- Weekly COVID-19 testing, with two-week isolation of positive cases, is the most cost-effective strategy to mitigate spread of the virus in the USA when transmission is high in affected areas until vaccines are widely available.
- When transmission rates are low to moderate, monthly testing and a one-week isolation period is the most cost-effective approach.
- Monthly population testing is more cost-effective than the current strategy of testing only people showing symptoms and their close contacts.
Weekly COVID-19 testing, coupled with a two-week isolation period for positive cases, may be the most cost-effective strategy to tackle the spread of SARS-CoV-2 in the USA when transmission is high until vaccines are widely available, a modelling study published in The Lancet Public Health journal suggests.
The study is the first to identify cost-effective strategies based on local transmission rates, the cost of testing and hospitalisations, and a societal willingness to pay in order to prevent COVID-19 deaths.
Since the pandemic began, countries worldwide have taken robust steps to mitigate the spread of the virus, including restrictions on movement, social distancing measures, and face mask requirements. To date, there have been more than 24 million confirmed COVID-19 cases in the USA and in excess of 400,000 deaths. Globally, more than 96 million cases and over two million deaths have been reported. Estimated economic costs in the USA exceeded $21 trillion in 2020.
This study suggests that until effective vaccines or antiviral drugs become widely available, mass testing is the best way to quickly identify and isolate infected cases. While testing was initially slow and relatively expensive, costs have decreased rapidly, with $5 tests that produce results in 15 minutes now becoming more available.
To evaluate the economic trade-offs of expanding and accelerating COVID-19 testing in the USA, the authors devised a model of 1,000 households that was scaled-up to represent the country's 328 million residents. The model incorporated population-level transmission rates and viral load dynamics - the amount of virus being produced by infected people - at the individual level.
The authors performed statistical analyses on eight scenarios in which people are tested at a frequency ranging from daily to monthly, coupled with a one- or two-week isolation period for confirmed cases. For each scenario, the authors assumed each rapid test cost $5, and that members of society are willing for government to spend $100,000 to prevent each year of life potentially lost due to COVID-19. The effectiveness of each strategy was determined taking account of rates of illness and death from COVID-19, testing and hospitalisation costs, and salary lost while in isolation.
The authors found that the most cost-effective strategy when transmission is high - when the reproduction number, or R number, is 2 or above - is weekly testing of everyone in the affected area followed by a two-week isolation period for confirmed cases. At present, the R number at state level is estimated to range from around 0.59 to 1.57 , though local-level transmission within states can vary widely.
When transmission is low to moderate - an R number below 1.9 - monthly testing followed by one week of isolation, rather than two, is likely to be most cost-efficient. The analysis indicated that loss of salary in the second week outweighs the costs of infections that occur during the same period.
The study also reveals that monthly testing across the USA - which would require 12 million tests per day - is more cost-effective than the current status quo approach of testing only people with symptoms and their close contacts, assuming each test cost $75 or less. The current cost of a single at-home test in the USA tends to range from around $25-$50.
Professor Lauren Ancel Meyers, of the University of Austin at Texas, USA, said: "Vastly expanding COVID-19 testing capacity in the US, and tailoring testing and isolation strategies depending on the local rate of transmission, has the potential to help deliver vital public health and economic benefits. While the up-front costs of mass testing might seem high, our results show that this approach is a cost-effective way of saving lives and mitigating the unprecedented threats posed by the pandemic until many more people are vaccinated.
"The logistics of implementing the mass testing could prove very challenging, requiring not only large quantities of tests, but also community compliance with the recommended testing and isolation procedures and coordinated distribution plans that may involve school, university and workplace testing, as well as the delivery of home testing kits and the establishment of public testing sites. We hope our study will help to inform policies that make these strategies more feasible."
The authors acknowledge some limitations to their study. Optimal testing strategies - which require large quantities of tests and sophisticated distribution plans - may not be logistically feasible everywhere. For the purposes of the study, it was also assumed that people who have had COVID-19 cannot be re-infected. However, there is uncertainty about long-term immunity, with some evidence suggesting it may reduce over time following recovery.
The team's economic calculations have limited scope, as they consider only testing costs and loss of salary during isolation. However, asymptomatic people who test positive may incur additional costs by seeking healthcare when they would otherwise not have suspected they were infected. While the authors quantified the economic benefits of averting deaths and hospitalisations, they did not consider the impact of preventing non-fatal illness, or indirect health and mental health effects.
NOTES TO EDITORS
This study was funded by US National Institutes of Health, US Centers for Disease Control and Prevention, and a donation from Love, Tito's (the philanthropic arm of Tito's Homemade Vodka, Austin, TX, USA) to the University of Texas to support the modeling of COVID-19 transmission and mitigation strategies. It was conducted by researchers from University of Texas at Austin, Yale School of Public Health, University of Maryland School of Medicine, Northeastern University, US, and University of Hong Kong, China.
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Peer-reviewed / Modelling / People