An Uneven Population Distribution Can Greatly Impact The Spread of COVID-19, According to Researcher at UCI
A study shows that population distribution can be a major factor in the spread of the coronavirus.
Most countries have long experienced population distribution disparities. Urbanized areas tend to be more crowded and densely populated while rural areas tend to be more sparsely populated. While it is believed that low-population areas can be harder to live in, they also have great advantages, particularly during these unprecedented times.
In a study conducted by the University of California Irvine, they have determined that population distribution has a major impact on the timing and severity of cases of the coronavirus. Places with high populations will always have a different experience to places with lower populations as the heterogeneous spatial features of interpersonal connections vary in terms of the manners and occasions of exposures to the illness.
In layman’s terms, places with high and low populated areas have different ways to get exposed to the virus. Since the disease is passed on from one person to another, the way and frequency of people’s interaction within a community can dictate how easily the virus can spread. In highly populated areas, people tend to get in contact with others a bit more often and in closer quarters than in areas with fewer people. As a result, the spread of the virus can be more severe and faster in places with more people.
The researchers said that the disparity in experiences can alter the understanding of infection risks in the population. It can have an impact on how willing individuals will be in taking protective actions as well as the healthcare delivery in the area in certain aspects that are not often considered or looked into by standard epidemiological projections.
Researchers also say that taking spatial heterogeneity into account, some places might not have as robust protective measures as other places. Areas that have not experienced an outbreak yet might be lulled with a false sense of security and become lax in the implementation of minimum health measures. This will be a very unfortunate event as there’s no guarantee when it comes to this disease. Due to its nature, no one can be sure that tomorrow will be the same if there are no infections today.
This study was conducted by creating geographically detailed network models of 19 US cities. They combined census information and data on contact probability as distances between residents increased. They then ran 10 versions of the COVID-19 diffusion process on every model. The infection curves on each model significantly differed as the social connectivities of the communities were very irregular.
The studies findings also suggested that there are opportunities for public health interventions. By using their models, experts may be able to lessen the stress on their healthcare services by planning ahead and taking preventive measures.
According to Carter Butts, UCI professor of sociology and co-author of the study, their research proves that incorporating geographical heterogeneity will be very useful when used in pandemic planning and scenario evaluation at the city and county levels. Since this is where decisions for policies involving healthcare logistics, infrastructure management, and many others are made, such studies will prove to be very useful to policymakers.
