Assessing pandemic preparedness, response, and lessons learned from the COVID-19 pandemic in four south American countries: agenda for the future.

Introduction: The COVID-19 pandemic emerged in a context that lacked adequate prevention, preparedness, and response (PPR) activities, and global, regional, and national leadership. South American countries were among world's hardest hit by the pandemic, accounting for 10.1% of total cases and 20.1% of global deaths. Methods: This study explores how pandemic PPR were affected by political, socioeconomic, and health system contexts as well as how PPR may have shaped pandemic outcomes in Argentina, Brazil, Colombia, and Peru. We then identify lessons learned and advance an agenda for improving PPR capacity at regional and national levels. We do this through a mixed-methods sequential explanatory study in four South American countries based on structured interviews and focus groups with elite policy makers. Results: The results of our study demonstrate that structural and contextual barriers limited PPR activities at political, social, and economic levels in each country, as well as through the structure of the health care system. Respondents believe that top-level government officials had insufficient political will for prioritizing pandemic PPR and post-COVID-19 recovery programs within their countries' health agendas. Discussion: We recommend a regional COVID-19 task force, post-pandemic recovery, social and economic protection for vulnerable groups, improved primary health care and surveillance systems, risk communication strategies, and community engagement to place pandemic PPR on Argentina, Brazil, Colombia, and Peru and other South American countries' national public health agendas.

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts.

Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.

Effectiveness and adherence to closed face shields in the prevention of COVID-19 transmission: a non-inferiority randomized controlled trial in a middle-income setting (COVPROSHIELD).

BACKGROUND: The use of respiratory devices can mitigate the spread of diseases such as COVID-19 in community settings. We aimed to determine the effectiveness of closed face shields with surgical face masks to prevent SARS-CoV-2 transmission in working adults during the COVID-19 pandemic in Bogotá, Colombia. METHODS: An open-label non-inferiority randomized controlled trial that randomly assigned participants to one of two groups: the intervention group was instructed to wear closed face shields with surgical face masks, and the active control group was instructed to wear only surgical face masks. The primary outcome was a positive reverse transcription polymerase chain reaction test, IgG/IgM antibody test for SARS-CoV-2 detection, or both during and at the end of the follow-up period of 21 days. The non-inferiority limit was established at - 5%. RESULTS: A total of 316 participants were randomized, 160 participants were assigned to the intervention group and 156 to the active control group. In total, 141 (88.1%) participants in the intervention group and 142 (91.0%) in the active control group completed the follow-up. PRIMARY OUTCOME: a positive SARS-CoV-2 test result was identified in one (0.71%) participant in the intervention group and three (2.1%) in the active control group. In the intention-to-treat analysis, the absolute risk difference was - 1.40% (95% CI [- 4.14%, 1.33%]), and in the per-protocol analysis, the risk difference was - 1.40% (95% CI [- 4.20, 1.40]), indicating non-inferiority of the closed face shield plus face mask (did not cross the non-inferiority limit). CONCLUSIONS: The use of closed face shields and surgical face masks was non-inferior to the surgical face mask alone in the prevention of SARS-CoV-2 infection in highly exposed groups. Settings with highly active viral transmission and conditions such as poor ventilation, crowding, and high mobility due to occupation may benefit from the combined use of masks and closed face shields to mitigate SARS-CoV-2 transmission. TRIAL REGISTRATION: ClinicalTrials.gov NCT04647305 . Registered on November 30, 2020.

Multicenter international assessment of a SARS-CoV-2 RT-LAMP test for point of care clinical application

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.

Factors Associated With SARS-CoV-2 Infection in Bogotá, Colombia: Results From a Large Epidemiological Surveillance Study.

BACKGROUND: Epidemiologic surveillance of COVID-19 is essential to collect and analyse data to improve public health decision making during the pandemic. There are few initiatives led by public-private alliances in Colombia and Latin America. The CoVIDA project contributed with RT-PCR tests for SARS-CoV-2 in mild or asymptomatic populations in Bogotá. The present study aimed to determine the factors associated with SARS-CoV-2 infection in working adults. METHODS: COVID-19 intensified 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. Two main occupational groups were included: healthcare and essential services workers with high mobility in the city. Social, demographic, and health-related factors were collected via phone survey. Afterwards, the molecular test was conducted to detect SARS-CoV-2 infection. FINDINGS: From the 58,638 participants included in the study, 3,310 (5·6%) had a positive result. A positive result was associated with the age group (18-29 years) compared with participants aged 60 or older, participants living with more than three cohabitants, living with a confirmed case, having no affiliation to the health system compared to those with social health security, reporting a very low socioeconomic status compared to those with higher socioeconomic status, and having essential occupations compared to healthcare workers. INTERPRETATION: The CoVIDA study showed the importance of intensified epidemiological surveillance to identify groups with increased risk of infection. These groups should be prioritised in the screening, contact tracing, and vaccination strategies to mitigate the pandemic. FUNDING: The CoVIDA study was funded through donors managed by the philanthropy department of Universidad de los Andes.

