Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

Lu, Fred; Nguyen, André T.; Link, Nicholas; Molina, Mathieu; Davis, Jessica T.; Chinazzi, Matteo; Xiong, Xinyue; Vespignani, Alessandro; Lipsitch, Marc; Santillana, Mauricio

doi:10.1371/journal.pcbi.1008994

Cited by 38 publications

(39 citation statements)

References 55 publications

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“…The figure shows that the M dl P aram Rate increases in early March, and then gradually decreases. This observation is explained by the community spread-driven COVID-19 outbreaks that were not reported until early March, which fits the earlier study [40].…”

Section: Resultssupporting

confidence: 89%

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Accurately Estimating Total COVID-19 Infections using Information Theory

Cui

Haddadan

Haque

et al. 2021

Preprint

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Estimating the true extent of the outbreak was one of the major challenges in combating COVID-19 outbreak early on. Our inability in doing so, allowed unreported/undetected in- fections to drive up disease spread in numerous regions in the US and worldwide. Accurately identifying the true magnitude of infections still remains a major challenge, despite the use of surveillance-based methods such as serological studies, due to their costs and biases. In this paper, we propose an information theoretic approach to accurately estimate the unreported infections. Our approach, built on top of an existing ordinary differential equations based epi- demiological model, aims to deduce an optimal parameterization of the epidemiological model and the true extent of the outbreak which "best describes" the observed reported infections. Our experiments show that the parameterization learned by our framework leads to a better estimation of unreported infections as well as more accurate forecasts of the reported infec- tions compared to the baseline parameterization. We also demonstrate that our framework can be leveraged to simulate what-if scenarios with non-pharmaceutical interventions. Our results also support earlier findings that a large majority of COVID-19 infections were unreported and non-pharmaceutical interventions indeed helped in mitigating the COVID-19 outbreak.

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Section: Resultssupporting

confidence: 89%

“…Other lines of work to estimate the reported rate for COVID-19 exploits existing influenza surveillance systems to estimate symptomatic infections [40], due to symptomatic similarities between the two diseases. However, they also suffer from ad-hoc corrections to account for the similarities between COVID-19 and influenza symptoms.…”

Section: Introductionmentioning

confidence: 99%

Accurately Estimating Total COVID-19 Infections using Information Theory

Cui

Haddadan

Haque

et al. 2021

Preprint

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“…However, this opens new avenues for construction and improvement of tools to extract information which are not explicit in data. A common strategy, possibly the gold standard in applied epidemiology, is the use of Bayesian inference models [29,30]. A particularly promising strategy is the data-driven [33,34,37] approach where mathematical and mechanistic models are supplied by data, allowing to make predictions which are not explicitly avail-able.…”

Section: Discussionmentioning

confidence: 99%

“…The approach can be promptly modified or generalized for other types of data and epidemic compartments. The method shares similarities with the recent approaches to estimate undocumented cases [25,29,30,32], such as the use of reported infections and deaths. The central difference is that our approach is more mechanistic and less Bayesian.…”

Section: Introductionmentioning

confidence: 98%

“…For example, Pullano et al [29] estimated that 9 out 10 cases of symptomatic infections were not ascertained by the surveillance system in France from 11 May to 28 June 2020, suggesting that large numbers of symptomatic cases of COVID-19 did not seek for medical advice. Lu et al [30] considered four complementary approaches to estimate the cumulative incidence of symptomatic cases of COVID-19 in the US and concluded that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Subramaniana et al [31] used a model including testing information to fit the case and serology data from New York City to estimate a low proportion of symptomatic cases (13 to 18% of the total infections), and that the reproductive number could be larger than often assumed.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven approach in a compartmental epidemic model to assess undocumented infections

Costa,

Cota,

Ferreira

2022

Preprint

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Nowcasting and forecasting of epidemic spreading, fundamental support for policy makers' decisions, rely on incidence series of reported cases to derive the fundamental epidemiological parameters. Two relevant drawbacks for predictions are the unknown fraction of undocumented cases and levels of nonpharmacological interventions that span highly heterogeneously across different places. We describe a simple approach using a compartmental model including asymptomatic and pre-asymptomatic contagions that allows to estimate both the level of undocumented infections and the value of Rt from reported case series in terms of epidemiological parameters. The method was applied to epidemic series for of COVID-19 across different municipalities in Brazil allowing to quantify the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states, while being very sensitive to variations in case count series. The methods described here are general and we expect that they can be extended to other epidemiological approaches and surveillance data.

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Tracking COVID-19 Infections Using Survey Data on Rapid At-Home Tests

Santillana,

Uslu,

Urmi

et al. 2024

JAMA Netw Open

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ImportanceIdentifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic’s effects, yet it remains a challenging task.ObjectiveTo characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing.Design, Setting, and ParticipantsInternet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium—the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution.Main Outcomes and MeasuresThe main outcomes were (1) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023 and (2) estimates of uncounted test-confirmed cases from February 1, 2022, to January 1, 2023. These estimates were compared with institutionally reported COVID-19 infections collected by Johns Hopkins University and wastewater viral concentrations for SARS-CoV-2 from Biobot Analytics.ResultsThe survey spanned 17 waves deployed from June 1, 2020, to January 31, 2023, with a total of 408 515 responses from 306 799 respondents (mean [SD] age, 42.8 [13.0] years; 202 416 women [66.0%]). Overall, 64 946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation, r = 0.96; P &lt; .001) from April 2020 to January 2022 (50-state correlation mean [SD] value, r = 0.88 [0.07]). This was before the government-led mass distribution of at-home rapid tests. After January 2022, correlation was diminished and no longer statistically significant (r = 0.55; P = .08; 50-state correlation mean [SD] value, r = 0.48 [0.23]). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r = 0.92; P &lt; .001) and after (r = 0.89; P &lt; .001) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79; P &lt; .001) with wastewater viral concentrations before January 2022, but poorly (r = 0.31; P = .35) after, suggesting that both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were likely unaccounted for in official records between January 2022 and January 2023.Conclusions and RelevanceThis study suggests that nonprobability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and health care officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future.

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Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

Cited by 38 publications

References 55 publications

Accurately Estimating Total COVID-19 Infections using Information Theory

Accurately Estimating Total COVID-19 Infections using Information Theory

Data-driven approach in a compartmental epidemic model to assess undocumented infections

Tracking COVID-19 Infections Using Survey Data on Rapid At-Home Tests

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