Impact of Prioritizing Elderly and High-risk Individuals During COVID-19 Vaccine Allocation (preprint)

Background: The prioritization of vaccine eligibility for different subpopulations is an important decision to curb spread and severe outcomes of epidemic infectious diseases for which vaccines become available. Using COVID-19 as a prototype, this study evaluates the public health impact of various vaccine prioritization schemes of elderly and high-risk individuals with different vaccination start dates and rollout speeds. MethodsAn agent-based simulation model was adapted and used to project the number of infections, hospitalizations, and deaths under various vaccination start dates, rollout speeds, and prioritization schemes, including (A)no prioritization, (B)two-staged prioritization of elderly population, and (C)multi-staged prioritization of elderly and then high-risk population. The study period was February 18 th , 2020 –June 1 st , 2021, and the state of Georgia was used as a case study. ResultsThe relative effectiveness of the prioritization schemes depends on the outcome considered, as well as the vaccination start date, prioritized subpopulations, and rollout speed. Having no prioritization results in the fewest infections and hospitalizations in most scenarios; however, it yields the highest number of deaths. Prioritizing the elderly and then high-risk individuals results in the fewest overall and high-risk deaths in most scenarios. ConclusionsHaving no vaccine prioritization, i.e., opening vaccination to every adult regardless of age, simplifies vaccine rollout and reduces the infection spread but results in a higher number of deaths, especially among the elderly and high-risk populations. Prioritizing the elderly, then high-risk patients, as opposed to prioritizing only the elderly provides benefit regarding severe outcomes; however, such assumed benefits need to be counterbalanced with challenges in communicating eligibility and criteria and in distributing vaccines effectively and equitably which could result in suboptimal vaccine uptake and coverage, including among people within subpopulations at disproportionate risk of illness, hospitalization and death, of particular concern during periods of high incidence, severe disease, and death.

Impact of Stratified Interventions in University Reopenings (preprint)

More than 4,000 colleges and universities in the U.S. are scheduled to start a new semester in August or September, 2021. Many colleges require Covid-19 vaccination, as well as some combination of face coverings or diagnostic testing, while others do not (in some cases due to governance structure). Large state universities may especially have limitations and are not requiring vaccination, testing, or indoor face coverings, nor offering hybrid classes (to promote physical distancing). Group living quarters or classrooms with densely packed students are among the riskiest settings for infectious disease spread.

The Impact of COVID-19 Testing on College Campuses (preprint)

BackgroundAfter moving instruction online for more than a year, many colleges and universities are preparing to reopen and offering fully in-person classes for the Fall 2021 semester. In this paper, we study the impact of weekly testing protocols on college campuses. MethodsAn extended susceptible-infectious-removed (SIR) compartmental model was used to simulate COVID-19 spread on a college campus setting. Seven scenarios were evaluated which considered polymerase chain reaction (PCR) and rapid antigen testing kits available at various levels of supply. The infection attack rate (IAR), the number of infections, and the number of tests utilized by the end of the simulation semester are reported and compared. ResultsWeekly testing significantly reduces the number of infections compared to when testing is not available. The use of PCR tests results in the lowest infection attack rate and the total number of cases; however, using rapid antigen tests with higher coverage is more effective than using PCR tests with lower coverage. ConclusionsThe implementation of COVID-19 testing protocols should be considered and evaluated as using testing allows for identification and isolation of cases which reduces the spread of COVID-19 on college campuses. Even if testing capacity is limited, its partial implementation can be beneficial.

COVID-19 Projections for K12 Schools in Fall 2021: Significant Transmission without Interventions (preprint)

BackgroundMillions of primary school students across the United States are about to return to in-person learning. Amidst circulation of the highly infectious Delta variant, there is danger that without the appropriate safety precautions, substantial amount of school-based spread of COVID-19 may occur. MethodsWe used an extended Susceptible-Infected-Recovered computational model to estimate the number of new infections during 1 semester among a student population under different assumptions about mask usage, routine testing, and levels of incoming protection. Our analysis considers three levels of incoming protection (30%, 40%, or 50%; denoted as "low", "mid", or "high"). Universal mask usage decreases infectivity by 50%, and weekly testing may occur among 50% of the student population; positive tests prompt quarantine until recovery, with compliance contingent on symptom status. ResultsWithout masking and testing, more than 75% of susceptible students become get infected within three months in all settings. With masking, this values decreases to 50% for "low" incoming protection settings ("mid"=35%, "high"=24%). Testing half the masked population ("testing") further drops infections to 22% (16%, 13%). ConclusionWithout interventions in place, the vast majority of susceptible students will become infected through the semester. Universal masking can reduce student infections by 26-78%, and biweekly testing along with masking reduces infections by another 50%. To prevent new infections in the community, limit school absences, and maintain in-person learning, interventions such as masking and testing must be implemented widely, especially among elementary school settings in which children are not yet eligible for the vaccine.

