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1.
Am J Epidemiol ; 191(5): 900-907, 2022 Mar 24.
Article in English | MEDLINE | ID: covidwho-1830972

ABSTRACT

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission continues to evolve, understanding the contribution of location-specific variations in nonpharmaceutical interventions and behaviors to disease transmission during the initial epidemic wave will be key for future control strategies. We offer a rigorous statistical analysis of the relative effectiveness of the timing of both official stay-at-home orders and population mobility reductions during the initial stage of the US coronavirus disease 2019 (COVID-19) epidemic. We used a Bayesian hierarchical regression to fit county-level mortality data from the first case on January 21, 2020, through April 20, 2020, and quantify associations between the timing of stay-at-home orders and population mobility with epidemic control. We found that among 882 counties with an early local epidemic, a 10-day delay in the enactment of stay-at-home orders would have been associated with 14,700 additional deaths by April 20 (95% credible interval: 9,100, 21,500), whereas shifting orders 10 days earlier would have been associated with nearly 15,700 fewer lives lost (95% credible interval: 11,350, 18,950). Analogous estimates are available for reductions in mobility-which typically occurred before stay-at-home orders-and are also stratified by county urbanicity, showing significant heterogeneity. Results underscore the importance of timely policy and behavioral action for early-stage epidemic control.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/prevention & control , Humans , SARS-CoV-2
2.
Proc Natl Acad Sci U S A ; 119(15): e2113561119, 2022 04 12.
Article in English | MEDLINE | ID: covidwho-1784075

ABSTRACT

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 multimodel ensemble forecast that combined predictions from dozens of 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 naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk 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.


Subject(s)
COVID-19 , COVID-19/mortality , Data Accuracy , Forecasting , Humans , Pandemics , Probability , Public Health/trends , United States/epidemiology
3.
Med Decis Making ; 41(1): 3-8, 2021 01.
Article in English | MEDLINE | ID: covidwho-1741762

ABSTRACT

Widespread, convenient access to COVID-19 testing has been challenging in the United States. We make a case for provisioning COVID-19 tests through the United States Postal Service (USPS) facilities and demonstrate a simple method for selecting locations to improve access. We provide quantitative evidence that even a subset of USPS facilities could provide broad access, particularly in remote and at-risk communities with limited access to health care. Based on daily travel surveys, census data, locations of USPS facilities, and an established care-seeking model, we estimate that more than 94% of the US population would be willing to travel to an existing USPS facility if warranted. For half of the US population, this would require traveling less than 2.5 miles from home; for 90%, the distance would be less than 7 miles. In Georgia, Illinois, and Minnesota, we estimate that testing at USPS facilities would provide access to an additional 4.1, 3.1, and 1.3 million people and reduce the median travel distance by 3.0, 0.8, and 1.2 miles, respectively, compared with existing testing sites per 28 July 2020. We also discuss the option of distributing test-at-home kits via USPS instead of private carriers. Finally, our proposal provides USPS an opportunity to increase revenues and expand its mission, thus improving its future prospects and relevance.


Subject(s)
COVID-19 Testing , Postal Service/organization & administration , COVID-19/diagnosis , Health Services Accessibility , Humans , Rural Population , SARS-CoV-2 , United States
4.
MDM Policy Pract ; 7(1): 23814683221084631, 2022.
Article in English | MEDLINE | ID: covidwho-1741907

ABSTRACT

Background. In mid-2020, there was significant concern that the overlapping 2020-2021 influenza season and COVID-19 pandemic would overwhelm already stressed health care systems in the Northern Hemisphere, particularly if influenza immunization rates were low. Methods. Using a mathematical susceptible-exposed-infected-recovered (SEIR) compartmental model incorporating the age-specific viral transmission rates and disease severity of Austin, Texas, a large metropolitan region, we projected the incidence and health care burden for both COVID-19 and influenza across observed levels of SARS-CoV-2 transmission and influenza immunization rates for the 2020-2021 season. We then retrospectively compared scenario projections made in August 2020 with observed trends through June 2021. Results. Across all scenarios, we projected that the COVID-19 burden would dwarf that of influenza. In all but our lowest transmission scenarios, intensive care units were overwhelmed by COVID-19 patients, with the levels of influenza immunization having little impact on health care capacity needs. Consistent with our projections, sustained nonpharmaceutical interventions (NPIs) in Austin prevented COVID-19 from overwhelming health care systems and almost completely suppressed influenza during the 2020-2021 respiratory virus season. Limitations. The model assumed no cross-immunity between SARS-CoV-2 and influenza, which might reduce the burden or slow the transmission of 1 or both viruses. Conclusion. Before the widespread rollout of the SARS-CoV-2 vaccine, COVID-19 was projected to cause an order of magnitude more hospitalizations than seasonal influenza because of its higher transmissibility and severity. Consistent with predictions assuming strong NPIs, COVID-19 strained but did not overwhelm local health care systems in Austin, while the influenza burden was negligible. Implications. Nonspecific NPI efforts can dramatically reduce seasonal influenza burden and preserve health care capacity during respiratory virus season. Highlights: As the COVID-19 pandemic threatened lives worldwide, the Northern Hemisphere braced for a potential "twindemic" of seasonal influenza and COVID-19.Using a validated mathematical model of influenza and SARS-CoV-2 co-circulation in a large US city, we projected the impact of COVID-19-driven nonpharmaceutical interventions combined with influenza vaccination on health care capacity during the 2020-2021 respiratory virus season.We describe analyses conducted during summer 2020 to help US cities prepare for the 2020-2021 influenza season and provide a retrospective evaluation of the initial projections.

