Results of our study extend the understanding of policy effectiveness in the literature. For example, Ref.15 studied the original virus and concluded that wearing mask should be widespread, rather than limited to susceptible or infectious individuals. Ref.29 reviewed data from 32 countries with varying levels of policy strictness under the original virus and found that the higher the strength of government interventions at early stages, the more effective the policy was at reversing or slowing growth of death rate. Our results demonstrate that these are only true for the original virus but not for the more transmissible variants (e.g., \(\:{R}_{0}>5\)). The widespread masking policy is less effective, e.g., peak ICU, peak hospitalizations, and deaths than social distancing for the vulnerable group against Omicron.
In Minnesota, the Governor started the Stay Home Order on 3/28/2020 when 441 cumulative confirmed cases and 5 cumulative deaths were recorded, and the order remained in effect until 5/18/2020, for a total of 54 days. Since the order was enacted on Day 7, i.e., 3/28/2020, our model’s results indicate that the best timing to end it should have been on Day 111 with a duration of 104 days, in terms of lowest possible peak ICU occupancy, or later than Day 150 in terms of the total deaths. The longest duration used in the numerical experiment is 150 days. The total deaths have not reached its lowest level on Day 150 if the Stay Home Order is enacted on Day 7. If the Governor was only able to keep the order in effect for 54 days, then the best timing to enact the Stay Home Order in Minnesota would have been on Day 28 to achieve the lowest peak ICU occupancy, or on Day 40 to minimize total deaths.
Interestingly, our model shows that if a policy is enacted on Day 7, kept for 54 days, with no other policies, the Stay Home Order (Policy 2) results in the highest total deaths (45.6K) by Day 365, compared to Policy 1 (44.5K), Policy 3 (37.7K), and Policy 4 (45.3K). Policy 2 also leads to the second highest peak ICU occupancy (5.0K), while Policy 1 results in 4.6K, Policy 3 in 4.5K, and Policy 4 in 5.1K. The best policy under these conditions is Policy 3 (social distancing for vulnerable groups), as it achieves the lowest peak ICU and total deaths, with lower social and economic costs. This suggests that stricter policies need to be in effect longer to be effective. If not, outcomes could be worse than with less strict policies, making the additional costs unjustified. The necessary duration of a policy shortens with increased virus transmissibility.
The test-trace-quarantine policy is a suppression policy, which requires “high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use”19. This policy aims to minimize the total infections rather than prevent the healthcare system from being overwhelmed. When effectively implemented, this approach can significantly reduce the transmission rate, potentially bringing it nearly to zero. According to Figure A1 in the Appendix III, if the strictest policy, with a 99% reduction of the contact rate (e.g., test-trace-quarantine policy), were enacted on Day 1 and lasted for 150 days, the peak ICU occupancy and the total number of deaths would almost be equivalent to the situation where no policies were used. This result suggests that such a strict policy cannot be lifted if the highly transmissible virus is not eradicated. Otherwise, the policy only delays the outbreak of the pandemic without reducing the total number of infections or deaths. Once lifted, the pandemic will begin anew. Thus, the test-trace-quarantine policy should be employed to buy time for the development of vaccines and effective treatments, or until the virus is eradicated.
Cornell University is one example that successfully implemented this policy for as long as they could. Cornell University reopened for in-person instruction in Fall 2020, when the original virus was dominant, one of the earliest universities in the US to reopen during the pandemic. It implemented an asymptomatic screening program based on an SEIR model to test students regularly and test varsity athletes and students in Greek-life organizations even more frequently11. Due to the specific settings of Cornell University, a near-closed community with a relatively small-scale total population, of which the majority is young and healthy; the compliance of this policy was high. Combining with mandatory vaccination, it succeeded in controlling campus outbreaks until Omicron hit in December 2021, which forced the university to shut down its campus35. Cornell maintained their COVID restrictions as the New York State dropped its mask mandates in March 202212. However, there was no severe illness in any of the infected students at Cornell since fall 202012. From a 20/20 hindsight perspective, the number of infections might not be the best objective to determine NPI policies. Applying our results to the settings of Cornell University, the test-trace-quarantine policy might not be the most cost-effective strategy. We would recommend that Cornell University require social distancing for the vulnerable group only, particularly after the vaccine became available or when the virus was highly transmissible, e.g., Omicron, while imposing no restrictions for others.
China is another example that implemented such a policy on a large scale, “dynamic Zero-COVID” policy, from January 2020 to January 8, 202344. Due to the high compliance to the policy enforced by the government, it succeeded in keeping the number of COVID deaths low, about 6,000 deaths recorded among 1.4 billion people in China8. According to our results, the suppression policy cannot be lifted until the virus is eradicated. China implemented the strictest policy early in the pandemic and maintained it for approximately three years, which allowed them to outlast the original and Delta variants. This approach ensured that the policy remained in effect long enough to achieve its intended goals. The country has, however, paid incredibly high social and economic costs, such as extremely harsh and severe restrictions on people's movements. This policy was no longer sustainable when Omicron hit.
As the virus becomes more transmissible and less deadly, a better objective to manage the pandemic would be not to overwhelm the healthcare system rather than minimizing the infections. For example, if a policy is enacted on Day 7 under the late Omicron, our results show that Policy 3 needs to stay in place for 25 days to reach the lowest peak ICU occupancy of 4.2K a day and Policy 2 needs to stay in place for 30 days to reach the lowest peak ICU of 3.9K a day. Policy 3 is a good balance between the social costs and the burden on the healthcare system. Although the specific number cannot be directly translated into situations in China, the general conclusion applies to China, that Policy 3, social distancing for vulnerable groups, should be the best policy to use when Omicron was the dominant variant, rather than the strictest dynamic Zero-COVID policy.
The optimal timing for initiating or terminating a policy is contingent upon the virus's transmissibility. There exists an ideal duration for a policy, beyond which no additional reduction in the peak burden on the healthcare system can be achieved. Note that extending an NPI beyond its ideal duration will continue to reduce overall infections and deaths. Lifting the policy after this period could cause ICU occupancy to rebound, but it would not exceed its previous peak level.
With higher transmissibility of the virus, a shorter duration of the policy is required, necessitating a more rapid response time for decision-makers to implement an NPI policy. Should a stringent policy be implemented for a duration that is insufficient, its efficacy may be less than that of a less restrictive policy applied over the same period, potentially yielding outcomes worse than taking no action at all against a highly transmissible virus, e.g., Omicron.
For a fixed duration of a policy, it is not always optimal to initiate the policy at the onset of the pandemic. The shorter the policy duration, the later it should be introduced into the pandemic timeline. Essentially, if a strict NPI policy like lockdowns cannot be sustained long enough, policymakers should not implement such stringent measures at the onset of a pandemic. Instead, policymakers should consider enacting these policies later in the pandemic for maximum effectiveness. This applies when virus eradication is infeasible, as with COVID-1928. If eradication is possible, most effective policy is a suppression strategy, such as test-trace-quarantine, as seen in the 2003 SARS outbreak50. If suppression policy cannot be maintained until eradication or vaccines are available, we recommend switching to mitigation policies. Given that hospitalization, ICU admission, and mortality rates vary among different groups, optimal NPI policy should balance social costs and total deaths, targeting strategies based on the virus's transmissibility. For highly transmissible but less lethal variants (e.g., Omicron), social distancing for vulnerable groups is best. For less transmissible variants (e.g., the original virus, Delta, \(\:{R}_{0}<5\)), widespread masking is more effective. Modest social distancing is nearly ineffective against highly transmissible viruses.