Can Vaccine Prioritization Reduce Disparities in Covid-19 Burden for Historically Marginalized Populations? (preprint)

Importance: Nationally stated goals for distributing SARS-CoV-2 vaccines included to reduce COVID-19 mortality, morbidity, and inequity using prioritization groups. However, the impact of these prioritization strategies is not well understood, particularly their effect on health inequity in COVID-19 burden for historically marginalized racial and ethnic populations. Objective: To assess the impact of vaccination prioritization and operational strategies on disparities in COVID-19 burden among historically marginalized populations, and on mortality and morbidity by race and ethnicity. Design: We use an agent-based simulation model of North Carolina to project SARS-CoV-2 infections and COVID-19-associated deaths (mortality), hospitalizations (morbidity), and cases over 18 months (7/1/2020-12/31/2021) with vaccine distribution beginning 12/13/2020 to frontline medical and people 75+, assuming initial uptake similar to influenza vaccine. We study two-stage subsequent prioritization including essential workers (essential), adults 65+ (age), adults with high-risk health conditions, HMPs, or people in low income tracts, with eligibility for the general population in the third stage. For age-essential and essential-age strategies, we also simulated maximal uptake (100% for HMP or 100% for everyone), and we allowed for distribution to susceptible-only people. Results: Prioritizing Age then Essential had the largest impact on mortality (2.5% reduction from no prioritization); Essential then Age had the lowest morbidity and reduced infections (4.2% further than Age-Essential) without significantly impacting mortality. Under each prioritization scenario, the age-adjusted mortality burden for HMPs is higher (e.g., 33.3-34.1% higher for the Black population, 13.3%-17.0% for the Hispanic population) compared to the White population, and the gap grew under some prioritizations. In the Age-Essential strategy, the burden on HMPs decreases only when uptake is increased to 100% in HMPs. However, the Black population still had the highest mortality rate even with the Susceptible-Only distribution. Conclusions and Relevance: Simulation results show that prioritization strategies have differential impact on mortality, morbidity, and disparities overall and by race and ethnicity. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve and could worsen. Although equity was one of the tenets of vaccine distribution, the vaccination strategies publicly outlined are insufficient to remove and may exacerbate disparities between racial and ethnic groups, thus targeted strategies are needed for the future.

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data (preprint)

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods.

The Balancing Role of Distribution Speed against Varying Efficacy Levels of COVID-19 Vaccines under Variants (preprint)

ABSTRACT Mutations in SARS-CoV-2 raised concerns about diminishing vaccine effectiveness against COVID-19 caused by particular variants. Even with high initial efficacy, if a vaccine’s efficacy drops significantly against variants, or if it cannot be distributed quickly, it is uncertain whether the vaccine can provide better health outcomes than other vaccines. Hence, we evaluated the trade-offs between the speed of distribution vs. efficacy against infection of multiple vaccines when variants emerge by utilizing a Susceptible-Infected-Recovered-Deceased (SIR-D) model and assessing the level of infection attack rate (IAR). Our results show that speed is a key factor to a successful immunization strategy to control the COVID-19 pandemic even when the emerging variants may reduce the efficacy of a vaccine. Due to supply-chain challenges, the accessibility and distribution of the vaccines have been hindered in many regions, especially in low-income countries, while the second or third wave of the pandemic has occurred due to the variants. Understanding the tradeoffs between speed and efficacy and distributing vaccines that are available as quickly as possible are crucial to eradicate the pandemic before new variants spread.

