Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making

Abstract

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

A novel benchmark for COVID-19 pandemic testing effectiveness enables the accurate prediction of new Intensive Care Unit admissions

“…, testing more or fewer asymptomatic persons, or testing a younger subset of the population). Accordingly, a change in the intercept will likely signify either (a) changes in viral prevalence 7 , 11 , as the intercept represents a fixed number U for a theoretical x = 0, (i.e., a number of individuals with very severe COVID-19, while no cases are detected), or (b) changes in testing accuracy 7 , 11 , with intercept values closer to zero reflecting optimal accuracy. Rolling 3-weeks regression windows could be employed to detect dynamic changes of the pandemic.…”

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing

“…Thus, we emphasize the importance of building testing systems to screen individuals without symptoms as a necessary step in the process of decreasing our reliance on NPIs, especially when pharmaceutical alternatives are not readily available. Testing in asymptomatic populations can also allow more accurate measurement of epidemiological dynamics and estimation of key parameters, which are crucial for epidemiological modeling for future decision-making [ 39 ].…”

“…We define effective test sensitivity as the percent of actively infectious individuals correctly identified by the diagnostic test. This definition differs from the way sensitivity is determined for other tests (e.g., sensitivity as defined for reverse transcription polymerase chain reaction (RT-PCR) which detects the presence of a viral sequence), but reflects our focus on public health rather than individual patient objectives [ 23 , 39 , 40 ]. Consistent with our definition of effective sensitivity, we assume that exposed individuals (E class) cannot be detected.…”

A novel benchmark for COVID-19 pandemic testing effectiveness enables the accurate prediction of new Intensive Care Unit admissions

“…g ., testing more or fewer asymptomatic persons, or testing a younger subset of the population). Accordingly, a change in the intercept will likely signify either (a) changes in viral prevalence 7,11 , as the intercept represents a fixed number U for a theoretical x=0, ( i . e ., a number of individuals with very severe COVID-19, while no cases are detected), or (b) changes in testing accuracy 7,11 , with intercept values closer to zero reflecting optimal accuracy.…”

“…Accordingly, a change in the intercept will likely signify either (a) changes in viral prevalence 7,11 , as the intercept represents a fixed number U for a theoretical x=0, ( i . e ., a number of individuals with very severe COVID-19, while no cases are detected), or (b) changes in testing accuracy 7,11 , with intercept values closer to zero reflecting optimal accuracy. Rolling 3-weeks regression windows could be employed to detect dynamic changes of the pandemic.…”