Part 5 -- Why we need locally/nationally interdependent models to successfully exit COVID-19 lockdown
|FIGURE 1: Chart for all major regions in England|
|FIGURE 2: Comparison of different authority districts in the North East of England|
|FIGURE 3: Map showing COVID-19 cumulative cases by major authority districts in England|
- how the environment-ecology of an area facilitates or hinders the spread of a disease (rural, urban, suburban, etc).
- how various social determinants influence a disease's impact, including inequality, population density, access to public health resources, racism and discrimination and also social deprivation -- as modelled, for example, by various multiple deprivation indices (Figure 4 below).
- the capacity for a particular place to respond to a public health crisis, as in the case of access to the resources needed for dealing with COVID-19, from critical supplies needed for hospitals and care homes to plans for social distancing and businesses reopening to managing the mental health aspects of sheltering people in place.
These, then, are my general recommendations -- and ones my colleagues in the COVID-19 Community Health and Social Care Modelling Team would agree with. Again, we do not pretend to have the only or best answer to the current situation; and, despite the need for regional modelling, we still support the importance of national level modelling and a national strategy. But, to reiterate our point, if we are to take the next step in this worldwide modelling challenge, we need a more diverse set of models that are more sensitive to and emerge out of not just the national, but also the local and the complexities of its socio-ecological context. Looking to the near future, such a modelling-public-policy dashboard is most likely to become even more important, as COVID-19 is probably the first in a series of global social and health problems we are about to face.
About the Durham COVID-19 Modelling team
The COVID-19 Community Health and Social Care Modelling Team is
under the guidance of our respective Executive Deans, Jacqui Ramagge
(Science) and Charlotte Clarke (Social Sciences and Health) as part of
the contribution of the Wolfson Research Institute for Health and Wellbeing and the Institute of Data Sciences to the University’s
Health@Durham strategy, as well as supported by the Research and Innovation Services, Marketing and Communications and CECAN (Centre for the study of Complexity Across the Nexus). The team is led by Dr Camila Caiado and Professor Brian Castellani, with the purpose of creating a
series of tools and dashboards that Trusts and Councils can use to help
support decision and planning accordingly. We would also like to
acknowledge the outstanding contribution of Dr Jennifer Badham, Dr Peter Barbrook-Johnson, Professor Amanda Ellison, Dr Andrew Iskauskas, Dr Rachel Oughton, Dr Corey Schimpf and Dr
Part 4 -- Social Networks and the Coronavirus: The Importance of Complexity Science for Public Health
The current post will review the utility of complex network modelling for infectious diseases and, more specifically, the model by Vespignani and colleagues at Northeastern University in the States.
The message is this: the causes and cures of our health are largely a public matter, and in complex and nuanced ways that we regularly (and perhaps purposely) fail to understand. Equally important -- from the negative impact poverty has on wellbeing to ensuring citizens equitable access to healthcare -- these causes and cures are primarily social determinants and social solutions.
The hidden influence of social networks
A recent study reported that among people who carried a single copy of the high- risk allele for the FTO gene, which is associated with fat mass and obesity, the risk of obesity increased by 30%. The risk of obesity increased by 67% among people who carried two alleles, and on average they gained 3.0 kg (6.6 lb) or more.1 Given that approximately one sixth of the population of European descent is homozygous for this allele, this link between the FTO gene and obesity appears to be one of the strongest genotype–phenotype associations detected by modern genome-screening techniques” (p. 404).
That obesity has a genetic component is not surprising: researchers have long known that it often runs in families. In this issue of the Journal, Christakis and Fowler suggest that friends have an even more important effect on a person’s risk of obesity than genes do.
The authors reconstructed a social network showing the ties between friends, neighbors, spouses, and family members among participants of the Framingham Heart Study, making use of the fact that the participants had been asked to name their friends to facilitate follow-up in the study. The authors observed that when two persons perceived each other as friends, if one friend became obese during a given time interval, the other friend’s chances of following suit increased by 171%. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40%. The results of this study also indicate that obesity is clustered in communities. For example, the risk that the friend of a friend of an obese person would be obese was about 20% higher in the observed network than in a random network; this effect vanished only by the fourth degree of separation.
- Vertex or a node: A fundamental unit of a network represented usually by a dot.
- Edge or a link: A line connecting vertices.
- Directed or undirected edges: An edge is directed is if it runs only in one direction (such as in a one-way road between two points), and undirected if it runs in both directions (Newman 2003, p. 173). A network in which all edges are directed is referred to as a directed network or graph.
- Neighborhood of a node i in a graph is simply defined as the set of all nodes that the node i is connected to. The usual convention is to assume that i is not connected to itself i.e., avoid loops.
- Degree: The number of connections (edges) a vertex has. For a directed network, one wants to know the direction of those connections. Those connections going out are called ’out-degree,’ and those coming in are called ’in-degree.’
- Component: The component to which a vertex belongs is the set of all vertices that can be reached from that vertex using existing edges in the network. ’For a directed graph, there is an in-component and an out-component, which are the sets of vertices from which the vertex can be reached and which can be reached from it,’ respectively (Newman 2003, p. 173).
- Geodesic path: The geodesic path refers to shortest route in the network from one vertex to another.
- Diameter: ’is the length of the longest geodesic path in a network’ (Newman 2003, p. 173).
If you click on the image to the right -->, it will allow you to run an infectious disease model on a social network. To run the model hit SETUP and then GO.
THEY STATE: Within each sub-population, the human-to-human transmission of COVID-19 is modeled using a compartmental representation of the disease where individuals can occupy one of the following states: Susceptible (S), Latent (L), Infectious (I) and Removed (R). Susceptible individuals can acquire the virus through contacts with individuals in the infectious compartment, and become latent, meaning they are infected but cannot transmit the infection yet.