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

COVID-19 mortality associated with multidimensional poverty, ethnicities, and armed conflict in Colombia: retrospective observational study based on national records

Background

The COVID-19 pandemic in Colombia exposed the interplay between multidimensional poverty, ethnic diversity, and armed conflict. This crisis worsened inequalities, disproportionately affecting Afro-Colombian and Indigenous communities already living in adverse conditions. Armed conflict further weakened social capital, limiting wellbeing in impoverished and violent regions. This study aimed to investigate the associations of poverty, ethnicity, and conflict zones with COVID-19 mortality.

Methods

A retrospective observational study based on national records was conducted in Colombia from March 2020 to December 2022, in which COVID-19 cases confirmed by PCR or antigen tests were analyzed. Demographic, ethnic, and mortality data were obtained from the National Institute of Health, while poverty data were obtained from the DANE's 2018 census. Descriptive analyses, chi-square tests, and bivariate analyses were performed. A multilevel logistic regression model identified risk factors, reporting odds ratios (ORs) with 95% confidence intervals (CIs).

Results

Between March 2020 and December 2022, 6,313,872 COVID-19 cases were recorded in Colombia. Afro-Colombians 2.58% and Indigenous peoples (2.75%) had higher mortality than White/Mestizo individuals did (2.24%). ZOMAC municipalities reported a mortality rate of 3.61%, and PDET municipalities reported a mortality rate of 3.20%. Multilevel analysis revealed increased mortality risks for Afro-Colombians (OR 1.14, 95% CI 1.11-1.18), Indigenous peoples (OR 1.22, 95% CI 1.17-1.28), and residents of ZOMAC (OR 1.69, 95% CI 1.66-1.72) and PDET municipalities (OR 1.34, 95% CI 1.44-1.49).

Conclusions

This study highlights disparities in COVID-19 mortality influenced by ethnic, socioeconomic, and territorial factors, with a greater burden on Afro-Colombians, Indigenous peoples, and conflict zones. Public health policies must address these structural inequalities.

Modeling the spatiotemporal transmission of COVID-19 epidemic by coupling the heterogeneous impact of detection rates: A case study in Hong Kong

During the COVID-19 epidemic, many infections may have been undiagnosed in communities (hidden cases) due to low detection rates, thus exacerbating the overall prevalence of the epidemic. However, the heterogeneity of detection rates poses a challenge in simulating the proportion and spatial distribution of hidden cases. Coupling the heterogeneous impact of detection rates to extend epidemic modeling is necessary for forecasting the health burden and mitigating the inequity of testing resources. In this study, we developed an agent-based model integrated with the Susceptible-Exposed-Reported-Hidden-Removed (SERHR) model to simulate the spatiotemporal transmission of reported and hidden cases (RH-ABM). The RH-ABM was fitted with data for the fifth wave of infection in Hong Kong induced by the Omicron variant. We conducted multi-scenario simulations based on various testing strategies to assess the local variation in attack rates. The RH-ABM predicted that maintaining a constant high detection rate would reduce the average attack rate from 65.62% to 53.09%. Increasing detection rates in groups with many individuals and daily close contact can also assist in controlling the health burden of outbreaks. The variation in the attack rates is strongly associated with changes in the region-stratified detection rates. In addition, The RH-ABM estimated that allocating limited testing resources based on demographic distribution and human mobility data is effective for controlling the average attack rate.

Exploring Spatial Inequalities in COVID-19 Mortality and Their Association With Multidimensional Poverty in Colombia: A Spatial Analysis Study

Objectives

The objective is to examine spatial inequalities in COVID-19 mortality rates in Colombia in relation to the spatial distribution of multidimensional poverty.

Methods

A retrospective spatial epidemiological study was conducted in Colombia from 2020 to 2022. Spatial statistics such as Moran's I index, LISA analysis, and simultaneous autoregressive conditional (SAC) regression models were used.

Results

The Moran's I index for different years was as follows: 2020: 0.3 (p = 0.0001), 2021: 0.27 (p = 0.0001), and 2022: 0.26 (p = 0.0001). In 2020, the significant variables were low educational achievement, barriers to early childhood care, child labor, school non-attendance, informal employment, lack of health insurance, inadequate floor material, and critical overcrowding. In 2021, the significant variables were low educational achievement, critical overcrowding, inadequate excreta disposal, and lack of access to water sources. In 2022, the significant variables were school lag and inadequate excreta disposal.

Conclusion

This study revealed that in Colombia, a series of socioeconomic and health factors are interconnected and contribute to COVID-19 mortality. These changes may reflect various socioeconomic, political, and environmental dynamics that shifted during the pandemic years.

