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

Abstract

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% − 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% − 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% − 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group. We make several suggestions for enhancements to current data collection practices that could improve response efforts in winter.

Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts

“…We use a mathematical model developed by Wikle et al [ 17 ] and fit to Rhode Island and Massachusetts data so that vaccination campaigns can be evaluated in the context of the current number of individuals already infected. As of Nov 30, total estimated attack rates are 20.7%(95%CI : 17.3%−24.1%) in Rhode Island and 12.5% (95% CI : 11.5% − 13.5%) in Massachusetts [ 21 ].…”

“…Individuals can be vaccinated if they are in the susceptible class. Individuals who experienced asymptomatic infection and are in the recovered class can also be vaccinated, according to whether they were likely to have been asymptomatic or not [ 26 ] and confirmed PCR-positive or not (reporting parameter ρ in Wikle et al [ 17 ]). Individuals in the exposed class E or the asymptomatic class A can be chosen for vaccination but we assume that they progress through their normal course of disease with no effect of the vaccine, as vaccination will have occurred for these individuals at a time when their immune system has already been exposed to whole live virus SARS-CoV-2.…”

“… We extend the model from [ 17 ] to include a 24-stage vaccinated compartment, denoted by Z . Other compartments are susceptible ( S ), exposed ( E ), asymptomatic ( A ), infected and symptomatic but not hospitalized ( I ), hospitalized in acute stage of infection ( H A ), in critical care (acute stage) ( C A ), on mechanical ventilation ( V ), in critical care recovering phase ( C R ), hospitalized and recovering ( H R ), recovered ( R ), recovered from hospitalization ( R HOSP ).…”

“…Vaccinating populations with real-time information on seroprevalence allows a public health system to introduce efficiencies into vaccine allocation. Here, we evaluate different age-distributions for vaccine roll-out given current age-stratified attack-rate estimates in Rhode Island and Massachusetts [ 17 ]. While we assume that confirmed seropositive individuals (i.e.…”

“…Using a mathematical model whose fit to Rhode Island and Massachusetts COVID-19 data was described in Wikle et al [ 17 ], we consider seven different vaccine efficacy profiles/halflives (currently an unknown), we evaluate the individual importance of each 10-year age band to vaccination outcomes, we compare several common age allocations under assumptions of high and low vaccine supply, and we evaluate the magnitude of population-level effects if vaccination is dependent on serostatus. We assume that health care workers and front-line medical staff are vaccinated first, as our model is not able to evaluate the effects of a stressed and understaffed health system.…”