Modeling the effectiveness of RT-PCR, RT-LAMP, and antigen testing strategies for COVID-19 control

The COVID-19 pandemic has highlighted the crucial role of testing in mitigating disease transmission. This study evaluates the effectiveness and cost-efficiency of various testing strategies, including daily screening, symptom-based testing, and contact-based testing, using RT-PCR, RT-LAMP, and antigen tests. We employed stochastic modeling on a contact network to assess the impact of these strategies on outbreak control. Simulations were conducted to evaluate the probability of an outbreak, epidemic size, and testing costs for each strategy. Scenarios with varying levels of population immunity were also explored. Daily screening, particularly with RT-PCR and RT-LAMP, significantly reduced transmission risks but incurred higher costs. Symptom-based testing offered a more cost-effective alternative, albeit with lower efficacy in mitigating outbreaks. Antigen tests, despite their lower sensitivity, proved to be a cost-effective option for symptom-based testing. Turnaround time of symptom-based testing was a more critical factor than assay sensitivity in containing outbreaks. Combining symptom-based testing with contact tracing further reduced outbreak probability. In scenarios with pre-existing population immunity, testing all symptomatic individuals was the most effective and cost-efficient approach when compared to testing a lower proportion of symptomatic individuals. Our findings suggest testing strategies could be adapted based on the stage of the epidemic, population immunity, and available resources. Daily screening is most effective but costly, while symptom-based testing combined with contact tracing offers a more cost-effective approach. Antigen tests can be a viable alternative for symptom-based testing in resource-limited settings. Rapid case identification and isolation are crucial for optimal outbreak control. These findings provide valuable insights for designing targeted interventions to protect communities while managing limited resources during current and future infectious disease outbreaks.