Estimation of COVID-19 spread curves integrating global data and borrowing information

Abstract

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual countrybased model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, the proposed model uses countrywide covariates to adjust infection trajectories in curve fitting. A joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.

Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework

“…The development of a variety of growth curves originates from population dynamics [ 49 ] and growth of biological systems [ 50–53 ] modeling. A number of growth curves have been adapted in epidemiology for trend characterization and forecasting of an epidemic, such as the severe acute respiratory syndrome (SARS) [ 30 , 31 ], dengue fever [ 32 , 33 ], pandemic influenza A (H1N1) [ 34 ], Ebola virus disease [ 28 , 38 ], Zika fever [ 29 ], and COVID-19 [ 3 , 6 , 7 , 54 ].…”

COVID-19 with Uncertain Phases: Estimation Issues with An Illustration for Argentina

“…We model ∆L(Ct), a log difference-approximation of the growth rate, given the persistent but shifting behavior (non-stationary) of the Ct series. To compare with the evolution of an epidemiological curve, we simulate a logistic (Gompertz) curve models that assume a single-peak trajectory (see for example Batista, 2020;Sanchez-Villegas, 2020, Lee et al, 2020 starting at the beginning of the contagion process, from which ∆L(Ct) 10 was calculated as shown in Figure 5 along with the actual data. We can observe how the lockdown effect in March 20 and the growth jump in May deviate the actual data from the Gompertz-curve growth rate trajectory.…”

“…However, for reasons reflected in the epidemiology literature (Moore et al, 2020) the overall process of the pandemic is uncertain in dimension, time span, speed and recurrence cycles. This late pattern may affect the prediction of long term forecast with the SIR model or with empirical logistic (Gompertz) curve models that assume a single peaked trajectory (see for example Batista, 2020;Sanchez-Villegas, 2020, Lee et al, 2020. Other more complex models based on detailed information and using either variants of nonlinear growth models (IHME, 2020) have been exposed by the specialized press to criticisms for forecast failure (underestimation) in the US (Wallace-Wells, 2020) and neural network forecast models applied to Brazil have also led to significant underestimation (Pereira et al, 2020) or have been defeated by exponential growth time series models (Martinez et al, 2020) .…”

Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia

“…One of the advantages of using the Richards model for mathematical modeling is to fit the accumulative case number, which helps to smooth out the stochastic variations in the epidemic curve owing to variations in data collection (Hsieh, 2010;Richards, 1959). The Richards model also extends the simple logistic growth model through a scaling parameter , which measures the deviation from the symmetric simple logistic curve (Lee et al, 2020;Pearl and Reed, 1930;Roosa et al, 2020). The Richards model is defined by the differential equation…”