key: cord-0684811-ntpt5is2 authors: Linas, Benjamin P.; Xiao, Jade; Dalgic, Ozden O.; Mueller, Peter P.; Adee, Madeline; Aaron, Alec; Ayer, Turgay; Chhatwal, Jagpreet title: Projecting COVID-19 Mortality as States Relax Nonpharmacologic Interventions date: 2022-04-01 journal: JAMA Health Forum DOI: 10.1001/jamahealthforum.2022.0760 sha: 09a10cad8a772083f4b46a670783d440526c7eeb doc_id: 684811 cord_uid: ntpt5is2 IMPORTANCE: A key question for policy makers and the public is what to expect from the COVID-19 pandemic going forward as states lift nonpharmacologic interventions (NPIs), such as indoor mask mandates, to prevent COVID-19 transmission. OBJECTIVE: To project COVID-19 deaths between March 1, 2022, and December 31, 2022, in each of the 50 US states, District of Columbia, and Puerto Rico assuming different dates of lifting of mask mandates and NPIs. DESIGN, SETTING, AND PARTICIPANTS: This simulation modeling study used the COVID-19 Policy Simulator compartmental model to project COVID-19 deaths from March 1, 2022, to December 31, 2022, using simulated populations in the 50 US states, District of Columbia, and Puerto Rico. Projected current epidemiologic trends for each state until December 31, 2022, assuming the current pace of vaccination is maintained into the future and modeling different dates of lifting NPIs. EXPOSURES: Date of lifting statewide NPI mandates as March 1, April 1, May 1, June 1, or July 1, 2022. MAIN OUTCOMES AND MEASURES: Projected COVID-19 incident deaths from March to December 2022. RESULTS: With the high transmissibility of current circulating SARS-CoV-2 variants, the simulated lifting of NPIs in March 2022 was associated with resurgences of COVID-19 deaths in nearly every state. In comparison, delaying by even 1 month to lift NPIs in April 2022 was estimated to mitigate the amplitude of the surge. For most states, however, no amount of delay was estimated to be sufficient to prevent a surge in deaths completely. The primary factor associated with recurrent epidemics in the simulation was the assumed high effective reproduction number of unmitigated viral transmission. With a lower level of transmissibility similar to those of the ancestral strains, the model estimated that most states could remove NPIs in March 2022 and likely not see recurrent surges. CONCLUSIONS AND RELEVANCE: This simulation study estimated that the SARS-CoV-2 virus would likely continue to take a major toll in the US, even as cases continued to decrease. Because of the high transmissibility of the recent Delta and Omicron variants, premature lifting of NPIs could pose a substantial threat of rebounding surges in morbidity and mortality. At the same time, continued delay in lifting NPIs may not prevent future surges. A2. Development Timeline eTable 1 lists major model updates and the dates on which they were introduced. eTable 1. Major model updates. Update February 2021 Vaccine rollout August 2021 Age-stratification to incorporate age-stratified vaccine data and differential mortality of age groups October 2021 Lower vaccine effectiveness due to Delta variant from August 1, 2021 December 2021 Waning (natural and vaccine-conferred) immunity January 2022 Lower vaccine effectiveness due to Omicron variant from December 1, 2021 Our model is an extension of the traditional susceptible-infected-recovered (SIR) model, 2 which partitions a population into compartments representing mutually exclusive disease states. At any time , the variables , , , , and denote the number of people in the susceptible, exposed, infected, recovered, and deceased compartments respectively. The flow of people between compartments is assumed to obey a system of deterministic ordinary differential equations. We let Δ 1 to be compatible with data sources reporting daily data. We stratify the population into two age groups, <65 years (low-risk) and ≥65 years (high-risk), with the subscript ∈ , . The total population in age group , denoted by , is assumed to be constant over the simulation period. To reflect administration guidelines of the Pfizer-BioNTech and Moderna vaccines, i we stratify the disease states by vaccination status. The subscript ∈ 0, 1, 2 denote the number of vaccine doses received under the recommended two-dose regime. The third vaccine, Janssen, approved for a single-dose regime, is omitted from the model due to its accounting for only 3.7% of all administered doses in the U.S. as of October 31, 2021. ii Since there is no data on vaccination status at the time of infection, we assume doses are allocated proportionally to the susceptible and recovered compartments over the historical time horizon, iii i.e., if , and , are the actual number of first and second doses administered to age group on day , the proportion of the -dose susceptible and recovered compartments, ∈ 0, 1 , moving into the corresponding 1 -dose compartments on day is , min 1, . The time lag of 12 days accounts for the delay between receiving a vaccine dose and the beginning of protection. 3 The implicit assumption is that a susceptible person does not become infected in the 12 days after receiving a dose. The vaccine reduces both susceptibility to infection and mortality risk. After vaccine doses, the probability of contracting the virus is reduced by 100 %, with 0 , 1; similarly, the infection fatality rate is reduced by 100 %, with 0 , 1. For a susceptible individual in age group who has received vaccine doses, the rate of exposure to the virus is given by where ℛ is the time-varying effective reproduction number. We model ℛ as a step function with breakpoints at the beginning of each calendar month over the historical time horizon to capture the effect of NPIs enforced during this period. The coefficients , are the elements of the contact matrix with row sums normalized to 1, so that , is the proportion of contacts per day of age group that are with age group ′. When a susceptible individual contracts the virus, they enter the exposed state and remain there for the duration of the latent period with a mean of 1/ days. After that, they transition to the infected state and remain there for the duration of the infectious period with a mean of 1/ days. Finally, the infected individual will either die with probability , 1 , where is the baseline infection fatality rate for age group , or recover with probability 1 , . An individual who has recovered from natural infection ( , ) enjoys a period of natural immunity with a mean of 1/ days before transitioning back into the susceptible state ( , ). A fully vaccinated susceptible individual ( , is protected for the duration of vaccine-conferred immunity with a mean of 1/ days before transitioning back into the partially susceptible state ( , ). Finally, since individuals with natural immunity who are subsequently vaccinated have been reported to exhibit "unusually potent immune responses", 4 a fully vaccinated recovered individual ( , ) is assumed to possess two 'layers' of immunity, shedding first their natural immunity then their vaccine-conferred immunity. It is assumed that, once vaccinated, an individual will never shed their immunity completely (within the time frame of the simulation), and a fully vaccinated individual who has shed their vaccine-conferred immunity is indistinguishable from a partially vaccinated individual. Thus, the model differentiates between the subpopulation that is willing to receive booster shots and the subpopulation that is unwilling to be vaccinated. Fully vaccinated individuals wane into the partially vaccinated state and are 'boosted' back into the fully vaccinated state. In summary, our model is described by the following system of equations, where has been dropped for notational simplicity: We calibrate the model to historical daily incident deaths. The system of ordinary differential equations is solved numerically using Euler's method (R package deSolve). 16 The calibration method is generalized simulated annealing (R package GenSA) with the sum of squared errors as the objective function. 17 We make forecasts by allowing the model to continue running past the historical time horizon. Diagnosed cases and hospital and ICU occupancy are not accounted for in the SEIR model. We estimate these in a post-processing step as follows. We assume the future diagnosis rate remains at the latest estimated value, i.e., the number of incident diagnosed cases on the last day of data divided by the number of incident total cases on the last day of data. We back-calculate hospital and ICU bed occupancy from incident deaths assuming an average time to death from hospital and ICU admission of 16 and 10 days respectively. 20 Starting in August, we forecast occupancy data provided by the U.S. Department of Health and Human Services. Note that the data does not include all hospitals in any given state so our forecasts do not estimate the total demand for hospital and ICU beds. 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