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

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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse 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. f

Significance of SARS-CoV-2 Specific Antibody Testing during COVID-19 Vaccine Allocation (preprint)

Objective: To assess the value of using SARS-CoV-2 specific antibody testing to prioritize the vaccination of susceptible individuals as part of a COVID-19 vaccine distribution plan when vaccine supply is limited. Methods: A compartmental model was used to simulate COVID-19 spread when considering diagnosis, isolation, and vaccination of a cohort of 1 million individuals. The scenarios modeled represented 4 pandemic severity scenarios and various times when the vaccine becomes available during the pandemic. Eligible individuals have a probability p of receiving antibody testing prior to vaccination (p = 0, 0.25, 0.5, 0.75, and 1). The value of serology testing was evaluated by comparing the infection attack rate, peak infections, peak day, and deaths. Results: The use of antibody testing to prioritize the allocation of limited vaccines reduces infection attack rates and deaths. The size of the reduction depends on when the vaccine becomes available relative to the infection peak day. The largest reduction in cases and deaths occurs when the vaccine is deployed before and close to the infection peak day. The reduction in the number of cases and deaths diminishes as vaccine deployment is delayed and moves closer to the peak day. Conclusions: Antibody testing as part of the vaccination plan is an effective method to maximize the benefit of a COVID-19 vaccine. Decision-makers need to consider relative timing between the infection peak day and when the vaccine becomes available.

Resource Allocation for Different Types of Vaccines against COVID-19: Tradeoffs and Synergies between Efficacy and Reach (preprint)

ObjectiveVaccine shortage and supply-chain challenges have caused limited access by many resource-limited countries during the COVID-19 pandemic. One of the primary decisions for a vaccine-ordering decision-maker is how to allocate the limited resources between different types of vaccines effectively. We studied the tradeoff between efficacy and reach of the two vaccine types that become available at different times. MethodsWe extended a Susceptible-Infected-Recovered-Deceased (SIR-D) model with vaccination, ran extensive simulations with different settings, and compared the level of infection attack rate (IAR) under different reach ratios between two vaccine types under different resource allocation decisions. ResultsWe found that when there were limited resources, allocating resources to a vaccine with high efficacy that became available earlier than a vaccine with lower efficacy did not always lead to a lower IAR, particularly if the former could vaccinate less than 42.5% of the population (with the selected study parameters) who could have received the latter. Sensitivity analyses showed that this result stayed robust under different study parameters. ConclusionsOur results showed that a vaccine with lower resource requirements (wider reach) can significantly contribute to reducing IAR, even if it becomes available later in the pandemic, compared to a higher efficacy vaccine that becomes available earlier but requires more resources. Limited resource in vaccine distribution is significant challenge in many parts of the world that needs to be addressed to improve the global access to life-saving vaccines. Understanding the tradeoffs between efficacy and reach is critical for resource allocation decisions between different vaccine types for improving health outcomes.

The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation (preprint)

Background Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths. Methods An agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.5 million. Vaccine efficacy of 50% and 90% and vaccine coverage of 25%, 50%, and 75% (at the end of a 6-month distribution period) were evaluated. Six vaccination scenarios were simulated with NPIs (i.e., reduced mobility, school closings, face mask usage) maintained and removed during the period of vaccine distribution. Results In the worst-case vaccination scenario (50% efficacy and 25% coverage), 2,231,134 new SARS-CoV-2 infections occurred with NPIs removed and 799,949 infections with NPIs maintained. In contrast, in the best-case scenario (90% efficacy and 75% coverage), there were 450,575 new infections with NPIs maintained and 527,409 with NPIs removed. When NPIs were removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared to the worst-case scenario (absolute risk reduction 13% and 8%, respectively). Conclusion Simulation results suggest that premature lifting of NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared to more efficacious vaccines at lower coverage. Our findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many pre-pandemic activities can be resumed.

Homebound by COVID19: The Benefits and Consequences of Non-Pharmaceutical Intervention Strategies (preprint)

Background: . Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household). Methods: . An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. Results: . Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119K-248K, 465K-499K, 5,388K-5,389K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound. Conclusions: . Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.

