Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021

Borchering, Rebecca K.; Viboud, Cécile; Howerton, Emily; Smith, Claire P; Truelove, Shaun; Runge, Michael C.; Reich, Nicholas G.; Contamin, Lucie; Levander, John; Salerno, Jessica; Panhuis, Wilbert van; Kinsey, Matt; Tallaksen, Kate; Obrecht, Richard; Asher, Laura; Costello, Cash; Kelbaugh, Michael; Wilson, Shelby; Shin, Lauren; Gallagher, Molly; Mullany, Luke C.; Rainwater-Lovett, Kaitlin; Lemaitre, Joseph C.; Dent, Juan; Grantz, Kyra H.; Kaminsky, Joshua; Lauer, Stephen A.; Lee, Elizabeth C.; Meredith, Hannah R.; Perez-Saez, Javier; Keegan, Lindsay T; Karlen, D.; Chinazzi, Matteo; Davis, Jessica T.; Mu, Kunpeng; Xiong, Xinyue; Piontti, Ana Pastore y; Vespignani, Alessandro; Srivastava, Ajitesh; Porebski, P.J.; Venkatramanan, Srinivasan; Adiga, Aniruddha; Lewis, Bryan; Klahn, Brian; Outten, Joseph; Schlitt, James; Corbett, Patrick; Telionis, Pyrros A.; Wang, Lijing; Peddireddy, Akhil Sai; Hurt, Benjamin; Chen, Jiangzhuo; Vullikanti, Anil; Marathe, Madhav V.; Healy, Jessica M.; Slayton, Rachel B.; Biggerstaff, Matthew; Johansson, Michael A.; Shea, Katriona; Lessler, Justin

doi:10.15585/mmwr.mm7019e3

Cited by 135 publications

(68 citation statements)

References 4 publications

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“…If the current rate of weekly vaccine initiation continues through August, coverage among young adults will not reach the coverage level of older adults. High vaccination coverage among all age groups is important for decreasing COVID-19 cases, hospitalizations, and deaths ( 2 , 3 ), especially among groups with lower vaccination uptake, such as young adults ( 4 , 5 ).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Vaccination Coverage Among Adults — United States, December 14, 2020–May 22, 2021

Diesel¹,

Sterrett²,

Dasgupta³

et al. 2021

MMWR Morb. Mortal. Wkly. Rep.

140

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Coverage was evaluated by selected community-level characteristics matched to vaccine recipients' county of residence. § § § County-level rankings of social vulnerability from the 2018 CDC Social Vulnerability Index (SVI), which is used to identify community needs during emergencies, were categorized into quartiles based on distribution among all U.S. counties. ¶ ¶ ¶ County-level data on Social Determinants of Health**** obtained from the American Community Survey † † † † were dichotomized based on the median of all U.S. counties. § § § § County-level urbanicity was based on the 2013 National Center for Health Statistics urban-rural classification scheme. ¶ ¶ ¶ ¶ Generalized estimating equation models with binomial regression and an identity link were used to † † † Periods are based on eligibility and other process factors (e.g., phase of vaccine rollout, eligible population, supply, and programs and policy enacted) important in framing the specific needs and constraints at that time. Period 1 represented when most states opened eligibility to health care workers, residents in long-term care facilities, and older adults while there was a highly constrained supply, which overlapped phase 1a, and a portion of phase 1b (https://www.cdc.gov/mmwr/volumes/69/wr/ mm695152e2.htm). Period 2 represented when states were expanding eligibility inconsistently, and supply was becoming more available, which overlapped with phases 1b and 1c. Period 3 represented when all states expanded eligibility to all adults while supply was steady and increased, which overlapped with phases 1c and 2. § § § The following jurisdictions were excluded from all county-level analyses (National Center for Health Statistics urban-rural, SVI, and Social Determinants of Health) due to lack of county-level vaccination data: all counties in Hawaii and eight counties in California for which total population was <20,000. Among all first doses analyzed during December 14, 2020-May 22, 2021, 5.9% were missing county data and were therefore excluded from models. ¶ ¶ ¶ Fifteen elements categorized into four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation) are included in SVI (https:// www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/ SVI2018Documentation-H.pdf ). Overall SVI includes all 15 indicators as a composite measure (https://www.atsdr.cdc.gov/placeandhealth/svi/ fact_sheet/fact_sheet.html). One county in New Mexico was excluded because SVI ranking could not be calculated (https://www.atsdr.cdc.gov/ placeandhealth/svi/index.html). **** Measures of Social Determinants of Health from the American Community Survey: percentage of the total population 1) unemployed, 2) uninsured, 3) that earned an income below the federal poverty level, 4) without a computer (e.g., desktop or laptop computer [excludes mobile phones]), 5) with a computer but without Internet access, and 6) identifying as a racial/ethnic group other than non-Hispanic White (https://healt...

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Section: Discussionmentioning

confidence: 99%

COVID-19 Vaccination Coverage Among Adults — United States, December 14, 2020–May 22, 2021

Diesel¹,

Sterrett²,

Dasgupta³

et al. 2021

MMWR Morb. Mortal. Wkly. Rep.

140

View full text Add to dashboard Cite

show abstract

“…As of June 18, 2021, there have been more than 177 million cases of COVID-19 worldwide, including 33 million in the United States, resulting in more than 3.8 million deaths globally and 600,000 deaths in the United States ( 1 ). Despite ongoing vaccination efforts, COVID-19 continues to spread around the world, driven by emergent variant strains and relaxation of prevention/mitigation strategies ( 2 , 3 ). With >99% of patients surviving the acute infectious period ( 4 ) and data on the long-term sequelae of COVID-19 disease beginning to emerge, there is an urgent need to better understand the lasting effects of COVID-19 on survivors ( 5 ).…”

mentioning

confidence: 99%

Postacute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection

Jiang

Roy

et al. 2021

JACC: Basic to Translational Science

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“…Given the similarity between our model and others developed concurrently (e.g. [91]), we recommend developing infrastructure to facilitate collaboration, rapid communication, and workflows to minimize duplication of effort, facilitate troubleshooting, and aggregate and analyse projections across sets of models [101]. Further, we identified human behavioural changes as a key source of inaccuracy in our model predictions, suggesting the importance of collaboration between disease modellers and behavioural scientists, as well as guidelines for proper incorporation of mobility data [88].…”

Section: (D) Early Modelling and Lessons For Future Epidemic Controlmentioning

confidence: 99%

The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

et al. 2021

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Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March–June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ( R 0 ) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ( R E ) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

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Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021

Cited by 135 publications

References 4 publications

COVID-19 Vaccination Coverage Among Adults — United States, December 14, 2020–May 22, 2021

COVID-19 Vaccination Coverage Among Adults — United States, December 14, 2020–May 22, 2021

Postacute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection

The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

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