Longitudinal Follow-Up of the Specific Antibody Response to SARS-CoV-2 Vaccination in Colombia

A total of 5011 adult volunteers attending vaccination centers in different regions of Colombia were enrolled in a 1-year prospective observational cohort study to evaluate the immunogenicity and effectiveness of SARS-CoV-2-based vaccines as part of a National Vaccine Program established to contain the COVID-19 pandemic. Following informed consent, 5,011 participants underwent a sociodemographic survey and PCR testing to assess SARS-CoV-2 infection. Blood samples were collected, and serum fractions were obtained from a participant subsample (n = 3441) at six-time points to assess virus-specific IgG responses to the Spike protein, its Receptor Binding Domain, and the Nucleoprotein by ELISA. Additionally, antibody-neutralizing activity was evaluated using a cPass SARS-CoV-2 neutralization kit. Most participants (95.8%; n = 4802) received between one Ad26. COV2.S (Janssen vaccine) and four vaccine doses of BNT162b2 (Pfizer/BioNTech), AZD1222 (AstraZeneca), mRNA-1273 (Moderna), CoronaVac (Sinovac), with some receiving vaccine combinations; a small group, 4.2% (n = 209), remained unvaccinated. Throughout the study, only 8.76% (n = 439) of the participants tested positive for SARS-CoV-2 by PCR. Notably, all participants seroconverted for IgG antibodies, with high seropositivity rates for S (99.8%; n = 4795), RBD (99.7%; n = 1691), and N (92.7%; n = 3072) proteins. Moreover, significant (92%-97%) neutralizing activity was observed for all four SARS-CoV-2 circulating variants. This study highlights the importance of assessing the duration of the IgG response to SARS-CoV-2 elicited by vaccination and infection, and the antibody neutralizing activity as a potential surrogate marker of protection. These findings provide important insight for further strengthening the vaccination strategies to control COVID-19.

Unveiling pandemic patterns: a detailed analysis of transmission and severity parameters across four COVID-19 waves in Bogotá, Colombia

BACKGROUND

Despite a wealth of data from high-income countries, there is limited information on the distinct epidemiological patterns observed in diverse, densely populated regions within Latin America. This retrospective analysis of COVID-19's four major waves in Bogotá, Colombia, evaluates 1.77 million cases in detail.

METHODS

A comprehensive suite of statistical methods was employed. Transmission dynamics were assessed by estimating the instantaneous reproduction number R ( t ) , while variant-specific transmission advantages were estimated using multinomial logistic regression models. Disease severity was assessed through a suite of indicators: Hospitalisation Case Ratio (HCR), intensive care unit case ratio (ICU-CR), case fatality ratio (CFR), hospitalisation fatality ratio (HFR), and ICU fatality ratio (ICU-FR). Additionally, we analysed the distribution of hospitalisations, ICU admissions, and fatalities by age group and wave. We employed a Bayesian hierarchical model to capture epidemiological delays-such as onset-to-death, hospitalisation, and ICU admission durations to estimate hospital and ICU stay durations.

RESULTS

Our findings reveal substantial variation in R ( t ) , with peaks exceeding 2.5 during the ancestral and Omicron waves. Over the course of the pandemic, we observed a 78% reduction in CFR, underscoring shifts in clinical severity. The third wave, associated with the Mu variant, recorded the highest case and death counts, alongside a decreased CFR, an elevated HFR, and a shift in the most affected age group towards younger populations. In contrast, the fourth wave, driven by the Omicron variant, exhibited the highest reproduction number and the lowest overall severity. This wave was characterised by a significant increase in pediatric hospitalisations. The study reveals a continued decline in the mean durations of hospital and ICU stays across the four waves, with hospital stays decreasing from 10.84 to 7.85 days and ICU stays dropping from 16.2 to 12.4 days.

CONCLUSIONS

This study reveals significant shifts in transmission and severity metrics-including mortality, hospitalisation, and ICU rates and stays-across age groups during Bogotá's four COVID-19 waves. These insights underscore the value of retrospective analyses to understand the pandemic's varied impact and inform public health strategies in diverse urban settings.

Social epidemiology of urban COVID-19 inequalities in Latin America and Canada

BACKGROUND

The COVID-19 pandemic has spread through pre-existing fault lines in societies, deepening structural barriers faced by precarious workers, low-income populations, and racialized communities in lower income sub-city units. Many studies have quantified the magnitude of inequalities in COVID-19 distribution within cities, but few have taken an international comparative approach to draw inferences on the ways urban epidemics are shaped by social determinants of health.

