A mathematical model questions the effectiveness of closing schools to reduce deaths from coronavirus

Researchers from China and the United States have analyzed the impact of pandemic control measures in New York. The results confirm the importance of distancing to reduce serious cases but show that the closure of educational centers contributes little if the elderly are not well protected in public spaces.

The coronavirus that causes covid-19 has already infected more than 100 million people and killed more than 2.3 million worldwide. Prevention and control measures that different countries have put in place, for example in schools, have yielded different results in terms of the scope of the pandemic and the changes it causes in society.

In this context, researchers from the City University of Hong Kong (CityU), the Chinese Academy of Sciences, and the Rensselaer Polytechnic Institute (USA) have developed a mathematical model to analyze the efficacy of different non-pharmaceutical interventions (NPI, in English), such as the closure of schools or social distancing in different areas (home, workplace, public spaces) in New York City.

According to a model applied to data from New York, the social distancing of the entire population and the protection of the elderly in public spaces is the most effective control measure to reduce serious coronavirus infections and deaths, and not so much the closure of schools, which hardly benefits

After running thousands of simulations, the results, published in the magazine Chaos, show that the social distancing of the entire population together with the protection of the elderly in public spaces is the most effective control measure to reduce serious coronavirus infections and deaths, and not so much the closure of schools, which hardly benefits.

“School only represents a small proportion of social contact. People are more likely to be exposed to the virus in public places, such as restaurants and shopping malls, "explains co-author Qingpeng Zhang of CityU, who insists:" Since we focus on serious infections and fatalities here, the closure of schools contributes little if older citizens are not protected in public facilities and places ”.

Zhang acknowledges to SINC that infected students could act as a bridge to older or more vulnerable people, “but our results indicate that social contacts linked to schools are relatively lower than those of other places. Therefore, the closure of schools is not as effective as would be expected if those other public places do not have adequate measures of social distancing”.

To carry out their study, the authors have used a new model called A-SEIRD (assessing susceptible-exposed-infected-recovered-dead cases taking into account age) based on the well-known SEIR / SIR models, highlighting the role of specific patterns that include age and location in epidemiological models.

The ideal non-pharmaceutical interventions (NPI) are those that can contain the epidemic with minimal disruption of social contacts, something especially important in cities whose economies depend on international trade

“These patterns are unique to different cities: good practice in one city may not translate to another,” says Zhang, “although in any case, ideal NPIs are those that can contain the epidemic with minimal disruption of social contacts, something especially important in cities like New York or Hong Kong, whose economies depend on international trade. In others, such as Madrid, I assume that it would be applicable because it is a relevant economic center in southern Europe, but it is not a scientific conclusion because I do not know the data”.

In the case of New York, the numerical simulations of the model show that its control policies reduced the number of infections by 72% and the number of deaths by 76% at the end of 2020 (considering ranges in statistical interquartile). The data also reflects that being such a densely populated city, the effects of schools with the coronavirus are significantly less than the general day-to-day interactions between the population.

The authors emphasize that while these findings have promising implications, the model is still unable to capture the complexities and subtle details of real-life interactions by offering a perfect measure. The inclusion of data from mobile telephony, census, transport, and other big data could help in the future to present results that are more adjusted to reality.