Study shows testing with self-isolation trumps physical distancing in epidemic control

In a recent study published in the journal Scientific Reports, researchers discuss the effectiveness of physical distancing and testing with self-isolation as measures to control viral transmission during an epidemic.

Study: Physical distancing versus testing with self-isolation for controlling an emerging epidemic. Image Credit: Kzenon / Shutterstock.com

The pros and cons of social distancing

Physical distancing measures, such as work-from-home requirements, closures of schools and businesses, travel restrictions, and voluntary behavioral changes limiting interpersonal contacts, were crucial in the early phases of the coronavirus disease 2019 (COVID-19) pandemic when reliable diagnostic tests and effective vaccines and treatments were not widely available.

The measures taken to combat the COVID-19 pandemic can be attributed to certain negative consequences, including decreased employment, reduced earnings, as well as adverse physical and mental health effects due to reduced economic and social activities. Therefore, extensive research is needed to compare the effectiveness of physical distancing and isolation in curbing an epidemic.

About the study

A continuous-time compartment model was formulated according to the S-I-R framework to couple epidemiological and economic processes to represent the aspects of viral transmission, illness, and recovery. A primary compartment was added for infected individuals with elevated transmissibility, known as “superspreaders,” along with the tracking of the susceptible (S), infected (I), recovered (R), or dead (D) population fractions.

Thirteen secondary compartments were included to monitor the outcomes of COVID-19 testing. These compartments consisted of nine unique states for individuals waiting for test results, as well as four states for individuals in isolation.

To characterize transition rates among the additional compartments, various parameters were included that represented the influence of physical distancing on the rate of contact rate, the fraction of infected individuals who became superspreaders, testing frequency, postponement in receiving test results, false negative and positive error rates in tests, and mean testing compliance rate among persons who tested positive and were required to self-isolate.

Study findings

The first model variation, which included superspreading and a diminishing value per statistical life (VSL), suggested that implementing an optimal distancing policy can reduce the contact rate by almost 34% of its uncontrolled level for approximately 4.5 months. This reduction in contact rate can lead to a 23% reduction in deaths caused by infection.

According to the analysis, the benefits of this policy are worth 24.9% of the annual gross domestic product (GDP), while its costs are 14.8% of GDP. Therefore, the net economic benefit is 10.2% of GDP.

The optimal testing policy involves testing individuals who are not isolated or waiting for test results every other day for approximately 10 months, which amounts to 52.2% of days. This policy results in a 67.4% decrease in infection-related deaths, with the benefits, costs, and net benefits are equivalent to 55.0%, 30.3%, and 24.7% of GDP, respectively.

Superspreaders were eliminated from the system in the second model variation. The qualitative results were comparable to those of superspreaders, with the testing strategy performing better than the physical distancing technique.

The optimized distancing policy’s performance remains consistent regardless of the presence of superspreaders. Additionally, the testing policy’s performance was lower in the absence of superspreaders than when they were present.

The third model variant included superspreaders with VSL remaining constant rather than reducing with the risk reduction size. Both optimized policies in this variation were stricter as compared to those in the second model variation due to the higher benefits of control with a constant VSL.

The optimized testing approach was more effective than the optimized distancing approach. The combined policy had a slightly better performance as compared to the optimized testing policy alone.

The fourth model variant involved the exclusion of super-spreading with the use of a constant VSL. The optimal distancing policy remained unchanged in the third model variation.

In this case, the optimal testing policy was extended by 11 additional days; however, the policy was less successful than under the third variation due to the comparative advantage testing displayed in identifying persons having more than average viral loads. The combined policy outperformed the optimal testing policy by adding a small amount of distancing for almost seven months.

Conclusions

The current study reports the effectiveness of physical distancing measures as compared to testing with self-isolation control techniques. According to the study model, random testing combined with voluntary self-isolation can provide more significant benefits than physical distancing in a variety of conditions for an epidemic similar to the recent COVID-19 pandemic. However, the effectiveness of these two strategies is highly reliant on the pathogen’s transmissibility.

Diagnostic tests are more effective in diagnosing infections in individuals with high viral loads who are also more likely to transmit the virus to others. Therefore, super-spreading events make testing a more effective strategy compared to physical distancing.