5.
Cramer, Estee, Ray, Evan, Lopez, Velma, Bracher, Johannes, Brennen, Andrea, Castro§Rivadeneira, Alvaro, Gerding, Aaron, Gneiting, Tilmann, House, Katie, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James, Tec, Mauricio, Srivastava, Ajitesh, George, Glover, Cegan, Jeffrey, Dettwiller, Ian, England, William, Farthing, Matthew, Hunter, Robert, Lafferty, Brandon, Linkov, Igor, Mayo, Michael, Parno, Matthew, Rowland, Michael, Trump, Benjamin, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen, Hess, Jonathan, Morley, Christopher, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina, Baer, Thomas, Eisenberg, Marisa, Falb, Karl, Huang, Yitao, Martin, Emily, McCauley, Ella, Myers, Robert, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V. P.; Guertin, Stephanie, Hulme-Lowe, Christopher, Turner, Stephen, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van§de§Walle, Axel, Turtle, James, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Koyluoglu, Ugur, DesRoches, David, Forli, Pedro, Hamory, Bruce, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Kraus, David, Kraus, Andrea, Gao, Zhifeng, Bian, Jiang, Cao, Wei, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Vespignani, Alessandro, Chinazzi, Matteo, Davis, Jessica, Mu, Kunpeng, y§Piontti, Ana Pastore, Xiong, Xinyue, Zheng, Andrew, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Nze-Ndong, David, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Sarker, Arnab, Jadbabaie, Ali, Shah, Devavrat, Penna, Nicolas Della, Celi, Leo, Sundar, Saketh, Wolfinger, Russ, Osthus, Dave, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Karlen, Dean, Kinsey, Matt, Mullany, Luke, Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Lee, Elizabeth, Dent, Juan, Grantz, Kyra, Hill, Alison, Kaminsky, Joshua, Kaminsky, Kathryn, Keegan, Lindsay, Lauer, Stephen, Lemaitre, Joseph, Lessler, Justin, Meredith, Hannah, Perez-Saez, Javier, Shah, Sam, Smith, Claire, Truelove, Shaun, Wills, Josh, Marshall, Maximilian, Gardner, Lauren, Nixon, Kristen, Burant, John, Wang, Lily, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Guannan, Wang, Yueying, Yu, Shan, Reiner, Robert, Barber, Ryan, Gakidou, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris J. L.; Pigott, David, Gurung, Heidi, Baccam, Prasith, Stage, Steven, Suchoski, Bradley, Prakash, Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Keskinocak, Pinar, Asplund, John, Baxter, Arden, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan, Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long, Li, Chun-Liang, Pfister, Tomas, Sava, Dario, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Abbott, Sam, Bosse, Nikos, Funk, Sebastian, Hellewell, Joel, Meakin, Sophie, Sherratt, Katharine, Zhou, Mingyuan, Kalantari, Rahi, Yamana, Teresa, Pei, Sen, Shaman, Jeffrey, Li, Michael, Bertsimas, Dimitris, Lami, Omar Skali, Soni, Saksham, Bouardi, Hamza Tazi, Ayer, Turgay, Adee, Madeline.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-329225

ABSTRACT

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 naïve 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 Statement This 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.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309733

