SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics

Abstract

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states—Rhode Island, Massachusetts, and Pennsylvania—that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.

Repeatability and timing of tropical influenza epidemics

“…The model also incorporates multiple recovery classes in order to reduce the variance in recovery time (76). Four recovery classes were chosen.…”

“…The model takes the form of a SIRS model, which includes a sinusoidal time-varying transmission parameter, 𝛽(𝑡), that incorporates timings of potential seasons (74,75). We define the model by The model also incorporates multiple recovery classes in order to reduce the variance in recovery time (76). Four recovery classes were chosen.…”

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021

“…A published bayesian inferential framework based on a dynamical epidemic model (eFigure 1 in the Supplement) was used to fit case, hospitalization, and death data from Massachusetts, Connecticut, and Rhode Island. 1,30 We collected 11 daily data streams from each state: (1) cumulative confirmed cases, (2) cumulative confirmed cases by age, (3) cumulative hospitalized cases, (4) cumulative hospitalized cases by age, (5) number of patients currently hospitalized, (6) number of patients currently in an ICU, (7) number of patients currently receiving mechanical ventilation, (8) cumulative deaths, (9) cumulative deaths by age, (10)…”

“…17,18 This means that age structure is necessary in these reporting streams, as the rate of asymptomatic SARS-CoV-2 infection, hospitalization probability, and death probability all vary substantially by age. [19][20][21] When hospitalization incidence is not available (eg, owing to underreporting 1 ), data streams for death, current hospitalization, current numbers of patients in intensive care units (ICUs) and using ventilators can be used to estimate the incidence of hospitalization.…”

Impact of Early Pandemic SARS-CoV-2 Lineages Replacement with the Variant of Concern P.1 (Gamma) in Western Bahia, Brazil

“…[ 14 , 15 , 16 ]. Understanding the dynamics of COVID-19 cases and deaths over time, and according to gender and age, is also essential to predict situations of epidemiological risk and for preparedness regarding specific groups [ 17 , 18 ]. In addition, the knowledge about viral genetic variants circulating in a given area [ 19 ], their epidemiological impact and their probable origins, are also essential for successful public health policies of containment of importing or exporting viruses.…”