Researchers have developed a detailed model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in New York and Seattle, USA. They found the risk of transmission is a combination of the place and the behavior of people who visit it.
Several strategies have been put in place to curb the transmission of COVID-19, the illness caused by the SARS-CoV-2 pathogen. As there are no effective pharmaceutical treatments, strategies have involved severe restrictions on people’s movements and gatherings, social distancing, lockdowns, and closure of schools, workplaces, shops and restaurants.
These strategies were put in place to reduce contact among people and break the transmission chain, which would prevent overwhelming healthcare systems. It is clear by now that these non-pharmaceutical interventions (NPIs) have helped curb disease transmission. Most of the evidence for this comes from measuring the number of cases, correlations, or statistical models to understand the relationship between mobility and the number of cases.
However, these studies do not have the detail required to measure how NPIs have affected disease transmission. This is important given that the virus does not spread homogeneously. For example, a single individual or a superspreader has been responsible for a large number of infections. Superspreading events may occur because of social contact types, such as large gatherings, high infectiousness of individuals, or the types of place, such as closed, poorly ventilated spaces.
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Modeling transmission in New York and Seattle
Understanding superspreading events can help design better NPIs or cluster-based contact tracing strategies. In a new study published in the medRxiv* preprint server, a team of international researchers used mobility information and socio-demographic data to build a model of SARS-CoV-2 transmission to understand where, when, and how many transmission events occurred in the first wave of the pandemic in the United States.
The authors generated daily contacts for 614,458 individuals in New York and 110,330 in Seattle between 15 February 2020 and 1 June 2020. They weighted the contacts between individuals based on the time and probability of exposure and defined layers of the community, workplaces, households, and schools.
During the week of 15 March 2020, the New York City School system announced the closure of all schools, and “shelter in place” orders were issued. In Seattle, schools were closed almost a week earlier, and a civil emergency was proclaimed.
The data shows how the contact networks changed with the introduction of these NPIs. For both cities, the effective reproduction number fell below 1 a week after introducing the NPIs. Using a hypothetical scenario, the data analysis suggests that if the NPIs were introduced a week later, the number of deaths would have doubled. Conversely, if the NPIs were introduced a week earlier in New York, the peak number of deaths could have been reduced by a factor of three.
Before the NPIs were introduced, the researchers found that most transmissions happened in the workplace and community. After the restrictions were in place, transmission mainly occurred in households, particularly in the NY area, in addition to food, groceries, and shopping areas.
Although the NPIs did not prevent superspreading events, they reduced their frequency. Transmission or superspreading did not occur equally in different settings or times.
The authors created a risk map of places with different types of risk. Outdoor places have a low contribution to transmission, while sports areas and museums contribute to large superspreading, but have a low overall infection rate. Shopping areas contribute more to widespread transmission but have less superspreading. However, after the introduction of NPIs, grocery shopping areas contribute to superspreading and widespread infections.
Network components, New York and Seattle metropolitan areas population and social contacts dynamics at the Community layer over time. Panel a is a schematic illustration of the weighted multilayer and temporal network for our synthetic population built from mobility data. There are three different compartments, Schools and Households layers that are static over time, and the workplace and community layer has a daily temporal component. Panel b shows the geographic penetration of mobile devices from our mobility data compared to the total population for the New York and Seattle metropolitan areas. Panel c represents the average daily number of contacts in the community layer for both metropolitan areas.
Transmission depends on the place and people
A place may not be dangerous on its own; rather, the risk is a combination of both the characteristics of the place/setting and of the behavior of individuals who visit it,” write the authors.
Although New York and Seattle showed consistent patterns, each urban area may have its peculiarities, so a common strategy may have different impacts in different areas. The results of the study likely cannot be extended to rural areas.
Therefore, the data suggests the introduction of NPIs in large urban areas were effective in slowing down the first wave of the pandemic. Because of human mobility's heterogeneous nature, COVID-19 is spread by different routes in different settings, people, and cities.
Implementation of full or partial closures over time affected how people mixed outside the household, which changed the settings for transmission and superspreading events. Prevention measures could be optimized further, such as staggered work times to reduce the number of contacts to better curb transmission.
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Article Revisions
- Apr 3 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
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