Unveiling Pandemic Patterns: A Detailed Analysis of Transmissibility and Severity Parameters Across Four COVID-19 Waves in Bogotá, Colombia (preprint)

This retrospective study on COVID-19's four waves in Bogotá, Colombia, scrutinises 1.77 million cases from March 2020 to April 2022, revealing significant shifts in both transmissibility and severity. The study highlights dynamic changes in the instantaneous reproduction number (Rt), with the highest values (> 2.5) corresponding to the ancestral and Omicron variants. There was a notable 88% decrease in the Case Fatality Ratio (CFR) from the first to the fourth wave, emphasising changing severity levels. The third wave, marked by the Mu variant, saw the highest case and death counts, yet paradoxically showed a decrease in CFR and an increase in the hospitalisation fatality ratio. Conversely, the fourth wave, dominated by Omicron, had the lowest severity despite higher hospitalisation rates in children. Additionally, the study records a consistent reduction in average hospital and ICU stay durations, from 10.84 days to 7.85 days and from 16.2 days to 12.4 days respectively, across the waves. These findings underscore the importance of ongoing epidemiological surveillance and adaptable public health strategies in lower-middle-income regions like Bogotá, deepening our understanding of COVID-19's impact in Latin America.

Understanding How Socioeconomic Inequalities Drive Inequalities In COVID-19 Infections (preprint)

Background: 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. Methods: : We use primary data from the CoVIDA project, including the results of 59,770 RT-PCR tests in Bogotá, targeted on a mostly asymptomatic adult population June 2020 to March 3rd, 2021. This is combined with administrative data that covers all reported cases in Bogotá. We estimate a number of parameters that are likely to drive inequality in COVID-19 infection rates across socioeconomic groups, then use these estimates in an individual-level branching process model of the epidemic. We use counterfactual scenarios to estimate the relative importance of different channels for explaining inequality in infection rates. Findings: 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. Interventions that mitigate transmission are found to be more effective when targeted on socioeconomically disadvantaged groups. Interpretation: Socioeconomically disadvantaged groups are particularly vulnerable to COVID-19 infections, and this appears to be primarily driven by the need to work out of home, higher transmission within home, and to some extent, the ability to isolate when needed. Policies that can successfully reduce these channels of transmission among the poor are likely to have large benefits.

Understanding How Socioeconomic Inequalities Drive Inequalities in COVID-19 Infections (preprint)

Background: 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.Methods: We use primary data from the CoVIDA project, including the results of 59,770 RT-PCR tests in Bogota´, targeted on a mostly asymptomatic adult population June 2020 to March 3rd, 2021. This is combined with administrative data that covers all reported cases in Bogota´. We estimate a number of parameters that are likely to drive inequality in COVID-19 infection rates across socioeconomic groups, then use these estimates in an individual-level branching process model of the epidemic. We use counterfactual scenarios to estimate the relative importance of different channels for explaining inequality in infection rates.Findings: 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. Interventions that mitigate transmission are found to be more effective when targeted on socioeconomically disadvantaged groups.Interpretation: Socioeconomically disadvantaged groups are particularly vulnerable to COVID-19 infections, and this appears to be driven by the need to work out of home, and higher transmission within home. Policies that can successfully reduce these channels of transmission among the poor are likely to have large benefits.Funding: The authors acknowledge generous support from the Interamerican Development Bank, the Development Bank of Latin America (CAF), the University of Los Andes and the Universidad Nacional de Colombia. Duncan Webb gratefully acknowledges support from the ED 465 at University Paris 1, and the EUR project ANR-17-EURE-0001.Declaration of Interest: The authors have no competing interests to declare.Ethical Approval: Ethics approval was obtained from the ethics committee of Universidad de los Andes (Act number 1278 of 2020). The ethics committee approved that the participants would receive the information via telephone and give their verbal consent, in order to comply with physical distancing and limit the restriction for a study is part of a public health surveillance strategy implemented jointly with the Health Secretary of Bogota.

Seroprevalence of Anti-Sars-Cov-2 Antibodies in Colombia, 2020: A Population-Based Study (preprint)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to cause very high morbidity and mortality throughout Latin American countries. However, few population-based seroprevalence surveys have been conducted to quantify attack rates and characterize drivers of transmission.Methods: We conducted a population-based cross-sectional study to assess the seroprevalence of antibodies against SARS-CoV-2 in ten cities in Colombia between September and December, 2020. The study involved multi-stage cluster sampling at each city. Participants provided a serum sample and answered a demographic and risk factor questionnaire. Prior infection by SARS-CoV-2 was ascertained using the "SARS-CoV-2 Total (COV2T) Advia Centaur - Siemens" chemiluminescence assay.Findings: A total of 17863 participants from 7075 households participated in the study. Seroprevalence varied substantially between cities, ranging from 21% (95%CI 16-25%) in Medellín to 78% (95%CI 65-91%) in Guapi. There were no differences in seroprevalence by sex, but seropositivity was lower in adults 60 years or older and higher in certain ethnic groups. There was substantial heterogeneity in seroprevalence within cities, driven to a large extent by a strong association between socio-economic stratum and seropositivity.Interpretation: Colombia has been one of the Latin American countries most affected by the COVID-19 pandemic. This study documented very high attack rates in several Colombian cities by the end of 2020 and identified key drivers of heterogeneities including ethnicity and socio-economic stratum. Few studies of seroprevalence of SARS-CoV-2 have been conducted in Latin America, and therefore this study contributes to the fundamental understanding of the pandemic in the region.