METHODS

Guided by critical epidemiology, this study quantifies sub-city unit-level COVID-19 inequalities across eight of the largest metropolitan areas of Latin America and Canada. Leveraging new open-data sources, we use concentration indices to quantify income- and vulnerability-related inequalities in incidence, test positivity, and deaths over the first 125 weeks of the pandemic between January 2020 and May 2022.

RESULTS

Our findings demonstrate that incidence, deaths, and test positivity are all less concentrated in low-income sub-city units than would be expected, with incidence ranging concentration in lower income neighbourhoods in Toronto (CI = -0.07) to concentration in higher income neighbourhoods in Mexico City (CI = 0.33). Drawing on relevant studies and evaluations of data reliability, we conclude that the best available public surveillance data for the largest cities in Latin America are likely not reliable measures of the true COVID-19 disease burden. We also identify recurring trends in the evolution of inequalities across most cities, concluding that higher income sub-city units were frequent early epicentres of COVID-19 transmission across the Latin America and Canada.

CONCLUSIONS

Just as critical epidemiology points to individuals biologically embodying the material and social conditions in which we live, it may be just as useful to think of cities reifying their material and social inequities in the form of sub-city unit-level infectious disease inequities. By shifting away from a typical vulnerability-based social determinants of health frame, policymakers could act to redress and reduce externalities stemming from sub-city unit-level income inequality through redistributive and equity-promoting policies to shift the centre of gravity of urban health inequalities before the next infectious disease epidemic occurs.

Trends in mental health before and after the onset of the COVID-19 pandemic: a longitudinal survey of a conflict-affected population in Colombia

BACKGROUND

Focusing on the Meta region in Colombia, we investigated the relationship between mental health, the COVID-19 pandemic, and social determinants of health influenced by over five decades of civil conflict. We studied the post-2016 peace agreement trends in mental health for the population of Meta, before and after the local onset of the pandemic.

METHOD

We conducted three rounds of a longitudinal health survey in years 2018 with N = 1309 (Women = 709; Men = 600); 2019 with N = 1106 (Women = 597; Men = 509); and 2020 with N = 905 (Women = 499; Men = 406). We measured mental health through the Self-Report Questionnaire (SRQ-20), investigating population trends in the average SRQ score and SRQ-positive frequency (SRQ + , indicating positive tendency towards experiencing mental health disorders).

RESULTS

Between 2018 and 2020, there were reductions in the mean SRQ-20 score by 1.74 points (95% CI -2.30 to -1.18) and in SRQ + frequency by 15 percentage points (95% CI -21.0 to -9.0) for the Meta population. Yet specific subgroups have become more vulnerable to mental illness during the pandemic, for example older age groups (e.g., increase in mean SRQ score among over 60 s by 2.49 points, 95% CI 0.51 to 4.46) and people living with children younger than five years-old (e.g., increase in mean SRQ score by 0.64 points, 95% CI 0.07 to 1.20). Increased mental health vulnerability among specific subgroups may be related to differences in the likelihood of knowing people who tested positive for COVID-19 or died from itf having been in quarantine.

CONCLUSION

Our findings support the importance of public policies in Colombia (and other low- and middle-income countries) that address the social determinants of mental illness whose influence was likely exacerbated by the pandemic, including persistent job insecurity leading to work and financial pressures, and inadequate support networks for isolated individuals and vulnerable caregivers.

COVID-19 epidemic in Panama

Objective

It is necessary to establish the evolution that the pandemic has had in Panama by weeks and months and to clearly establish the existence of surges or peaks, according to cases and deaths and the relationship with age groups.

Methodology

We conducted a retrospective cohort study of all confirmed COVID-19 cases reported by the Ministry of Health of Panama during the first 3 years of the epidemic (March 9, 2020, March 11, 2023). All cases were obtained from information provided by the Ministry of Health. We obtained daily information of the population at the national level reported as new cases, deaths, admission to hospitals, admission to intensive care units and by age groups. The information is classified by epidemiological week and by month from the diagnosis of the first case until March 2023.

Results

During the three years of the study, 1,032,316 cases of COVID-19 were registered in the Republic of Panama, and the number of deaths reported was 8,621, for a fatality rate of 0.83 % throughout that period. The number of deaths decreased over the 3 years of the pandemic; however, similar to the cases, there were periods of surges (peaks) per year in June/July and in December/January. The lethality progressively increased according to the age of the affected patients. During the first year, the lethality in those under 20 years of age was 0.05 %, and in those over 80 years old, it was 17.54 %. This pattern was maintained during the second year; however, there was a large decrease in all age groups.

