Community incidence patterns drive the risk of SARS-CoV-2 outbreaks and alter intervention impacts in a high-risk institutional setting

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings. DisclaimerThe views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army, the Department of Defense, or the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study

BackgroundSARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. MethodsNine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. FindingsAbsent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. ConclusionsResults from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

The potential impact of delta variant of SARS-CoV-2 in the context of limited vaccination coverage and increasing social mixing in Bogota, Colombia

BackgroundIn Bogota by August 1st, more than 27,000 COVID-19 deaths have been reported, while complete and partial vaccination coverage reached 30% and 37%, respectively. Although reported cases are decreasing, the potential impact of new variants is uncertain. MethodsWe used an agent-based model of COVID-19 calibrated to local data. Variants and vaccination strategies were included. We estimated the impact of vaccination and modelled scenarios of early and delayed introduction of the delta variant, along with changes in mobility, social contact, and vaccine uptake over the next months. FindingsBy mid-July, vaccination may have prevented 17,800 (95% CrI: 16,000 - 19,000) deaths in Bogota. We found that delta could lead to a fourth wave of magnitude and timing dependent on social mixing, vaccination strategy, and delta dominance. In scenarios of early dominance of delta by mid-July, age prioritization and maintaining the interval between doses were important factors to avert deaths. However, if delta dominance occurred after mid-September, age prioritization would be less relevant, and the magnitude of a four wave would be smaller. In all scenarios, higher social mixing increased the magnitude of the fourth wave. Increasing vaccination rates from 50,000/day to 100,000/day reduced the impact of a fourth wave due to delta. InterpretationThe magnitude and timing of a potential fourth wave in Bogota caused by delta would depend on social mixing and the timing of dominance. Rapidly increasing vaccination coverage with non-delayed second doses could reduce the burden of a new wave. FundingNSF RAPID DEB 2027718. HERMES 50419. Medical Research Council. MR/R024855/1 Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSThe impact of vaccination strategies in the context of emerging SARS-CoV-2 variants and increasing social mixing in Colombia had not been previously evaluated through mathematical modelling. We searched PubMed for modelling studies using the terms "COVID-19 vaccine AND model AND variant AND Colombia" or "SARS-CoV-2 AND vaccine AND model AND variant AND Colombia" (From 2021/1/1 to 2021/07/31). We did not find studies addressing this question. However, we found a model describing the evolution of the epidemic in the country during the first year, and research on the emergence of alpha, gamma, and B.1.621 variants in Colombia. We extended a previous version of our SARS-CoV-2 agent-based model for Bogota to include the potential effect of vaccination and variants. This model simulates transmission of SARS-CoV-2 based on daily activity patterns of a synthetic population, representing demographic and geographic characteristics of the total population of the city. Added value of this studyFirst, our study provides a preliminary estimate of the impact of the vaccination program in Bogota in terms of the number of deaths prevented. The second major finding is the indication that due to the introduction of the delta variant in the city, and based on the current knowledge of its biology, there is a risk of a fourth epidemic wave, whose time of occurrence and magnitude would depend mainly on three factors: when delta becomes dominant, the intensity of social contact, and vaccination roll-out strategy and coverage. Implications of all the available evidenceWe estimate that by mid-July, vaccination may have already prevented 17,800 (95% CrI: 16,000 - 19,000) deaths in Bogota. The delta variant could become dominant and lead to a fourth wave later in the year, but its timing will depend on the date of introduction, social mixing patterns, and vaccination strategy. In all scenarios, higher social mixing is associated with a fourth wave of considerable magnitude. If an early delta introduction occurred (dominance by mid-July), a new wave may occur in August/September and in such case, age prioritization of vaccination and maintaining the 21-day interval between doses of the Pfizer-BioNTech BNT162b2 are more important. However, if introduction occurred one or two months later (with dominance by mid-August/September) a fourth wave would be of smaller magnitude, the age-prioritization is less relevant, but maintaining the dose scheme without postponement is more important. In all scenarios we found that increasing the vaccination rate from the current average of 50,000/day to 100,000/day reduces the impact of a potential fourth wave due to the delta variant. Our study indicates that given the possibility of a fourth wave in the city, it is necessary to continue maintaining adherence to non-pharmacological interventions, such as the use of face masks and physical distancing, to be cautious with the intensification of social activities, and that it is essential to increase the current pace of vaccinations to rapidly reach high vaccination coverage in the population of the city.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

The impacts of COVID-19 mitigation on dengue virus transmission: a modelling study

BackgroundThe COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. MethodWe used an agent-based model with a realistic treatment of human mobility and vector control to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home. ResultWe found that a lockdown in which 70% of the population sheltered at home led to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with high mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control. DiscussionOur results indicate that an unintended outcome of COVID-19 control measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA

In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50%, 75%, and 100%) and three levels of face-mask adherence in schools (50%, 75%, and 100%). Under a scenario in which schools operated remotely, we projected 45,579 (95% CrI: 14,109-132,546) infections and 790 (95% CrI: 176-1680) deaths statewide between August 24 and December 31. Reopening at 100% capacity with 50% face-mask adherence in schools resulted in a proportional increase of 42.9 (95% CrI: 41.3-44.3) and 9.2 (95% CrI: 8.9-9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50% capacity with 100% face-mask adherence, the number of infections and deaths were 22% (95% CrI: 16%-28%) and 11% (95% CrI: 5%-18%) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80-95%) and intermediate levels of face-mask adherence (40-70%) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities.

Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

BackgroundThe COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. MethodsBeginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. ResultsAnalysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. ConclusionsThis analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.

Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the US and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.