Year: 2022
Type: article
Abstract: BACKGROUND: Despite the development of safe and effective vaccines, effective treatments for COVID-19 disease are still urgently needed. Several antiviral drugs have shown to be effective in reducing progression of COVID-19 disease. METHODS: In the present work, we use an agent-based mathematical model to assess the potential population impact of the use of antiviral treatments in four countries with different demographic structure and current levels of vaccination coverage: Kenya, Mexico, United States (US) and Belgium. We analyzed antiviral effects on reducing hospitalization and death, and potential antiviral effects on reducing transmission. For each country, we varied daily treatment initiation rate (DTIR) and antiviral effect in reducing transmission (AVT). RESULTS: Irrespective of location and AVT, widespread antiviral treatment of symptomatic adult infections (20% DTIR) prevented the majority of COVID-19 deaths, and recruiting 6% of all adult symptomatic infections daily reduced mortality by over 20% in all countries. Furthermore, our model projected that targeting antiviral treatment to the oldest age group (65 years old and older, DTIR of 20%) can prevent over 30% of deaths. Our results suggest that early antiviral treatment (as soon as possible after inception of infection) is needed to mitigate transmission, preventing 50% more infections compared to late treatment (started 3 to 5 days after symptoms onset). Our results highlight the synergistic effect of vaccination and antiviral treatment: as the vaccination rate increases, antivirals have a larger relative impact on population transmission. Finally, our model projects that even in highly vaccinated populations, adding antiviral treatment can be extremely helpful to mitigate COVID-19 deaths. CONCLUSIONS: These results suggest that antiviral treatments can become a strategic tool that, in combination with vaccination, can significantly reduce COVID-19 hospitalizations and deaths and can help control SARS-CoV-2 transmission.
Source: BMC Infectious Diseases
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: National Science Foundation, Fred Hutchinson Cancer Research Center, National Institutes of Health, Centers for Disease Control and Prevention, National Cancer Institute, National Institute of Allergy and Infectious Diseases, Office of Research Infrastructure Programs, National Institutes of Health
Awards: G1165297796, High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases Research, Statistical Methods for Evaluating and Guiding Implementation of New HIV Prevention Strategies, G4191098137, RAPID: A comparative online simulation tool for COVID-19 vaccine allocation, G4681072085, Statistical Methods for Evaluating Markers for Treatment Selection, G607705607, G7522203623, G9000028484, HIV Prevention Trials Network Leadership and Operations Center, SDMC: HIV Vaccine Trials Network, The University of North Carolina Center for AIDS Research, Leadership Group for the Infectious Diseases Clinical Research Consortium (IDCRCLG), SDMC: HIV Prevention Trials Network
OpenAlex ID: W4291019601