New strategies for dealing with future pandemics
Non-pharmaceutical interventions (NPIs) like lockdowns, social distancing restrictions, and mandatory face masks are among the most unpopular solutions to the pandemic. While many are aware of their necessity, the burden these measures have taken on livelihoods, businesses and mental health has been exhausting for a lot of people.
These strict restrictions have also given rise to a vast array of new conspiracy theories Some of them have condemned these NPIs as an attempt to remove rights. In a recent study published on the preprint server medRxiv*,researchers from the University of Vermont explore strategies to combat pandemics, as well as model potentially optimal solutions.
Study: Should we Mitigate or suppress the next Pandemic? Costs and Time-Horizons determine optimal Social Distancing Strategies. Image Credit: Khakimullin Aleksandr / Shutterstock.com
About the study
The researchers used a variational analysis approach that allowed them to derive functions that minimize or maximize a quantity over an interval in order to combine susceptible-infected-recovered (SIR) epidemic models that describe disease transmission with a function for both infection and social distancing costs over time. SIR models can determine future evolution quite accurately so long as the parameters used in the model do not change.
Sadly, with the initial pandemic response responses being dispersed and frequently changing and constantly changing, many of the early SIR models didn’t accurately reflect reality. The principles of variation allow the parameters of models to be adjusted to reach an agreed-upon time and end state. This allows the SIR model to be altered to a fixed-time model with a horizon.
SIR models typically use state variables like the population densities of susceptible or recovered populations. These variables are characterized by a constant disease progression rate (from infected to recovered) and per capita rates of disease transmission. The R0 is the basic reproduction numbers. It is the transmission rate divided by the per capita progression rate from recovered and infected.
This model shows that reproduction and transmission rates change as time passes. This could be a result of changes in social distancing practices or policies. This new number is called the RD. The Rt is the effective reproduction rate that is based on both the removal and change in social distancing.
A third parameter, c describes the costs of social distancing in relation to the risk of infection, and the final parameter, Tfinal, describes the time when the pandemic is predicted to be over. Other important values are S, which is defined as the percentage of people who will remain susceptible following the end of the pandemic and sH, which is the highest percentage of people who could be susceptible in the population that has reached herd immunity.
The researchers categorized strategies for dealing with the pandemic into two fundamental archetypes: suppression and mitigation. A mitigation strategy is when infection spikes before subsiding and a large portion of the population gets infected. This strategy is designed to minimize social distancing.
The suppression strategy is designed to prevent as many cases as is possible, with a lower percentage of infected people but enough people who have recovered to provide herd immunity. The most effective suppression strategies won’t be able to keep Rt lower than 1 for long and the most effective mitigation strategies won’t allow completely uncontrolled pandemics.
One of the most significant findings was that the perceived time-horizon and cost of social distancing may quickly lead to a switch from suppression to mitigation strategies. The chance that society will prioritize a mitigation strategy is higher the longer the disease progresses and the greater the cost of social distancing. This can happen quickly.
Any pandemic close to the border of the mitigation/suppression threshold will likely provoke argument and disagreement. Researchers point out that their strategies were created in an idealized environment, in which there were no sudden shifts in strategy or reversals of policies.
The authors offer the tongue-in-cheek suggestion that these may have been better solutions than what they have proposed and suggest that the strategies they propose have not been tested. They also identified poor communication and misinformation as a few of the main reasons why society isn’t able to implement these strategies, and the complexity, uncertainty and heterogeneity as the main obstacles that could prevent society from delivering them in the future.
These methods could be used to inform NPIs about mandatory face mask-wearing and social distancing which could be used to shut down public spaces for the duration of the pandemic as well as in the future. While the mitigation strategy may seem callous to some, loss of livelihoods could result in as many deaths due to the disease in certain scenarios, and the maths discussed here can aid in ensuring that the most effective interventions are in place. As difficult as this decision may be, I hope this research will help inform public health policy makers in the future.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information