Conclusion

the highest lethality rate in Panama occurred in the first year of the pandemic, with a great decrease in the third year; the impact of lethality is proportional to the age of the individual, with a high possibility of death in those over 80 years of age. During each pandemic year, there are two peaks (surges of new cases and deaths) per year, which are important times to take into account to generate strategies aimed at reducing the impact.

Genomic epidemiology of SARS-CoV-2 variants during the first two years of the pandemic in Colombia

BACKGROUND

The emergence of highly transmissible SARS-CoV-2 variants has led to surges in cases and the need for global genomic surveillance. While some variants rapidly spread worldwide, other variants only persist nationally. There is a need for more fine-scale analysis to understand transmission dynamics at a country scale. For instance, the Mu variant of interest, also known as lineage B.1.621, was first detected in Colombia and was responsible for a large local wave but only a few sporadic cases elsewhere.

METHODS

To better understand the epidemiology of SARS-Cov-2 variants in Colombia, we used 14,049 complete SARS-CoV-2 genomes from the 32 states of Colombia. We performed Bayesian phylodynamic analyses to estimate the time of variants' introduction, their respective effective reproductive number, and effective population size, and the impact of disease control measures.

RESULTS

Here, we detect a total of 188 SARS-CoV-2 Pango lineages circulating in Colombia since the pandemic's start. We show that the effective reproduction number oscillated drastically throughout the first two years of the pandemic, with Mu showing the highest transmissibility (Re and growth rate estimation).

CONCLUSIONS

Our results reinforce that genomic surveillance programs are essential for countries to make evidence-driven interventions toward the emergence and circulation of novel SARS-CoV-2 variants.

COVID-19 in Latin America: A Snapshot in Time and the Road Ahead

Since its initial detection in Brazil in February 2020, SARS-CoV-2 and the associated COVID-19 pandemic have continued to devastate Latin America. Specific comorbidities, as well as sociodemographic and lifestyle factors that may be more prevalent in underserved areas, have been identified as risk factors for COVID-19 infection or associated adverse outcomes. Dynamics of infections and deaths in Latin America have varied by country and temporally, as has SARS-CoV-2 variant prevalence; however, more recently, the Delta and subsequent Omicron variants have become ubiquitous. Successful pandemic responses have involved robust infection mitigation measures, testing, and smart deployment of healthcare resourcing. While in some Latin American countries up to 90% of the population is fully vaccinated (i.e., 2 doses) against COVID-19, other countries have failed to reach the World Health Organization's 70% target. Continued focus on comprehensive surveillance, strategies to maximize vaccine availability and uptake, and mitigation of collateral damage on other aspects of public health and social services are critical for managing the COVID-19 pandemic. This review summarizes the COVID-19 experience in Latin America, including epidemiology and vaccination. Key learnings and future considerations for the ongoing pandemic response are also discussed.

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.

Cumulative incidence, prevalence, seroconversion, and associated factors for SARS-CoV-2 infection among healthcare workers of a University Hospital in Bogotá, Colombia

This study aimed to determine the cumulative incidence, prevalence, and seroconversion of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its associated factors among healthcare workers (HCWs) of a University Hospital in Bogotá, Colombia. An ambispective cohort was established from March 2020 to February 2021. From November 2020 to February 2021, SARS-CoV-2 antibodies were measured on two occasions 14-90 days apart to determine seroprevalence and seroconversion. We used multivariate log-binomial regression to evaluate factors associated with SARS-CoV-2 infection. Among 2,597 HCWs, the cumulative incidence of infection was 35.7%, and seroprevalence was 21.5%. A reduced risk of infection was observed among those aged 35-44 and ≥45 years (adjusted relative risks [aRRs], 0.84 and 0.83, respectively), physicians (aRR, 0.77), those wearing N95 respirators (aRR, 0.82) and working remotely (aRR, 0.74). Being overweight (aRR, 1.18) or obese (aRR, 1.24); being a nurse or nurse assistant (aRR, 1.20); working in the emergency room (aRR, 1.45), general wards (aRR, 1.45), intensive care unit (aRR, 1.34), or COVID-19 areas (aRR, 1.17); and close contact with COVID-19 cases (aRR, 1.47) increased the risk of infection. The incidence of SARS-CoV-2 infection found in this study reflects the dynamics of the first year of the pandemic in Bogotá. A high burden of infection calls for strengthening prevention and screening measures for HCWs, focusing especially on those at high risk.