High-Quality Masks Can Reduce Infections and Deaths in the US (preprint)

Objectives: To evaluate the effectiveness of widespread adoption of masks or face coverings to reduce community transmission of the SARS-CoV-2 virus that causes Covid-19. Methods: We employed an agent-based stochastic network simulation model, where Covid-19 progresses across census tracts according to a variant of SEIR. We considered a mask order that was initiated 3.5 months after the first confirmed Covid-19 case. We evaluated scenarios where wearing a mask reduces transmission and susceptibility by 50% or 80%; an individual wears a mask with a probability of 0%, 20%, 40%, 60%, 80%, or 100%. Results: If 60% of the population wears masks that are 50% effective, this decreases the cumulative infection attack rate (CAR) by 25%, the peak prevalence by 51%, and the population mortality by 25%. If 100% of people wear masks (or 60% wear masks that are 80% effective), this decreases the CAR by 38%, the peak prevalence by 67%, and the population mortality by 40%. Conclusions: After community transmission is present, masks can significantly reduce infections.

Evaluating Scenarios for School Reopening Under COVID19 (preprint)

Background: Thousands of school systems have been struggling with the decisions about how to safely and effectively deliver education during the fall semester of 2020, amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return in-person instruction) on the spread of COVID19. Methods: : An agent-based simulation model was adapted and used to project the number of infections and deaths under multiple school reopening dates and scenarios, including different cohorts receiving in-person instruction on alternating days, only younger children returning to in-person instruction, regular schedule (all students receiving in-person instruction), and school closure (all students receiving online instruction). The study period was February 18 th -November 24 th , 2020 and the state of Georgia was used as a case study. Results: : Across all scenarios, the number of COVID19-related deaths ranged from approximately 17 to 22 thousand during the study period, and on the peak day, the number of new infections ranged from 43 to 68 thousand. An alternating school day schedule performed: (i) almost as well as keeping schools closed, with the infection attack rate ranging from 38.5% to 39.8% compared to that of 37.7% under school closure; (ii) slightly better than only allowing children 10 years or younger to return to in-person instruction. Delaying the reopening of schools had a minimal impact on reducing infections and deaths under most scenarios. Conclusions: : Reopening schools following a regular schedule, i.e., all children returning to school without strict public health measures, would have serious negative public health consequences. The alternating school day schedule, especially if offered as an option to families and teachers who prefer to opt in, provides a good balance in reducing the infection spread compared to the regular schedule, while ensuring access to in-person education.

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

Background The 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. Methods Beginning 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. Results Analysis 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. Conclusions This 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.

Recombinant SARS-CoV-2 RBD molecule with a T helper epitope as a built in adjuvant induces strong neutralization antibody response (preprint)

Without approved vaccines and specific treatment, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading around the world with above 20 million COVID-19 cases and approximately 700 thousand deaths until now. An efficacious and affordable vaccine is urgently needed. The Val308 - Gly548 of Spike protein of SARS-CoV-2 linked with Gln830 - Glu843 of Tetanus toxoid (TT peptide) (designated as S1-4) and without TT peptide (designated as S1-5), and prokaryotic expression, chromatography purification and the rational renaturation of the protein were performed. The antigenicity and immunogenicity of S1-4 protein was evaluated by Western Blotting (WB) in vitro and immune responses in mice, respectively. The protective efficiency of it was measured by virus neutralization test in Vero E6 cells with SARS-CoV-2. S1-4 protein was prepared to high homogeneity and purity by prokaryotic expression and chromatography purification. Adjuvanted with Alum, S1-4 protein stimulated a strong antibody response in immunized mice and caused a major Th2-type cellular immunity compared with S1-5 protein. Furthermore, the immunized sera could protect the Vero E6 cells from SARS-CoV-2 infection with neutralization antibody GMT 256. The candidate subunit vaccine molecule could stimulate strong humoral and Th1 and Th2-type cellular immune response in mice, giving us solid evidence that S1-4 protein could be a promising subunit vaccine candidate.

Functional and druggability analysis of the SARS-CoV-2 proteome (preprint)

The infectious coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, appeared in December 2019 in Wuhan, China, and has spread worldwide. As of today, more than 22 million people have been infected, with almost 800,000 fatalities. With the purpose of contributing to the development of effective therapeutics, this work provides an overview of the viral machinery and functional role of each SARS-CoV-2 protein, and a thorough analysis of the structure and druggability assessment of the viral proteome. All structural, non-structural, and accessory proteins of SARS-CoV-2 have been studied, and whenever experimental structural data of SARS-CoV-2 proteins were not available, homology models were built based on solved SARS-CoV structures. Several potential allosteric or protein-protein interaction druggable sites on different viral targets were identified, knowledge that could be used to expand current drug discovery endeavors beyond the cysteine proteases and the polymerase complex. It is our hope that this study will support the efforts of the scientific community both in understanding the molecular determinants of this disease and in widening the repertoire of viral targets in the quest for repurposed or novel drugs against COVID-19.

