Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study

The COVID-19 pandemic has affected millions of people around the world. In Colombia, 1.65 million cases and 43,495 deaths were reported in 2020. Schools were closed in many places around the world to slow down the spread of SARS-CoV-2. In Bogotá, Colombia, most of the public schools were closed from March 2020 until the end of the year. School closures can exacerbate poverty, particularly in low- and middle-income countries. To reconcile these two priorities in health and fighting poverty, we estimated the impact of school reopening for in-person instruction in 2021. We used an agent-based model of SARS-CoV-2 transmission calibrated to the daily number of deaths. The model includes schools that represent private and public schools in terms of age, enrollment, location, and size. We simulated school reopening at different capacities, assuming a high level of face-mask use, and evaluated the impact on the number of deaths in the city. We also evaluated the impact of reopening schools based on grade and multidimensional poverty index. We found that school at 35% capacity, assuming face-mask adherence at 75% in>8 years of age, had a small impact on the number of deaths reported in the city during a third wave. The increase in deaths was smallest when only pre-kinder was opened, and largest when secondary school was opened. At larger capacities, the impact on the number of deaths of opening pre-kinder was below 10%. In contrast, reopening other grades above 50% capacity substantially increased the number of deaths. Reopening schools based on their multidimensional poverty index resulted in a similar impact, irrespective of the level of poverty of the schools that were reopened. The impact of schools reopening was lower for pre-kinder grades and the magnitude of additional deaths associated with school reopening can be minimized by adjusting capacity in older grades.

Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study.

The COVID-19 pandemic has affected millions of people around the world. In Colombia, 1.65 million cases and 43,495 deaths were reported in 2020. Schools were closed in many places around the world to slow down the spread of SARS-CoV-2. In Bogotá, Colombia, most of the public schools were closed from March 2020 until the end of the year. School closures can exacerbate poverty, particularly in low- and middle-income countries. To reconcile these two priorities in health and fighting poverty, we estimated the impact of school reopening for in-person instruction in 2021. We used an agent-based model of SARS-CoV-2 transmission calibrated to the daily number of deaths. The model includes schools that represent private and public schools in terms of age, enrollment, location, and size. We simulated school reopening at different capacities, assuming a high level of face-mask use, and evaluated the impact on the number of deaths in the city. We also evaluated the impact of reopening schools based on grade and multidimensional poverty index. We found that school at 35% capacity, assuming face-mask adherence at 75% in>8 years of age, had a small impact on the number of deaths reported in the city during a third wave. The increase in deaths was smallest when only pre-kinder was opened, and largest when secondary school was opened. At larger capacities, the impact on the number of deaths of opening pre-kinder was below 10%. In contrast, reopening other grades above 50% capacity substantially increased the number of deaths. Reopening schools based on their multidimensional poverty index resulted in a similar impact, irrespective of the level of poverty of the schools that were reopened. The impact of schools reopening was lower for pre-kinder grades and the magnitude of additional deaths associated with school reopening can be minimized by adjusting capacity in older grades.

Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis.

BACKGROUND: The public health impact of the coronavirus disease 2019 (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: Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. RESULTS: The impact of drugs like dexamethasone (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 hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics. CONCLUSIONS: Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered 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.

Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling.

Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion. Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries. Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%. Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.

Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions.

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia.

BACKGROUND: Contact tracing is a crucial part of the public health surveillance toolkit. However, it is labor-intensive and costly to carry it out. Some countries have faced challenges implementing contact tracing, and no impact evaluations using empirical data have assessed its impact on COVID-19 mortality. This study assesses the impact of contact tracing in a middle-income country, providing data to support the expansion and optimization of contact tracing strategies to improve infection control. METHODS: We obtained publicly available data on all confirmed COVID-19 cases in Colombia between March 2 and June 16, 2020. (N = 54,931 cases over 135 days of observation). As suggested by WHO guidelines, we proxied contact tracing performance as the proportion of cases identified through contact tracing out of all cases identified. We calculated the daily proportion of cases identified through contact tracing across 37 geographical units (32 departments and five districts). Further, we used a sequential log-log fixed-effects model to estimate the 21-days, 28-days, 42-days, and 56-days lagged impact of the proportion of cases identified through contact tracing on daily COVID-19 mortality. Both the proportion of cases identified through contact tracing and the daily number of COVID-19 deaths are smoothed using 7-day moving averages. Models control for the prevalence of active cases, second-degree polynomials, and mobility indices. Robustness checks to include supply-side variables were performed. RESULTS: We found that a 10 percent increase in the proportion of cases identified through contact tracing is related to COVID-19 mortality reductions between 0.8% and 3.4%. Our models explain between 47%-70% of the variance in mortality. Results are robust to changes of specification and inclusion of supply-side variables. CONCLUSION: Contact tracing is instrumental in containing infectious diseases. Its prioritization as a surveillance strategy will substantially impact reducing deaths while minimizing the impact on the fragile economic systems of lower and middle-income countries. This study provides lessons for other LMIC.

Reduction in mobility and COVID-19 transmission.

In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.

Genomic Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2, Colombia.

Coronavirus disease (COVID-19) in Colombia was first diagnosed in a traveler arriving from Italy on February 26, 2020. However, limited data are available on the origins and number of introductions of COVID-19 into the country. We sequenced the causative agent of COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), from 43 clinical samples we collected, along with another 79 genome sequences available from Colombia. We investigated the emergence and importation routes for SARS-CoV-2 into Colombia by using epidemiologic, historical air travel, and phylogenetic observations. Our study provides evidence of multiple introductions, mostly from Europe, and documents >12 lineages. Phylogenetic findings validate the lineage diversity, support multiple importation events, and demonstrate the evolutionary relationship of epidemiologically linked transmission chains. Our results reconstruct the early evolutionary history of SARS-CoV-2 in Colombia and highlight the advantages of genome sequencing to complement COVID-19 outbreak investigations.

Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19.

We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8-6.9 days, serial interval 4.0-7.5 days, and doubling time 2.3-7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available.