Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: results of a Bayesian evidence synthesis model

Chitwood, Melanie H.; Russi, Marcus; Gunasekera, Kenneth S.; Havumaki, Joshua; Klaassen, Fayette; Pitzer, Virginia E.; Salomon, Joshua A.; Swartwood, Nicole A; Warren, Joshua L.; Cohen, Ted; Menzies, Nicolas A

doi:10.1101/2020.06.17.20133983

Cited by 36 publications

(43 citation statements)

References 40 publications

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“…By comparison, the 2-step approach of first transforming the observed time series and then calculating R t requires users to propagate uncertainty from the back-calculation step into the R t estimation step. A final advantage of models that include forward delays to observation is that they could facilitate inference from multiple populations or data streams simultaneously [ 52 , 53 , 64 ]. For example, by assuming that cases, hospitalizations, and deaths all arise from a common infection process, these methods might be able to infer the incident time series of infections more accurately and precisely, potentially while also estimating delays and changes in ascertainment for specific data sources (e.g., outpatient cases).…”

Section: Adjusting For Delaysmentioning

confidence: 99%

Practical considerations for measuring the effective reproductive number, Rt

et al. 2020

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Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

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Section: Adjusting For Delaysmentioning

confidence: 99%

Practical considerations for measuring the effective reproductive number, Rt

et al. 2020

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“…A second complication in estimating a state-level surveillance system’s reporting rate is that the daily presented case numbers combine cases from both passive surveillance and random screening of asymptomatic individuals. The reporting rate ρ should be adjusted downward when levels of asymptomatic random screening are known and high, a correction that is not included in current estimation methods [22]. In Rhode Island, monthly screening numbers are available (Supplementary Materials, Section 3.7) and indicate that 40% of diagnostic tests administered were done so in congregate care settings, in business settings for high-contact individuals, known contacts of cases, and other settings where it was deemed helpful to test asymptomatic individuals.…”

Section: Resultsmentioning

confidence: 99%

“…The reporting rate ρ should be adjusted downward when levels of asymptomatic random screening are known and high, a correction that is not included in current estimation methods[22].In Rhode Island, monthly screening numbers are available (Supplementary Materials, Section 3.7) and indicate that 40% of diagnostic tests administered were done so in congregate care settings, in business settings for high-contact individuals, known contacts of cases, and other settings where it was deemed…”

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confidence: 99%

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

Wikle

Tran

Gentilesco

et al. 2020

Preprint

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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.

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“…In Fig. S8 we show additional reconstructions with the Covidestim method [23]; they also tend to oversmooth the first peak.…”

Section: Methodsmentioning

confidence: 99%

“…For these analyses, we considered the log-normal onset-to-death distribution described in [20] and two demography-stratified IFR estimates, one from Diamond Princess cruise ship [21] and from a seroprevalence study in Spain [22]. For comparison, we also present reconstructions based on the Covidestim method [23], and by the re-scaling of case counts by the under-reporting estimates obtained with the method of [24]. B Association between Average daily test and SES during the early peak.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile

Mena

Martinez

Mahmud

et al. 2021

Preprint

105

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The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality –measured either by direct COVID-19 attributed deaths or excess deaths– and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.

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Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: results of a Bayesian evidence synthesis model

Cited by 36 publications

References 40 publications

Practical considerations for measuring the effective reproductive number, Rt

Practical considerations for measuring the effective reproductive number, Rt

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

Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile

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