The influence of crowding on COVID-19 transmission dynamics

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Researchers in the United States have analyzed the effects that geographic factors have on the spread of COVID-19 disease. They found that the epidemic intensity is significantly influenced by crowding, with epidemics in densely-populated cities lasting longer and having a more significant overall incidence.

Their analysis also predicted that small inland cities in sub-Saharan Africa will experience high epidemic intensity and are especially prone to strains on public health systems.

A pre-print version of the article, which is undergoing peer review, is currently available in MedRxiv.

Study: Crowding and the epidemic intensity of COVID-19 transmission. Image Credit: Lightspring / Shutterstock
Study: Crowding and the epidemic intensity of COVID-19 transmission. Image Credit: Lightspring / Shutterstock

The influence of geographic factors has been unclear

Analyses have shown that during the first phase of the outbreak, the spread of COVID-19 in China was primarily driven by human mobility, and the non-pharmaceutical interventions that were implemented to restrict mobility and gatherings in cities do seem to have reduced the number of locally-acquired cases.

However, authorities remain unclear about which geographic factors influence the dynamics of local transmission because empirical evidence of their effects on epidemic intensity is lacking.

In the case of respiratory pathogens, epidemic intensity (which refers to the highest number of cases over time or the briefest time over which the most cases arise) changes according to the extent of indoor crowding and other geographic factors. The intensity is reduced when incidence occurs evenly over weeks and is increased when incidence is greater on certain days.

Wherever the location, a greater epidemic intensity results in greater strains on public health systems.

Data on Chinese cities provides a valuable opportunity

There is a plethora of comprehensive epidemiological time-series data available for China across various geographic contexts, providing a valuable opportunity to assess the factors that influence the intensity of local epidemics.

Now, Moritz Kraemer (Computational Epidemiology Lab, Boston Children’s Hospital) and colleagues have used data available for cities in China, along with population and climate data, local mobility data, and outbreak response data to establish which factors influence local transmission.

They aggregated daily data available for individual prefectures that were found in government reports of the incidence of COVID-19.

Study findings

The distribution and pattern of disease varied among prefectures, with incidence rapidly rising in some and quickly falling in others and some experiencing more prolonged epidemics than others.

The team estimated the epidemic intensity and calculated the “mean crowding”  (distribution of population density) for each prefecture, as well as calculating mean daily temperature and humidity.

Regression analysis showed that epidemic intensity negatively correlated with mean crowding and varied significantly between different locations. The researchers propose that the reason more crowding correlated with less epidemic intensity is that “crowding enables more widespread and sustained transmission between households leading incidence to be more widely distributed in time.”

Population size and mean temperature and humidity had significant but smaller effects.  

Multivariate analysis revealed that peak incidence of COVID-19 correlated with epidemic intensity (cities, where the intensity was higher, had more cases at incidence peak).

However, total incidence, was greater in cities with a lower epidemic intensity, which the authors say is intuitive, since more crowded locations experience epidemics of longer duration that affect a greater number of people.  

Measures may need to be more strictly enforced

“This suggests that measures taken to mitigate the epidemic may need to be enforced more strictly in smaller cities to lower the peak incidence (flatten the curve) but conversely may not need to be implemented as long,” writes the team.

Furthermore, the findings suggest that a lower overall incidence in small cities increases the risk of disease resurgence as a result of lower herd immunity, say Kraemer and colleagues.

“There is urgent need to collect serological evidence to provide a full picture of attack rates across the world,” they write.

Next, the team then applied their model based on epidemic intensity among cities in China, to cities across the globe. According to the authors, this revealed that “small inland cities in sub-Saharan Africa had high predicted epidemic intensity and may be particularly prone to experience large surge capacity in the public health system.”

It also revealed that for coastal cities, the predicted intensity tended to be lower, and the predicted epidemic was generally larger and longer-lasting.

To explore why outbreaks in crowded cities may be of lower intensity, the team built population models where people had a lot of contact within their household, less contact with people outside of their households, and comparatively rare contact with others in the same prefecture.

“Assumptions are consistent with reports that the majority of onward transmission occurred in households,” writes the team.

Further analysis revealed that sparse prefectures tended to have outbreaks that were shorter in duration but more intense and confined to particular neighborhoods. In contrast, crowded prefectures tended to have more prolonged, less intense outbreaks that moved between more connected neighborhoods.

“These outbreaks had larger final size than those in less-crowded areas, which likely is related to large overdispersion in the reproduction number of COVID-19, where local outbreaks can reach their full potential due to the availability of contacts,” suggests the team.

Interventions need to focus on taking crowding into account

The researchers say their findings demonstrate that spatial context, particularly crowding, can increase the risk of more intense epidemics in more rural, less crowded areas and that non-pharmaceutical interventions implemented in cities across the world need to be considered within the context of crowding.

“Specifically, cities in sub-Saharan Africa have high predicted epidemic intensities that will likely overwhelm already stressed health care systems,” they warn.

Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

Kraemer M, et al. Crowding and the epidemic intensity of COVID-19 transmission. MedRxiv 2020. doi:

Sally Robertson

Written by

Sally Robertson

Sally first developed an interest in medical communications when she took on the role of Journal Development Editor for BioMed Central (BMC), after having graduated with a degree in biomedical science from Greenwich University.


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