associated with multiple re-introductions, a relatively low percentage of sequenced samples, as well as an increment in the mutation frequency since the beginning of the pandemic, are in line with worldwide data. Besides, some variants of concern (VOC) and variants of interest (VOI) such as Gamma, Mu and Lambda, and at least 83 other lineages have predominated locally with a country-specific enrichments. This work has contributed to the understanding of the dynamics of the pandemic in Latin America as part of the local and international efforts to achieve timely genomic surveillance of SARS-CoV-2.
Subject(s)
COVID-19ABSTRACT
Continued waves, new variants, and limited vaccine deployment mean that SARS-CoV-2 tests remain vital to constrain the COVID-19 pandemic. Affordable, point-of-care (PoC) tests allow rapid screening in non-medical settings. Reverse-transcription loop-mediated isothermal amplification (RT-LAMP) is an appealing approach. A crucial step is to optimize testing in low/medium resource settings. Here, we optimized RT-LAMP for SARS-CoV-2 and human {beta} actin, and tested clinical samples in multiple countries. TTTT linker primers did not improve performance, and while guanidine hydrochloride, betaine and/or Igepal-CA-630 enhanced detection of synthetic RNA, only the latter two improved direct assays on nasopharygeal samples. With extracted clinical RNA, a 20 min RT-LAMP assay was essentially as sensitive as RT-PCR. With raw Canadian nasopharygeal samples, sensitivity was 100% (95% CI: 67.6% - 100%) for those with RT-qPCR Ct values [≤] 25, and 80% (95% CI: 58.4% - 91.9%) for those with 25 > Ct [≤] 27.2. Highly infectious, high titer cases were also detected in Colombian and Ecuadorian labs. We further demonstrate the utility of replacing thermocyclers with a portable PoC device (FluoroPLUM). These combined PoC molecular and hardware tools may help to limit community transmission of SARS-CoV-2.
Subject(s)
COVID-19ABSTRACT
Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8,000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
Subject(s)
COVID-19ABSTRACT
Background: Epidemiologic surveillance of COVID-19 is essential to collect and analyze data to improve public health decision making during the COVID-19 pandemic. There are few initiatives led by public–private alliances in Colombia and Latin America. The CoVIDA study led by Universidad de los Andes contributed with RT-PCR tests for SARS-CoV-2 in population groups with mild or asymptomatic infections in Bogotá. The present study aimed to determine the factors associated with SARS-CoV-2 infection in working adults.Methods: COVID-19 sentinel epidemiological surveillance study, from April 18, 2020 to March 29, 2021. The study included people aged 18 years or older without a history of COVID-19. Priority for inclusion was given to two main occupational groups working during the pandemic: health care workers and essential services workers with high mobility in the city. Social, demographic, and health-related factors were collected via phone survey. Afterward, the molecular test was conducted to detect SARS-CoV-2 infection.Findings: From 58,638 participants included in the study, 3,310 (5·6%) had a positive result for SARS-CoV-2 infection. A positive result was associated with the age group (18-29 years), living with more than three cohabitants, living with a COVID-19 confirmed case, having no affiliation to the health system, reporting a very low socioeconomic status, and having essential occupations.Interpretation:The CoVIDA study showed the importance of intensified epidemiological surveillance to identify groups with increased risk of infection. These groups should be prioritized in the screening, contact tracing, and vaccination strategies of the city to contribute to the pandemic mitigation.Funding: The CoVIDA study was funded through donors managed by the philanthropy department of Universidad de los Andes.Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: Ethics approval was obtained from the ethics committee of Universidad de los Andes (2020; Approval No. 1181).
Subject(s)
COVID-19ABSTRACT
Most community-specific serological surveys for SARS-CoV-2 antibodies have been done in healthcare workers and institutions. In this study, IgG antibodies specific for the virus were evaluated in individuals working at one university in Bogota-Colombia. The aim of this work was to determine previous exposure to SARS-CoV-2 in those attending the campus during city lockdown. A total of 237 individuals including 93 women and 144 men were evaluated using chemiluminescent detection of IgG anti N-viral protein. There were 32 positives giving a seroprevalence of 13.5% (10 women and 22 men) and mostly asymptomatic (68.75%). Only 13 of the seropositive individuals had previous positive detection of SARS-CoV-2 RNA by RT-qPCR done in average 91 days before serological test. Seropositive individuals did not come from localities having higher percentages of SARS-CoV-2 cases in the city. Three cluster of seropositive individuals were identified. This survey was carried out after the first peak of SARS-CoV-2 transmission in the city, and before the preparedness to reopening the campus for students in 2021. These results will help to develop some of the strategies stablished to control virus spread in the campus.
ABSTRACT
Background: COVID-19 is an acute respiratory illness caused by the novel coronavirusSARS-CoV-2. The disease has rapidly spread to most countries and territories and hascaused 14.2 million confirmed infections and 602,037 deaths as of July 19th 2020. Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance the efficiency of testing, but they are viable only at very low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. Methods: We developed machine learning methods that estimate the probability that a sample will test positive for SARS-Cov-2 based on complementary information from the sample. We use these predictions to exclude samples predicted as positive from pools. We trained our machine learning methods on a dataset of 2000 patients tested for SARS-Cov-2 from April to July in Bogota, Colombia. Findings: Our method, Smart Pooling, shows efficiency of 306% at a disease prevalence of 5% and efficiency of 107% at disease a prevalence of up to 50%, a regime in which two-stage pooling offers marginal efficiency gains compared to individual testing. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-stage pooling strategies, and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory. Interpretation: Pooled testing has been a theoretically alluring option to increase the coverage of diagnostics since its proposition by Dorfmann during World War II. Although there are examples of successfully using pooled testing to reduce the cost of diagnostics, its applicability has remained limited because efficiency drops rapidly as prevalence increases. Not only does our method provide a cost-effective solution to increase the coverage of testing amid the COVID-19 pandemic, but it also demonstrates that artificial intelligence can be used complementary with well-established techniques in the medical praxis.