Understanding the Potential Impact of Different Drug Properties On SARS-CoV-2 Transmission and Disease Burden: A Modelling Analysis (preprint)

Background The unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods and Findings We develop a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care to explore the potential public-health impact of a range of different potential therapeutics, under a range of different scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) drug efficacy in the absence of supportive care. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. We find the impact of drugs like dexamethasone (which are delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions There is a global asymmetry in who is likely to benefit from advances in the treatment of COVID-19 to date, which have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Understanding How Socioeconomic Inequalities Drive Inequalities in SARS-CoV-2 Infections (preprint)

Across the world, the SARS-CoV-2 (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 inability 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. Interventions that mitigate transmission are often more effective when targeted on socioeconomically disadvantaged groups.

SARS-CoV-2 Spread, Detection, and Dynamics in a Megacity in Latin America (preprint)

In many developing countries, the COVID-19 pandemic has spread much faster and wider than the number of detected cases implies. By combining data from 59,770 RT-PCR tests on mostly asymptomatic individuals with administrative data on all detected cases, we capture the spread and dynamics of the COVID-19 pandemic in Bogotá from June 2020 to early March 2021. Our data provide unusually broad and detailed information on mostly asymptomatic adults in Bogotá, allowing to describe various features of the pandemic that appear to be specific to a developing country context. We find that, by the end of March 2021, slightly more than half of the population in Bogotá has been infected, despite only a small fraction of this population being detected. In July 2020, after four months of generalized quarantine that mitigated the pandemic without curving it, the initial buildup of immunity contributed to the end of the first wave. We also show that the share of the population infected by February 2021 varies widely by occupation, socio-economic stratum, and location. This, in turn, has affected the dynamics of the spread: while the first wave of infections was driven by the lowest economic strata and highly-exposed occupations, the second peak affected the population more evenly. A better understanding of the spread and dynamics of the pandemic across different groups provides valuable guidance for efficient targeting of health policy measures and restrictions.

Understanding the Potential Role of Therapeutics in Preventing Deaths Due to COVID-19: A Modelling Analysis (preprint)

Background: The unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of current and proposed treatments, and consequently research and procurement priorities, have not been clear. Methods: First, we used a model of SARS-CoV-2 transmission, COVID-19 disease and clinical care pathways to explore the potential impact of dexamethasone - the main treatment currently for hospitalised COVID-19 patients - under scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) the efficacy of dexamethasone in the absence of supportive care. We then fit the model to the observed epidemic trajectory to-date in 165 countries and analysed the potential future impact of dexamethasone in different countries, regions, and country-income strata. Finally, we constructed hypothetical profiles of novel therapeutics based on current trials, and compared the potential impact of each under different circumstances. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. Findings: We find the potential benefit dexamethasone is severely limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). However, therapeutics for different patient populations (in particular, those not in hospital and early in the course of infection) and types of benefit (in particular, reducing disease severity or infectiousness) could have much greater benefits. Such therapeutics would have particular value in resource-poor settings facing large epidemics, even if the efficacy or achievable coverage of such therapeutics is lower in comparison to other types. Interpretation: People in low-income countries will benefit the least from advances in the treatment of COVID-19 to date, which have focussed on hospitalised-patients with adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have much greater impact. Such therapeutics may be feasible and research into their efficacy and means of delivery should be a priority. Funding: None to declare. Declaration of Interest: None to declare.

Report 21: Estimating COVID-19 cases and reproduction number in Brazil (preprint)

Brazil is an epicentre for COVID-19 in Latin America. In this report we describe the Brazilian epidemic using three epidemiological measures: the number of infections, the number of deaths and the reproduction number. Our modelling framework requires sufficient death data to estimate trends, and we therefore limit our analysis to 16 states that have experienced a total of more than fifty deaths. The distribution of deaths among states is highly heterogeneous, with 5 states---Sao Paulo, Rio de Janeiro, Ceara, Pernambuco and Amazonas---accounting for 81% of deaths reported to date. In these states, we estimate that the percentage of people that have been infected with SARS-CoV-2 ranges from 3.3% (95% CI: 2.8%-3.7%) in Sao Paulo to 10.6% (95% CI: 8.8%-12.1%) in Amazonas. The reproduction number (a measure of transmission intensity) at the start of the epidemic meant that an infected individual would infect three or four others on average. Following non-pharmaceutical interventions such as school closures and decreases in population mobility, we show that the reproduction number has dropped substantially in each state. However, for all 16 states we study, we estimate with high confidence that the reproduction number remains above 1. A reproduction number above 1 means that the epidemic is not yet controlled and will continue to grow. These trends are in stark contrast to other major COVID-19 epidemics in Europe and Asia where enforced lockdowns have successfully driven the reproduction number below 1. While the Brazilian epidemic is still relatively nascent on a national scale, our results suggest that further action is needed to limit spread and prevent health system overload.

A sub-national analysis of the rate of transmission of COVID-19 in Italy (preprint)

Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28,238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. New interventions, such as enhanced testing and contact tracing are going to be introduced and will likely contribute to reductions in transmission; therefore our estimates should be viewed as pessimistic projections. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a second wave will not be immediately apparent from just monitoring of the daily number of deaths. Our results suggest that SARS-CoV-2 transmission as well as mobility should be closely monitored in the next weeks and months. To compensate for the increase in mobility that will occur due to the relaxation of the currently implemented NPIs, adherence to the recommended social distancing measures alongside enhanced community surveillance including swab testing, contact tracing and the early isolation of infections are of paramount importance to reduce the risk of resurgence in transmission.