With the current COVID-19 pandemic, enormous disruptions have occurred to social and economic activity, besides the high toll of human life. In attempts to reduce the spread of the virus, different authorities have adopted a variety of non-pharmacological interventions (NPI). These include face shields, hand-cleansing, social distancing, and widespread restrictions on mobility.
Figure 1. Outcomes of the simulations of COVID-19 infection curves. On the left: green, yellow, and red circles indicate low, intermediate, and high levels of model parameters β, v, and k for each of the 27 simulation scenarios. Parameter values are depicted in the box on the right panel. On the right: grey shades portray the progression of the infection across simulation time steps, which was obtained through the smoothing of all simulations under each scenario. The width of each shade is scaled to the height of each infection wave. Blue horizontal bars represent the period when the healthcare system is simulated as overloaded, according to empirical data on available beds. Red circles with crosses portray the peaks of infections.
The need for NPIs is urgent since the clinical course of the outbreak makes it clear that an uncontrolled increase in the number of cases will inevitably overwhelm the healthcare system. Therefore, the most effective measures need to be identified.
The type of NPI used ranges from physical barriers to the passage of the virus, such as plastic face shields and masks, to social distancing and restrictions on travel. The opponents of such NPIs cite the disruption of economic interests. The researchers concur with the view that the right containment measures need to be devised with respect to public welfare and the weight of scientific evidence.
The study: modeling epidemic spread with various npis
The current study is aimed at modeling the spread of the epidemic when these strategies are in place to help policymakers to make the right decisions.
The researchers focus on the perception that the virus spreads through a network of human contacts over time and within different spaces. Therefore, they seek to make use of a model that will take into consideration this type of change, as well as individual characteristics that affect disease spread. They also account for the difference in disease severity and mortality with age.
Using network models that are based on the individual but are stratified by age, the researchers constructed city-level simulations to try out the effects of each NPI on weakening the outbreak.
They used different health protocols and COVID-19 parameters to arrive at different scenarios considering the time to infection peak, waning of case numbers and relatively low-risk activities.
Higher exposure/contact levels, higher rate of spread
Considering the probabilities of personal exposure to the virus, and local as well as contact among people, in terms of low, intermediate and high levels, they found that an increase in all three of these parameters was linked to a faster and higher peak of infection. When the parameter values were high, the peak was at 6-8 weeks from the first case, with a narrow high shape.
When they were moderate to high, early wave peaks emerged. At lower values, the peak occurred later, and almost two peaks were found at about 14-16 weeks from the first case. Therefore, the infection pattern showed a flatter profile.
In the simulation used, in almost all the scenarios, the healthcare system was overwhelmed. When the wave was steeper and smoother in outline, corresponding to higher parametric values, the hospital capacity recovered more quickly.
Secondly, the researchers examined the simulation in terms of changing values for each parameter. The results showed that the infection grows rapidly with higher values. When both parameters are reduced in value, the interventions become consistently more effective in reducing the burden of infection. Importantly, this is a non-linear relationship.
Models that had a low exposure rate and high social distancing inputs showed later infection peaks and fewer overall infections.
Higher contact/exposure, higher hospital overwhelm
When healthcare systems were studied, with respect to the first and last day of the over-capacity period, and the percentage of bed deficit, the model shows that both social distancing over large scale (but not neighborhood scale) and exposure rate influence the saturation significantly.
When both these parameters were at high values, the saturation-related aspects of the infection wave increased. The model also predicts a 3-5 times higher requirement for healthcare units than the current potential.
Individual exposure: a first-line risk factor
A dramatic finding of the model, which puts the responsibility for preventing viral spread squarely on the individual’s shoulders, is that the exposure of each individual is among the most critical factors propelling the wave upwards and forwards.
This includes the use of face masks, face shields, hand sanitizers and other personal protective equipment (PPE). These have been recommended for general use by the World Health Organization (WHO).
However, there is no general agreement on whether all workers should use them, or if they should be restricted to essential workers. It is also not known if everyone will have access to them once the demand rises worldwide.
The researchers favor legal measures to promote mask use and hand hygiene in a rational manner, but point out that so far no studies have shown concrete scientific evidence that masks and hand sanitation do reduce the spread of the virus. Moreover, unnecessary demand for masks has led to their incorrect use, which could push the risk higher and lead to overpricing.
The researchers recommend that PPE should be used when there is a potential threat of virus spread or contact with air droplets. However, they emphasize, “Social distancing protocols seem considerably more effective.”
Implications and applications
The study found that when a single intervention protocol is changed, the pattern of spread of the infection and the number of cases undergoes significant change. Secondly, without effective NPI use, healthcare facilities will be overwhelmed.
Thirdly, social distancing, increased mass testing and hospital investments are the best ways to fight the pandemic.
There are many ways such as stay-at-home recommendations, home-based offices, and online teaching classes, ranging from voluntary to context-dependent mandatory reasons, which can greatly contribute to reducing the spread of the new coronavirus. Our results reinforce that these may be the best current strategies.”
Simultaneously, the model suggests that essential activities that must be repeated regularly, such as shopping for grocery necessities, or errands to the bank, pharmacy or gas station, have little impact when carried out within the same neighborhood.
Early adoption of such protocols within a few days of the first case is essential to reduce the spread of the infection, but, the researchers say, “Many people tend to underestimate this protocol since its effects may take weeks to appear.”
At the same time, they call for more research to understand the variation in the need for social distancing based on the social and economic context and the financial hardship suffered by the people involved. They conclude, “Models with a real-time structure accounting for this dynamic would be more appropriate to build an evidence-based political framework.”
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.
Baumgartner, M. T., and Lansac-Toha, F. M. (2020). Assessing the Relative Contributions Of Healthcare Protocols For Epidemic Control: An Example With Network Transmission Model For COVID-19. medRxiv preprint doi: https://doi.org/10.1101/2020.07.20.20158576. https://www.medrxiv.org/content/10.1101/2020.07.20.20158576v1