Regional disparities in SARS-CoV-2 infections by labour market indicators: a spatial panel analysis using nationwide German data on notified infections

Background

Regional labour markets and their properties are named as potential reasons for regional variations in levels of SARS-CoV-2 infections rates, but empirical evidence is missing.

Methods

Using nationwide data on notified laboratory-confirmed SARS-CoV-2 infections, we calculated weekly age-standardised incidence rates (ASIRs) for working-age populations at the regional level of Germany’s 400 districts. Data covered nearly 2 years (March 2020 till December 2021), including four main waves of the pandemic. For each of the pandemic waves, we investigated regional differences in weekly ASIRs according to three regional labour market indicators: (1) employment rate, (2) employment by sector, and (3) capacity to work from home. We use spatial panel regression analysis, which incorporates geospatial information and accounts for regional clustering of infections.

Results

For all four pandemic waves under study, we found that regions with higher proportions of people in employment had higher ASIRs and a steeper increase of infections during the waves. Further, the composition of the workforce mattered: rates were higher in regions with larger secondary sectors or if opportunities of working from home were comparatively low. Associations remained consistent after adjusting for potential confounders, including a proxy measure of regional vaccination progress.

Conclusions

If further validated by studies using individual-level data, our study calls for increased intervention efforts to improve protective measures at the workplace, particularly among workers of the secondary sector with no opportunities to work from home. It also points to the necessity of strengthening work and employment as essential components of pandemic preparedness plans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07643-5.

Covid-19 in unequal societies

We document the heterogeneous effect of Covid-19 on health and economic outcomes across socioeconomic strata in Bogotá. We assess its distributional impact and evaluate policy counterfactuals in a heterogeneous agent quantitative dynamic general equilibrium model intertwined with a behavioral epidemiological model.

Seroprevalence of IgM and IgG anti-SARS-COV-2 and associated factors among agricultural workers in Colombia

Background

The population of South America has been severely affected by the COVID-19 pandemic. In this region, during the year 2020, high seroprevalence percentages were reported, which have been associated with the socioeconomic characteristics of the population, mainly in urban areas. However, a relative lack of information on the dynamics of the pandemic in rural areas of these countries, where the population is more vulnerable, is still present. This study determined antibody prevalence against SARS-CoV-2 in urban and rural food producing workers in Colombia.

Methods

A total of 1242 workers, urban and rural, linked to poultry, dairy, and meat production and supply chains, were analyzed through a sociodemographic survey and two serological tests against S and N proteins of SARS-CoV-2.

Results

78.7% were male. 50.9% of the participants were rural inhabitants, with an average age of 40.9 years old. 39.2% had IgM and IgG against SARS-CoV-2 S protein and 31.3% against N protein for the same virus; 83.6% had not been tested with an RT-PCR test for COVID-19 and 75.7% did not report symptoms related to the disease. The associated risk factors were low education, OR: 1.46, greater number of cohabitants, OR: 1.36, and contact with people infected with COVID-19, OR: 2.03.

Conclusions

The seroprevalences found suggest an important interconnectivity between rural and urban areas, where asymptomatic subjects and sociodemographic factors facilitate the virus' spread in the population.

Epidemiology of COVID-19 in Individuals under 18 Years Old in Cartagena, Colombia: An Ecological Study of the First 14 Months of the Pandemic

The epidemiology of the coronavirus disease (COVID-19) has been scarcely described in individuals under 18 years old, particularly during the first months of the pandemic. The study aimed to describe the COVID-19 epidemiology in the Colombian department of Bolívar from March 2020 to April 2021 among individuals under 18 years. Furthermore, we explored whether the use of data generated by a Bolívar reference laboratory captures the departmental epidemiology. Two information sources were used; the national COVID-19 surveillance system and the Bolívar COVID-19 reference laboratory. In using a population-based ecological approach and information from confirmed symptomatic cases, epidemic curves and heat maps were constructed to assess the COVID-19 dynamics and patterns by sex, age, and residence (Cartagena vs. 45 other municipalities). The COVID-19 incidence was comparable between males and females but varied by age group, being higher in children aged 10 years and older. Cartagena had a significantly higher number of cases and experienced early epidemic peaks. Our analyses suggest that information generated by the reference laboratory does not capture the COVID-19 departmental epidemiology, despite conducting population-based surveillance across Bolívar. The study provides a retrospective characterization of the COVID-19 epidemiology in an understudied population and information that may be useful for future evidence-based responses.

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient's X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

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.