A new extension of state-space SIR model to account for Underreporting – An application to the COVID-19 transmission in California and Florida

Deo, Vishal; Grover, Gurprit

doi:10.1016/j.rinp.2021.104182

Cited by 18 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the above studies directly study the spatiotemporal distribution of COVID-19 through reported case data, ignoring the possible under-reporting of data in reported cases ( Lau et al, 2021 ). The conclusions obtained directly from the reported cases may deviate from the actual situation, thereby affecting the judgment of decision-makers( Bastos et al, 2021 ; Deo & Grover, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Wang

Bao

et al. 2022

Cities

View full text Add to dashboard Cite

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

show abstract

Section: Related Workmentioning

confidence: 99%

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Wang

Bao

et al. 2022

Cities

View full text Add to dashboard Cite

show abstract

“…Wu et al, 2020 [22] reported that the actual number of infections was estimated to be 3 to 20 times higher than the confirmed cases for various states of the United States of America. Lu et al, 2020 [23] showed that there were drastic cases of underreporting in many countries worldwide.…”

Section: Parameters Of the Modelmentioning

confidence: 99%

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Sallahi

Park

El-Mellouhi

et al. 2021

Biology

View full text Add to dashboard Cite

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

show abstract

“…Bjornstad et al [ 16 ] estimated the transmission rates of measles for England and Wales using the tSIR model. Deo and Grover [ 17 ] noticed that the unreported pathogens are more threatening than reported and quarantined ones. Their proposed model was susceptible-infected (quarantined/free)-removed-deceased (SI(Q/F)RD).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR

Taimoor

Ali

Shah

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Pakistan is currently facing the fourth wave of the deadly coronavirus, which was first reported in Wuhan, China, in December 2019. This work utilizes the epidemiological models to analyze Pakistan’s COVID-19 data. The basic susceptible, infected, and recovered (SIR) model is studied assuming Bayesian and time-series SIR (tSIR) approaches. Many studies have been conducted from different perspectives, but to the best of our knowledge, no study is available using the SIR models for Pakistan. The coronavirus incubation period has been set to 14 days across the globe; however, this study noticed that the assumption of 14 days is not suitable for Pakistan’s data. Furthermore, on the basis of R 0 , we infer that COVID-19 is not a pandemic in Pakistan, as it was in other nations, such as the United States, India, Brazil, and Italy, among others. We attribute this to the best strategy adopted by the Government of Pakistan to minimize the burden of COVID-19 cases in Pakistani hospitals. It is also noticed that the posterior-based SIR (pSIR) model with uniform prior to R 0 and Poisson distribution (of log-likelihood) provides better results as compared to other distributions. From time-series SIR (tSIR), we observed that the value of the reporting rate ρ is less than 1 which means that cases are underreported.

show abstract

A new extension of state-space SIR model to account for Underreporting – An application to the COVID-19 transmission in California and Florida

Cited by 18 publications

References 15 publications

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR

Contact Info

Product

Resources

About