ABSTRACT

Background: To mitigate the coronavirus pandemic that emerged in 2019 (COVID-19), countries worldwide have enacted unprecedented movement restrictions, social distancing measures, and face mask requirements. Until safe and efficacious vaccines or antiviral drugs become widely available, viral testing remains the primary mitigation measure for rapid identification and isolation of infected cases. Methods: We evaluate the economic tradeoffs of expanding and accelerating SARS-CoV-2 testing using a multi-scale model that incorporates SARS-CoV-2 transmission at the population level and daily viral load dynamics at the individual level. Findings: Assuming a willingness-to-pay of $100,000 per year of life lost (YLL) and a price of $5 per test, the strategy most likely to be cost-effective under a rapid transmission scenario (Re > 2) is daily testing followed by a one-week rather than two-week isolation period subsequent to test confirmation. Under lower transmission scenarios, weekly testing of the population is expected to be more cost effective. Expanded surveillance testing is expected to be cost effective if the price per test is less than $400 across all transmission rates considered. Interpretation: Extensive expansion of testing coupled with isolation of confirmed cases is essential for mitigating the COVID-19 pandemic. Further, resources recouped from shortened isolation duration could be cost-effectively allocated to more frequent testing.Funding Statement: US National Institutes of Health and US Centers for Disease Control and Prevention.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Not applicable.

7.
Proc Natl Acad Sci U S A ; 119(7)2022 02 15.
Article in English | MEDLINE | ID: covidwho-1671750

ABSTRACT

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.


Subject(s)
COVID-19/epidemiology , Hospitals , Pandemics , SARS-CoV-2 , Delivery of Health Care , Forecasting , Hospitalization/statistics & numerical data , Humans , Public Health , Retrospective Studies , United States
8.
Lancet Reg Health Am ; 8: 100182, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1620909

ABSTRACT

BACKGROUND: As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness. METHODS: Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized. FINDINGS: Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario (Re ∼ 2.5) is daily testing until one third of the population is immunized and then weekly testing until half the population is immunized, combined with a 10-day isolation period of positive cases and their households. Under a low transmission scenario (Re ∼ 1.2), the optimal sequence is weekly testing until the population reaches 10% partial immunity, followed by monthly testing until 20% partial immunity, and no testing thereafter. INTERPRETATION: Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants. FUNDING: US National Institutes of Health, US Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council, and EPSRC Impact Acceleration Grant.

9.
Clin Infect Dis ; 73(12): 2257-2264, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1596073

ABSTRACT

BACKGROUND: Global vaccine development efforts have been accelerated in response to the devastating coronavirus disease 2019 (COVID-19) pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States. METHODS: We developed an agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, whereas children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection and specified 10% preexisting population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current nonpharmaceutical interventions in the United States. RESULTS: Vaccination reduced the overall attack rate to 4.6% (95% credible interval [CrI]: 4.3%-5.0%) from 9.0% (95% CrI: 8.4%-9.4%) without vaccination, over 300 days. The highest relative reduction (54%-62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-intensive care unit (ICU) hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3%-66.7%), 65.6% (95% CrI: 62.2%-68.6%), and 69.3% (95% CrI: 65.5%-73.1%), respectively, across the same period. CONCLUSIONS: Our results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with nonpharmaceutical interventions is essential to achieve this impact.


Subject(s)
COVID-19 , Adolescent , COVID-19 Vaccines , Child , Disease Outbreaks/prevention & control , Humans , SARS-CoV-2 , United States/epidemiology , Vaccination
10.
Emerg Infect Dis ; 27(12): 3188-3190, 2021 12.
Article in English | MEDLINE | ID: covidwho-1496964

ABSTRACT

We used the incidence of spike gene target failures identified during PCR testing to provide an early projection of the prevalence of severe acute respiratory syndrome coronavirus 2 variant B.1.1.7 in a university setting in Texas, USA, before sequencing results were available. Findings from a more recent evaluation validated those early projections.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Texas/epidemiology , Universities
11.
Ann Intern Med ; 174(11): 1586-1591, 2021 11.
Article in English | MEDLINE | ID: covidwho-1405523

