COVID-19 Outbreak: Non-controllable factors that decide the spread of coronavirus
The coronavirus outbreak has affected countries differently. Though it is difficult to quantify the intensity of outbreak among the different countries, it is important to look into several intrinsic pre-outbreak factors that could have decided the course of the pandemic.
To emphasize, this article does not include controllable factors, such as government’s response to the virus — mitigation and suppression strategies that put in place; the article aims to do a deeper dive into factors that are intrinsic to a country and that the government has no control over.
- Population Density
China:
As illustrated by Figure 1, cities with very high population densities such as Shanghai, Beijing, Shenzhen, Tianjin, and Zhuhai have had far fewer confirmed cases per 10,000 people. We notice that the group of dense cities are also the wealthier ones (with bigger bubbles), making them more able to mobilize enough fiscal resources to cope with the coronavirus. This partly explained their low infection rates.
Higher densities, in some cases, can even be a blessing rather than a curse in fighting epidemics. Due to economies of scale, cities often need to meet a certain threshold of population density to offer higher-grade facilities and services to their residents. For instance, in dense urban areas where the coverage of high-speed internet and door-to-door delivery services are conveniently available at competitive prices, it is easier for residents to stay at home and avoid unnecessary contact with others.
United States:
Kolko’s analysis finds density to be significantly associated with Covid-19 deaths across U.S. counties. But density is not the only factor at play. His analysis also finds that Covid-19 death rates per capita are higher in counties with older populations and larger shares of minorities, and colder, wetter climates. It’s important to remember that this analysis only looks at the U.S., and in other parts of the world, denser cities have had more success controlling the spread.
But even in the U.S., it is not density in and of itself that seems to make cities susceptible, but the kind of density and the way it impacts daily work and living. That’s because places can be dense and still provide places for people to isolate and be socially distant. Simply put, there is a huge difference between rich dense places, where people can shelter in place, work remotely, and have all of their food and other needs delivered to them, and poor dense places, which push people out onto the streets, into stores and onto crowded transit with one another.
This density divide is vividly apparent in geographic breakdown of the virus across New York City: Covid-19 is hitting hardest not in uber-dense Manhattan but in the less-dense outer boroughs, like the Bronx, Queens, and even far less dense Staten Island.
The density that transmits the virus is when people are crammed together in multifamily, multi-generational households or in factories or frontline service work in close physical proximity to one another or the public. Such density is why the earlier 1918 flu pandemic ravaged the working-class neighborhoods of industrial centers of Pittsburgh and Philadelphia.
We’ll also want to zero in the differences in the kinds of work people do: the shares of the workforce that are able to engage in remote work versus the share of frontline workers in health care, delivery and grocery stores that are particularly vulnerable to the virus.
- Social Capital
We may find that some things we want to encourage in cities, like tight social bonds and civic capital, make them more vulnerable. “When it’s all said and done, we’re going to find that COVID was uniquely lethal to people with high social capital,” demographer Lyman Stone suggested on Twitter. Conversely, we may also find that some things people criticize cities for — like childlessness, relatively few children and low levels of families with kids — worked to protect them, for the simple reason that kids can be vectors for the disease spread.
- Age Distribution
There are a number of other factors in addition to density that merit closer attention as we continue to track the virus’s spread. Two obvious one are the age of population and pre-existing health conditions like smoking, obesity, diabetes and heart disease.
- Trust in governance / Type of government
In a divided nation like the U.S., it will be interesting to see to what degree political orientation matters: Blue states and cities have tended to move much quicker on social distancing that their Red counterparts.
- Culture/ Religion
We may also find that high levels of religiosity, where people gather in large groups to worship, or places with large shares of multi-generational families also have greater vulnerability.
- Population Mobility/ Geographical Proximity to Epicentre
To further investigate the possible spatial decay effect in the spread of the coronavirus, we made another plot using the infection rate against each city’s distance to Wuhan; see Figure 2 for details. It shows that the chances of infection decline as distance to Wuhan increases. Cities in Xinjiang province (e.g., Karamay, Urumqi, and Turpan) that are over 2,500 kilometers away from Wuhan, have extremely low coronavirus infection rates.
- Urbanization
The World Bank’s research on urban geography has found that the 3Ds — higher density, shorter distance, and fewer divisions (i.e., better market integration) — are essential for economic development. From an urban resilience point of view, the potential risks of public health emergencies associated with the 3Ds need to be managed carefully, because as urbanization continues, we will be living in a world where people are even closer to each other than before, both spatially and economically. Over time, ensuring well-designed institutions, high-quality infrastructure, and effective interventions (e.g., social distancing and lockdown to flatten the curve of disease transmission) will be the ingredients to making cities stronger against infectious diseases.
It is critically important to look closely at the uneven impact of the virus on poor and minority communities.
- Economic relations with outbreak regions
Cities with the highest coronavirus infection rates were those with relatively low population densities, in the range between 5,000 to 10,000 people per square kilometer. The higher infection rates can be attributed either to their strong economic connection with Wuhan — which is the case for Wenzhou, with over 180,000 people working in Wuhan despite the long distance between the two cities — or to their geographical proximity, which explains the situation in Xinyang, Zhumadian, Xinyu, and Yueyang, which are close to the provincial border of Hubei.
A couple factors that require further analysis are:
- Weather
- Pre-existing health conditions of population
- Country’s prior exposure to pandemics
- Types of Health Service Delivery Systems
- # Critical beds/ Million people
- # Staff/ Million people
- PPE Manufacturing Setup & Testing Capacity
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