Human diseases spread over networks of contacts between individuals. Whether they are tracking the future spread of an epidemic, or determining where best to distribute a vaccine during an outbreak, today’s disease researchers depend on reliable computer models.
One of the ultimate goals is to understand how the topological structures of networks affect the dynamics upon them.
Recent advances in computational science and the increasing availability of real-world data are making it possible to develop realistic scenarios and real-time forecasts of the global spreading of emerging health threats.
I think it may be useful to divulge the work of some researchers who have developed a data-driven model based on complex networks.
The reference text of this type of approach is “Charting the Next Pandemic: Modeling Infectious Disease Spreading in the Data Science Age” (Authors: Pastore y Piontti, A., Perra, N., Rossi, L., Samay, N., Vespignani, A.).
At Northeastern’s Laboratory for the modeling of biological and socio-technical systems, the Director Vespignani and his colleagues have been analyzing networks to build more realistic models.
Vespignani’s Team researchers started from building a model based on the assumption that people mostly spend their time within their own social networks, and this behavior directly affects how an infection spreads in the environment. The so-called zero patient will more likely infect her or his family and close friends than stranger people living across town or far away.
In their recent paper “Measurability of the epidemic reproduction number in data-driven contact networks” published in Proceedings of the National Academy of Sciences they have gone a step further: it shows that not all networks are equal.
“We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data”.
How does the dynamics of an infectious disease depend on the structure of a population
“The reproductive number that we used in the past doesn’t work in realistic situations” said Vespignani. Populations have different household structures and different kinds of contact networks. Furthermore in some social contacts are stable which can preserve for a long time, while others are unstable which persist only for a short time.
“During different phases of the epidemic, you have different combinations of different kinds of contacts. You have to combine all that information together”. Ajelli said.
According to Vespignani and Ajelli’s new speculations the disease modellers should better rely on a different method to predict with higher precision the spread of epidemics by switching from usage of a single basic reproductive number to that of data from real outbreaks as they occur to recalculate on each day a new and more precise reproductive number.
Vespignani’s team found how their model was able to much better and more precisely follow the real growth and spread of the disease than previous models based on a single constant basic reproductive number.
On the other side, it has to be noted how the new proposed model is very specific to a given country and places with similar contact networks. To adapt this new model in order to be effectively used upon other areas, a detailed data set related to different societies from all over the world is needed.
I finally suggest reading the following article “The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak” of which I report here a short excerpt:
“the modeling study shows that additional travel limitations up to 90% of the traffic have a modest effect unless paired with public health interventions and behavioral changes that achieve a considerable reduction in the disease transmissibility”.
The results of this study provide data with potential uses for the definition of optimized containment schemes and mitigation policies that includes the local and international dimension of the COVID-19 epidemic.