ABSTRACT

BACKGROUND: As of 28 July 2021, 60% of adults in the United States had been fully vaccinated against COVID-19, and more than 34 million cases had been reported. Given the uncertainty regarding undocumented infections, the population level of immunity against COVID-19 in the United States remains undetermined. OBJECTIVE: To estimate the population immunity, defined as the proportion of the population that is protected against SARS-CoV-2 infection due to prior infection or vaccination. DESIGN: Statistical and simulation modeling to estimate overall and age-specific population immunity. SETTING: United States. PARTICIPANTS: Simulated age-stratified population representing U.S. demographic characteristics. MEASUREMENTS: The true number of SARS-CoV-2 infections in the United States was inferred from data on reported deaths using age-specific infection-fatality rates (IFRs). Taking into account the estimates for vaccine effectiveness and protection against reinfection, the overall population immunity was determined as the sum of protection levels in vaccinated persons and those who were previously infected but not vaccinated. RESULTS: Using age-specific IFR estimates from the Centers for Disease Control and Prevention, it was estimated that as of 15 July 2021, 114.9 (95% credible interval [CrI], 103.2 to 127.4) million persons had been infected with SARS-CoV-2 in the United States. The mean overall population immunity was 62.0% (CrI, 58.4% to 66.4%). Adults aged 65 years or older were estimated to have the highest immunity level (77.2% [CrI, 76.2% to 78.6%]), and children younger than 12 years had the lowest immunity level (17.9% [CrI, 14.4% to 21.9%]). LIMITATION: Publicly reported deaths may underrepresent actual deaths. CONCLUSION: As of 15 July 2021, the U.S. population immunity against COVID-19 may still have been insufficient to contain the outbreaks and safely revert to prepandemic social behavior. PRIMARY FUNDING SOURCE: National Science Foundation, National Institutes of Health, Notsew Orm Sands Foundation, Canadian Institutes of Health Research, and Natural Sciences and Engineering Research Council of Canada.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/immunology , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/prevention & control , Child , Child, Preschool , Female , Humans , Immunity, Herd , Infant , Male , Middle Aged , Pandemics , SARS-CoV-2 , United States/epidemiology
12.
Emerg Infect Dis ; 27(7): 1976-1979, 2021 07.
Article in English | MEDLINE | ID: covidwho-1278362

ABSTRACT

During rollout of coronavirus disease vaccination, policymakers have faced critical trade-offs. Using a mathematical model of transmission, we found that timing of vaccination rollout would be expected to have a substantially greater effect on mortality rate than risk-based prioritization and uptake and that prioritizing first doses over second doses may be lifesaving.


Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , Models, Theoretical , SARS-CoV-2 , United States/epidemiology , Vaccination
13.
Nat Commun ; 12(1): 3767, 2021 06 18.
Article in English | MEDLINE | ID: covidwho-1275921

ABSTRACT

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.


Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Hospitalization/statistics & numerical data , COVID-19/transmission , COVID-19/virology , Computer Simulation , Delivery of Health Care/methods , Delivery of Health Care/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Intensive Care Units/supply & distribution , Quarantine/methods , SARS-CoV-2/isolation & purification , Texas/epidemiology
14.
PLoS One ; 16(5): e0251153, 2021.
Article in English | MEDLINE | ID: covidwho-1225810

ABSTRACT

As COVID-19 spreads across the United States, people experiencing homelessness (PEH) are among the most vulnerable to the virus. To mitigate transmission, municipal governments are procuring isolation facilities for PEH to utilize following possible exposure to the virus. Here we describe the framework for anticipating isolation bed demand in PEH communities that we developed to support public health planning in Austin, Texas during March 2020. Using a mathematical model of COVID-19 transmission, we projected that, under no social distancing orders, a maximum of 299 (95% Confidence Interval: 223, 321) PEH may require isolation rooms in the same week. Based on these analyses, Austin Public Health finalized a lease agreement for 205 isolation rooms on March 27th 2020. As of October 7th 2020, a maximum of 130 rooms have been used on a single day, and a total of 602 PEH have used the facility. As a general rule of thumb, we expect the peak proportion of the PEH population that will require isolation to be roughly triple the projected peak daily incidence in the city. This framework can guide the provisioning of COVID-19 isolation and post-acute care facilities for high risk communities throughout the United States.


Subject(s)
COVID-19/transmission , Forecasting/methods , Patient Isolators/supply & distribution , COVID-19/epidemiology , Homeless Persons/statistics & numerical data , Humans , Models, Theoretical , Patient Isolation/instrumentation , Patient Isolation/trends , Public Health , SARS-CoV-2/pathogenicity , United States
15.
Emerg Infect Dis ; 27(5): 1527-1529, 2021 05.
Article in English | MEDLINE | ID: covidwho-1148279

ABSTRACT

A fast-spreading severe acute respiratory syndrome coronavirus 2 variant identified in the United Kingdom in December 2020 has raised international alarm. We analyzed data from 15 countries and estimated that the chance that this variant was imported into these countries by travelers from the United Kingdom by December 7 is >50%.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , United Kingdom/epidemiology
16.
Science ; 371(6533)2021 03 05.
Article in English | MEDLINE | ID: covidwho-1119274

ABSTRACT

Spread of contagious pathogens critically depends on the number and types of contacts between infectious and susceptible hosts. Changes in social behavior by susceptible, exposed, or sick individuals thus have far-reaching downstream consequences for infectious disease spread. Although "social distancing" is now an all too familiar strategy for managing COVID-19, nonhuman animals also exhibit pathogen-induced changes in social interactions. Here, we synthesize the effects of infectious pathogens on social interactions in animals (including humans), review what is known about underlying mechanisms, and consider implications for evolution and epidemiology.