Seroreactivity to SARS-CoV-2 in individuals attending a university campus in Bogota Colombia

Most community-specific serological surveys for SARS-CoV-2 antibodies have been performed in healthcare workers and institutions. In this study, IgG antibodies specific to the virus were evaluated in individuals working at a university campus 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 between November and December 2020. There were 32 positives individuals corresponding to a seroprevalence of 13.5% (10 women and 22 men) and mostly asymptomatic (68.75%) and three cluster of seropositive individuals were identified. Only 13 of the seropositive individuals had previous positive detection of SARS-CoV-2 RNA by RT-qPCR performed in average 91 days before serological test. Seropositive individuals did not come from boroughs having higher percentages of SARS-CoV-2 cases in the city. 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, demonstrating a low seroprevalence in high percentage of asymptomatic. These results will help to evaluate some of the strategies stablished to control virus spread in the campus or other similar communities.

Smart Pooling: AI-powered COVID-19 testing

BackgroundCOVID-19 is an acute respiratory illness caused by the novel coronavirus SARS-CoV-2. The disease has rapidly spread to most countries and territories and has caused 14{middle dot}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 testing efficiency, 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. MethodsWe 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 samples from more than 8,000 patients tested for SARS-Cov-2 from April to July in Bogota, Colombia. FindingsOur 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 (see Figure 1). 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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/20152983v2_fig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@13980eforg.highwire.dtl.DTLVardef@3fd3beorg.highwire.dtl.DTLVardef@668b2eorg.highwire.dtl.DTLVardef@3bb6c3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Efficiency of Smart Pooling compared to standard testing methods on Patient Dataset. Smart pooling achieves higher efficiencies than two-stage pooling and individual testing for prevalences of the disease of up to 50% on the Patient Dataset. The average efficiency measures the overall efficiency of each method in the complete prevalence range. C_FIG InterpretationPooled 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. FundingFaculty of Engineering, Universidad de los Andes, Colombia. 1 Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe acute respiratory illness COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) labeled COVID-19 as a pandemic in March 2020. Reports from February 2020 indicated the possibility of asymptomatic transmission of the virus, which has called for molecular testing to identify carriers of the disease and prevent them from spreading it. The dramatic rise in the global need for molecular testing has made reagents scarce. Pooling strategies for massive diagnostics were initially proposed to diagnose syphilis during World War II, but have not yet seen widespread use mainly because their efficiency falls even at modest disease prevalence. We searched PubMed, BioRxiv, and MedRxiv for articles published in English from inception to July 15th 2020 for keywords "pooling", "testing" AND "COVID-19", AND "machine learning" OR "artificial intelligence". Early studies for pooled molecular testing of SARS-CoV-2 revealed the possibility of detecting single positive samples in dilutions of samples from up to 32 individuals. The first reports of pooled testing came in March from Germany and the USA. These works suggested that it was feasible to conduct pooled testing as long as the prevalence of the disease was low. Numerous theoretical works have focused only on finding or adapting the ideal pooling strategy to the prevalence of the disease. Nonetheless, many do not consider other practical limitations of putting these strategies into practice. Reports from May 2020 indicated that it was feasible to predict an individuals status with machine learning methods based on reported symptoms. Added value of this studyWe show how artificial intelligence methods can be used to enhance, but not replace, existing well-proven methods, such as diagnostics by qPCR. We show that in this fashion, pooled testing can yield efficiency gains even as prevalence increases. Our method does not compromise the sensitivity or specificity of the diagnostics, as these are still given by the molecular test. The artificial intelligence models are simple, and we make them free to use. Remarkably, artificial intelligence methods can continuously learn from every set of samples and thus increase their performance over time. Implications of all the available evidenceUsing artificial intelligence to enhance rather than replace molecular testing can make pooling testing feasible, even as disease incidence rises. This approach could make pooled testing an effective tool to tackle the diseases progression, particularly in territories with limited resources.