Evaluating Scenarios for School Reopening under COVID19 (preprint)

Thousands of school systems have been struggling with the decisions about how to safely and effectively deliver education during the fall semester of 2020, amid the COVID19 pandemic. The objective of this study is to evaluate the public health impact of reopening schools on the spread of COVID19. An agent-based simulation model was adapted and used to project the number of infections and deaths under multiple school reopening dates and scenarios, including different cohorts receiving in-person instruction on alternating days, only younger children returning to in-person instruction, regular schedule (all students receiving in-person instruction), and school closure (all students receiving online instruction). The study period was February 18th-November 24th, 2020 and the state of Georgia was used as a case study. Across all scenarios, the number of COVID19-related deaths ranged from approximately 17 to 22 thousand during the study period, and on the peak day, the number of new infections ranged from 43 to 68 thousand. An alternating school day schedule performed: (i) almost as well as keeping schools closed, with the infection attack rate ranging from 38.5% to 39.8% compared to that of 37.7% under school closure; (ii) slightly better than only allowing children 10 years or younger to return to in-person instruction. Delaying the reopening of schools had a minimal impact on reducing infections and deaths under most scenarios.

Homebound by COVID19: The Benefits and Consequences of Non-Pharmaceutical Intervention Strategies (preprint)

ObjectivesTo evaluate the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of non-pharmaceutical interventions for COVID19 and being homebound (i.e., refraining from community/workplace interactions). MethodsAn agent-based simulation model to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in- place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. ResultsCompared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 3.43, 19.8, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 121K-268K, 522K-567K, 5,377K-5,380K, respectively. ConclusionsVoluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place. Three-question Summary BoxO_LIWhat is the current understanding of this subject? Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. C_LIO_LIWhat does this report add to the literature? Our report assessed which intervention strategies provided the best results in terms of both reducing infection outcomes (cases, deaths, etc.) and minimizing their social and economic effects (e.g., number of people homebound, providing childcare, etc.). C_LIO_LIWhat are the implications for public health practice? Voluntary quarantine proved to be the most beneficial in terms of reducing infections and deaths compared to the number of people who were homebound. C_LI

The Impact of Social Distancing on COVID19 Spread: State of Georgia Case Study (preprint)

ImportanceAs the COVID19 spread in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a "new normal." ObjectiveProject the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine. DesignWe developed an agent-based simulation model to project the infection spread. We populated the model using COVID19-specific parameters for the natural history of the disease and data from Georgia on agents interactions and demographics. SettingThe simulation study covered a six-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The outcomes are compared at the state and community levels. Main OutcomesThe number and percentage of cumulative and daily new and symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. ResultsThe combined intervention of shelter-in-place followed by voluntary quarantine reduced peak infections from 180,000 under no intervention and 120,000 under school closure, respectively, to below 80,000, and delayed the peak from April to June or later. Increasing shelter-in-place duration from four to five weeks yielded 3-14% and 4-6% decrease in cumulative infection and fatality rates, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 80,000 to 50,000, and decreased cumulative infection rate by 50%. The total number of fatalities ranged from 6,150 to 17,900 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 7 days across counties. The peak percentage is similar across rural and urban counties. Region D is estimated to have the highest COVID19-related healthcare needs with 7,357 hospital beds, 1,141 ICU beds, and 558 ventilators. Conclusions and RelevanceShelter-in-place followed by voluntary quarantine substantially reduce COVID19 infections, healthcare resource needs, and severe outcomes; delay the peak; and enable better preparedness. Time of the peak is projected to vary across locations, enabling reallocation of health system capacity. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow social distancing strategies impact the spread of COVID19? FindingsExtending shelter-in-place by one week delays the peak by about 8 days but it does not significantly reduce the peak. High compliance with voluntary quarantine following shelter-in-place reduces the peak by 40% in Georgia. MeaningThere needs to be a very strong public messaging about social distancing when shelter-in-place is lifted, to achieve a better match between healthcare capacity and demand, considering different peak times across the communities.