Data rectification to account for delays in reporting disease incidence with an application to forecasting COVID-19 cases (preprint)

Effective monitoring of infectious disease incidence remains a major challenge to public health. Difficulties in estimating the trends in disease incidence arise mainly from the time delay between case diagnosis and the reporting of cases to public health databases. However, predictive models usually assume that public data sets faithfully reflect the state of disease transmission. In this paper, we study the effect of delayed case reporting by comparing data reported by the Johns Hopkins Coronavirus Resource Center (CRC) with that of the raw clinical data collected from the San Antonio Metro Health District (SAMHD), San Antonio, Texas. An insight on the subtle effect that such reporting errors potentially have on predictive modeling is presented. We use an exponential distribution model for the regression analysis of the reporting delay. The proposed model for correcting reporting delays was applied to our recently developed SEYAR (Susceptible, Exposed, Symptomatic, Asymptomatic, Recovered) dynamical model for COVID-19 transmission dynamics. Employing data from SAMHD, we demonstrate that the forecasting ability of the SEYAR model is substantially improved when the rectified reporting obtained from our proposed model is utilized. The methods and findings demonstrated in this work have ample applicability in the forecasting of infectious disease outbreaks. Our findings suggest that failure to consider reporting delays in surveillance data can significantly alter forecasts.

Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission (preprint)

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild, represent a major contributor in the propagation of COVID-19. The asymptomatic sub-population frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number (Ro) of the virus are inaccurate. It is unlikely that a pathogen can blanket the planet in three months with an Ro in the vicinity of 3, as reported in the literature. In this manuscript, we present a mathematical model taking into account asymptomatic carriers. Our results indicate that an initial value of the effective reproduction number could range from 5.5 to 25.4, with a point estimate of 15.4, assuming mean parameters. The first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems; the average rate of growth in the number of reported cases is 23.3% per day during this period.