Subject(s)
Communicable Diseases/transmission , Host-Pathogen Interactions , Physical Distancing , Social Behavior , Animals , Biological Evolution , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Humans , Risk
17.
Lancet Public Health ; 6(3): e184-e191, 2021 03.
Article in English | MEDLINE | ID: covidwho-1065700

ABSTRACT

BACKGROUND: To mitigate the COVID-19 pandemic, countries worldwide have enacted unprecedented movement restrictions, physical distancing measures, and face mask requirements. Until safe and efficacious vaccines or antiviral drugs become widely available, viral testing remains the primary mitigation measure for rapid identification and isolation of infected individuals. We aimed to assess the economic trade-offs of expanding and accelerating testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the USA in different transmission scenarios. METHODS: We used a multiscale model that incorporates SARS-CoV-2 transmission at the population level and daily viral load dynamics at the individual level to assess eight surveillance testing strategies that varied by testing frequency (from daily to monthly testing) and isolation period (1 or 2 weeks), compared with the status-quo strategy of symptom-based testing and isolation. For each testing strategy, we first estimated the costs (incorporating costs of diagnostic testing and admissions to hospital, and salary lost while in isolation) and years of life lost (YLLs) prevented under rapid and low transmission scenarios. We then assessed the testing strategies across a range of scenarios, each defined by effective reproduction number (Re), willingness to pay per YLL averted, and cost of a test, to estimate the probability that a particular strategy had the greatest net benefit. Additionally, for a range of transmission scenarios (Re from 1·1 to 3), we estimated a threshold test price at which the status-quo strategy outperforms all testing strategies considered. FINDINGS: Our modelling showed that daily testing combined with a 2-week isolation period was the most costly strategy considered, reflecting increased costs with greater test frequency and length of isolation period. Assuming a societal willingness to pay of US$100 000 per YLL averted and a price of $5 per test, the strategy most likely to be cost-effective under a rapid transmission scenario (Re of 2·2) is weekly testing followed by a 2-week isolation period subsequent to a positive test result. Under low transmission scenarios (Re of 1·2), monthly testing of the population followed by 1-week isolation rather than 2-week isolation is likely to be most cost-effective. Expanded surveillance testing is more likely to be cost-effective than the status-quo testing strategy if the price per test is less than $75 across all transmission rates considered. INTERPRETATION: Extensive expansion of SARS-CoV-2 testing programmes with more frequent and rapid tests across communities coupled with isolation of individuals with confirmed infection is essential for mitigating the COVID-19 pandemic. Furthermore, resources recouped from shortened isolation duration could be cost-effectively allocated to more frequent testing. FUNDING: US National Institutes of Health, US Centers for Disease Control and Prevention, and Love, Tito's.


Subject(s)
COVID-19 Testing/economics , COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/epidemiology , Cost-Benefit Analysis , Humans , Models, Theoretical , United States/epidemiology
18.
medRxiv ; 2020 Dec 24.
Article in English | MEDLINE | ID: covidwho-955704

ABSTRACT

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France's ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

19.
medRxiv ; 2021 Jan 02.
Article in English | MEDLINE | ID: covidwho-955700

ABSTRACT

BACKGROUND: Global vaccine development efforts have been accelerated in response to the devastating COVID-19 pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States (US). METHODS: We developed an agent-based model of SARS-CoV-2 transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, while children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection, and specified 10% pre-existing population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current non-pharmaceutical interventions in the US. RESULTS: Vaccination reduced the overall attack rate to 4.6% (95% CrI: 4.3% - 5.0%) from 9.0% (95% CrI: 8.4% - 9.4%) without vaccination, over 300 days. The highest relative reduction (54-62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-ICU hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3% - 66.7%), 65.6% (95% CrI: 62.2% - 68.6%), and 69.3% (95% CrI: 65.5% - 73.1%), respectively, across the same period. CONCLUSIONS: Our results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with non-pharmaceutical interventions is essential to achieve this impact.

20.
Non-conventional | Homeland Security Digital Library, Grey literature | ID: grc-740381
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