A Test for Constant Fatality Rate of an Emerging Epidemic: With Applications to Severe Acute Respiratory Syndrome in Hong Kong and Beijing

Abstract

The etiology, pathogenesis, and prognosis for a newly emerging disease are generally unknown to clinicians. Effective interventions and treatments at the earliest possible times are warranted to suppress the fatality of the disease to a minimum, and inappropriate treatments should be abolished. In this situation, the ability to extract most information out of the data available is critical so that important decisions can be made. Ineffectiveness of the treatment can be reflected by a constant fatality over time while effective treatment normally leads to a decreasing fatality rate. A statistical test for constant fatality over time is proposed in this article. The proposed statistic is shown to converge to a Brownian motion asymptotically under the null hypothesis. With the special features of the Brownian motion, we are able to analyze the first passage time distribution based on a sequential tests approach. This allows the null hypothesis of constant fatality rate to be rejected at the earliest possible time when adequate statistical evidence accumulates. Simulation studies show that the performance of the proposed test is good and it is extremely sensitive in picking up decreasing fatality rate. The proposed test is applied to the severe acute respiratory syndrome data in Hong Kong and Beijing.

A sequential test for assessing the effectiveness of response strategies during an emerging epidemic

“…This is the case under the perfect hypothetical situations where everything remains constant over time, like sufficient medical supply, no new treatment, or other newly implemented interventions. In particular, the spread of the 2003 SARS epidemic in Hong Kong was analogous to scenario (A) with almost constant death and recovery probabilities throughout the outbreak (Lam et al., 2008). Other scenarios are considered to assess the robustness of the method.…”

“…Based on that, a statistical test for the null hypothesis of no change in the real‐time fatality rate over time against the alternative of a decreasing one was proposed by Lam et al. (2008) and applied to assess the effectiveness of implemented measures in Hong Kong and Beijing during the SARS epidemic in 2003. However, the daily number of inpatients, and the occurrence time, or at least the order of occurrences, of the deaths and recoveries are required to be known in their proposed test.…”

A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy

“…In the context of the current COVID-19 pandemic, however, (i) the epidemic has lasted for a long time, approximately two years; (ii) virulent mutant variants of COVID-19 have emerged since 2021 2 ; (iii) there is a significant reporting delay in time from disease onset to death 3 ; and (iv) a non-negligible proportion of the population are getting vaccinated worldwide 4 . Therefore, the disease severity no longer depends solely on disease virulence, but varies according to a basket of time-varying confounding factors 5 . For instance, a worsening fatality rate always informs public health professionals to adopt timely response strategies, such as social distancing or travel bans, before the hospitals are slammed by infected patients.…”

A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

“…Relative to CFR, the RTFR was shown to be more sensitive to capture changes in fatality rate during the course of an epidemic. To detect a change in RTFR statistically, Lam et al 10 developed a one-sample sequential test for the null hypothesis of constant fatality rate, which is applied to investigate the effectiveness of the interventions in Hong Kong and Beijing during the severe acute respiratory syndrome epidemic in 2003. Therein, the testing procedure starts before the implementation of a potential intervention.…”