Estimation of Unreported Novel Coronavirus (SARS-CoV-2) Infections from Reported Deaths: A Susceptible–Exposed–Infectious–Recovered–Dead Model

Abstract

In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this study, we aimed to estimate the unreported number of infections in China prior to the 23 January 2020 restrictions. To do this, we developed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model that estimated unreported infections from the reported number of deaths. Our approach relied on the fact that observed deaths were less likely to be affected by ascertainment biases than reported infections. Interestingly, we estimated that the basic reproductive number (R 0 ) was 2.43 (95%CI = 2.42-2.44) at the beginning of the epidemic and that 92.9% (95%CI = 92.5%-93.1%) of total cases were not reported. Similarly, the proportion of unreported new infections by day ranged from 52.1% to 100%, with a total of 91.8% (95%CI = 91.6%-92.1%) of infections going unreported. Agreement between our estimates and those from previous studies proves that our approach is reliable for estimating the prevalence and incidence of undocumented SARS-CoV-2 infections. Once it has been tested on Chinese data, our model could be applied to other countries with different surveillance and testing policies.

WITHDRAWN: A Fuzzy Rule-Based Compartmental Modeling for SARS-CoV-2

“…We have estimated E 0 = 20I 0 for China and NZ and E 0 = 30I 0 for the US and Bangladesh based on the estimation of the literatures [7]. We have considered the initial recovered, R(0) = 0 and the initial death, D(0) = 0 in the model [7,40]. However, we took D(0) = 5 for Bangladesh based on the real data [6].…”

“…We have set T E = 5 days (σ = 1 5 days −1 ) for China and US, T E = 4.5 days (σ = 1 4.5 days −1 ) for NZ, T E = 6 days (σ = 1 6 days −1 ) for Bangladesh, T I = 14 days (γ = 1 14 days −1 ) for China and US, T I = 12.5 days (γ = 1 12.5 days −1 ) for NZ, T I = 19 days (γ = 1 19 days −1 ) for Bangladesh, α = 0.0365 for China, α = 0.0102 for NZ, α = 0.022 for the US and α = 0.01 for Bangladesh. All these chosen parameter values are concurrent with the estimation of those in the published literatures [1,7,8,[40][41][42] and the real infection datasets of these countries as yet [6]. Using these parameter values and initial conditions, along with the values of β(I s , Q, M, t) (described in subsection 4.1), we have solved system (1) for determining the predicted S(t), E(t), I(t), R(t), D(t) curves and also the total cases curve over time for China, NZ, US and Bangladesh.…”

Estimating the daily trend in the size of the COVID-19 infected population in Wuhan

“…1. Maugeri et al [12] estimate a total of 8724 (95% CI: 8478-8921) infected cases and 92.9% (95% CI: 92.5-93.1%) unreported by 23 January 2020 with a proposed SEIRD model based on the reported number of deaths between 23 January and 9 February 2020. However, a total of 1290 cases were added to the death toll in Wuhan on 18 April 2020 by Wuhan government [13].…”

Different Patterns of the SARS-CoV-2 Epidemic across Italian Regions: a Hierarchical Clustering on Principal Components approach

“…Looking at the data, Veneto conducted a wider testing campaign (approximately 44 tests per 1,000 residents) if compared with Emilia-Romagna and Piedmont (24 tests per 1,000 residents and 17 tests per 1,000 residents, respectively) 2 . If on the one hand performing an insu cient number of tests underestimates the transmission rate and distorts the statistics 11,12 , on the other hand combining the restrictions with widespread testing may have contributed to a more rapid resolution of the epidemic in Veneto 10 .…”

“…Our study has some limitations to be considered. First, it does not take into the proportion of undocumented events, which might differently affect some indicators 12,13 . Our approach, furthermore, considers only a part of the availability of medical care resources, indicated in terms of ICU beds.…”