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.

Risk factors for SARS-CoV-2 transmission in close contacts of adults at high risk of infection due to occupation: results from the contact tracing strategy of the CoVIDA epidemiological surveillance study in Bogotá, Colombia, in 2020-2021.

OBJECTIVES: To estimate the risk factors for SARS-CoV-2 transmission in close contacts of adults at high risk of infection due to occupation, participants of the CoVIDA study, in Bogotá D.C., Colombia. SETTING: The CoVIDA study was the largest COVID-19 intensified sentinel epidemiological surveillance study in Colombia thus far, performing over 60 000 RT-PCR tests for SARS-CoV-2 infection. The study implemented a contact tracing strategy (via telephone call) to support traditional surveillance actions performed by the local health authority. PARTICIPANTS: Close contacts of participants from the CoVIDA study. PRIMARY AND SECONDARY OUTCOME MEASURES: SARS-CoV-2 testing results were obtained (RT-PCR with CoVIDA or self-reported results). The secondary attack rate (SAR) was calculated using contacts and primary cases features. RESULTS: The CoVIDA study performed 1257 contact tracing procedures on primary cases. A total of 5551 close contacts were identified and 1050 secondary cases (21.1%) were found. The highest SAR was found in close contacts: (1) who were spouses (SAR=32.7%; 95% CI 29.1% to 36.4%), (2) of informally employed or unemployed primary cases (SAR=29.1%; 95% CI 25.5% to 32.8%), (3) of symptomatic primary cases (SAR of 25.9%; 95% CI 24.0% to 27.9%) and (4) living in households with more than three people (SAR=22.2%; 95% CI 20.7% to 23.8%). The spouses (OR 3.85; 95% CI 2.60 to 5.70), relatives (OR 1.89; 95% CI 1.33 to 2.70) and close contacts of a symptomatic primary case (OR 1.48; 95% CI 1.24 to 1.77) had an increased risk of being secondary cases compared with non-relatives and close contacts of an asymptomatic index case, respectively. CONCLUSIONS: Contact tracing strategies must focus on households with socioeconomic vulnerabilities to guarantee isolation and testing to stop the spread of the disease.

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.

Understanding how socioeconomic inequalities drive inequalities in COVID-19 infections.

Across the world, the COVID-19 pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor. Combining an epidemiological model with rich data from Bogotá, Colombia, we show that total infections and inequalities in infections are largely driven by inequalities in the ability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role because it is too slow to contain the virus. Interventions that mitigate transmission are often more effective when targeted on socioeconomically disadvantaged groups.

Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings.

Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.

Smart pooling: AI-powered COVID-19 informative group testing.

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 8000 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.

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.

COVID-19 spread, detection, and dynamics in Bogota, Colombia.

Latin America has been severely affected by the COVID-19 pandemic but estimations of rates of infections are very limited and lack the level of detail required to guide policy decisions. We implemented a COVID-19 sentinel surveillance study with 59,770 RT-PCR tests on mostly asymptomatic individuals and combine this data with administrative records on all detected cases to capture the spread and dynamics of the COVID-19 pandemic in Bogota from June 2020 to early March 2021. We describe various features of the pandemic that appear to be specific to a middle income countries. We find that, by March 2021, slightly more than half of the population in Bogota has been infected, despite only a small fraction of this population being detected. The initial buildup of immunity contributed to the containment of the pandemic in the first and second waves. We also show that the share of the population infected by March 2021 varies widely by occupation, socio-economic stratum, and location. This, in turn, has affected the dynamics of the spread with different groups being infected in the two waves.

Reconciling yield gains in agronomic trials with returns under African smallholder conditions.

Increased adoption of improved agricultural technologies is considered an essential step to address global poverty and hunger, and agronomic trials suggest intensification in developing countries could result in large yield gains. Yet the promise of new technologies does not always carry over from trials to real-life conditions, and diffusion of many technologies remains limited. We show how parcel and farmer selection, together with behavioural responses in agronomic trials, can explain why yield gain estimates from trials may differ from the yield gains of smallholders using the same inputs under real-life conditions. We provide quantitative evidence by exploiting variation in farmer selection and detailed data collection from research trials in Western Kenya on which large yield increments were observed from improved input packages for maize and soybean. After adjusting for selection, behavioural responses, and other corrections, estimates of yield gains fall to being not significantly different from zero for the input package tested on one of the crops (soybean), but remain high for the other (maize). These results suggest that testing new agricultural technologies in real-world conditions and without researcher interference early in the agricultural research and development process might help with identifying which innovations are more likely to be taken up at scale.

Challenges to capture the big five personality traits in non-WEIRD populations.

Can personality traits be measured and interpreted reliably across the world? While the use of Big Five personality measures is increasingly common across social sciences, their validity outside of western, educated, industrialized, rich, and democratic (WEIRD) populations is unclear. Adopting a comprehensive psychometric approach to analyze 29 face-to-face surveys from 94,751 respondents in 23 low- and middle-income countries, we show that commonly used personality questions generally fail to measure the intended personality traits and show low validity. These findings contrast with the much higher validity of these measures attained in internet surveys of 198,356 self-selected respondents from the same countries. We discuss how systematic response patterns, enumerator interactions, and low education levels can collectively distort personality measures when assessed in large-scale surveys. Our results highlight the risk of misinterpreting Big Five survey data and provide a warning against naïve interpretations of personality traits without evidence of their validity.