Coronavirus disease outbreak (COVID-19) -Photo by Markus Spiske for Unsplash

It is nowadays ascertained that the airline networks play a crucial role in the global spreading of emerging infectious diseases, especially airborne ones like the coronavirus responsible of the ongoing pandemic crisis. The Computational Epidemiology Lab at the Institute for Scientific Interchange (ISI), one of ICARUS’s Demonstrators, has been working together with international research partners since the beginning of the Covid-19 outbreak, in order to study the diffusion patterns and assist health agencies.

Starting in December 2019, Chinese health authorities have been closely monitoring a cluster of pneumonia cases in the city of Wuhan, in Hubei Province. It has been determined that the pathogen causing the viral pneumonia among affected individuals was a new coronavirus, originally called 2019-nCoV and then officially named SARS-CoV-2. As of January 17, 45 cases were detected and confirmed in the Hubei region, with 3 additional cases detected and confirmed in Japan and Thailand, but those numbers grow rapidly and as of January 29 over 6000 cases had been detected and confirmed in Mainland China, with 133 deaths, while internationally there were more than 60 additional cases detected and confirmed in Japan, Thailand, South Korea, Taiwan, United States, Vietnam, Singapore, France, Australia, Nepal, Malaysia, Canada, Cambodia, Sri Lanka, Germany, United Arab Emirates and Finland. At the time the source of the outbreak was still unknown, however investigations identified environmental samples that tested positive for the virus at the Huanan Seafood Wholesale Market in Wuhan city. Some of the most recent cases did not report exposure to animal markets, suggesting that human-to-human transmission was possible.

Considering the international threat that an outbreak of a novel virus like that could pose to the world, our research team had been monitoring daily the reporting since the beginning of the year, and in mid-January we started to regularly publish reports assessing the spreading of the infections outside China. The reports did provide a modeling analysis of the risk of dissemination of SARS-CoV-2 infections, and, by using the cases detected outside the seeding country, also provided estimates of the potential outbreak size in Wuhan over time. We used a model relying on (historical) airline transportation data, based on the Origin-Destination traffic flows available in the OAG database, aggregated at the specific time and spatial scales used by the Global Epidemic And Mobility model. Commuting flows are derived by the analysis and modeling of data for more than 5,000,000 commuting patterns among 78,000 administrative regions in five continents.

ICARUS Epidemic Assessment Epirisk Dashboard
Figure 1: EpiRisk Platform Dashboard

Using this approach, we were able to quickly assess the relative risk of case importation in other countries worldwide, and provide realistic estimates of the outbreak size in China. Reports were published until the end of January. No detailed stochastic simulations were feasible at the time, due to huge uncertainties about the disease characteristics and the epidemiological parameters. We had to make various assumptions, like that the travel probability was independent of age, risk of exposure, and specific location within the catchment area. The dashboard shown above [1] would let users simply estimate the probability of exporting a certain number of cases given the outbreak size.

The international spreading observed in the following weeks showed that those estimates, even though subject to many limitations, could catch with good approximation the actual diffusion patterns, strongly driven by the topology of human mobility networks. Once that oncoming data started to unveil some of the most relevant uncertainties about the disease characteristics (e.g. asymptomatic transmission, serial interval range, etc.), and reasonable assumptions could be made, we worked using the full machinery of our computational infrastructure to study the effect of travel restrictions on the spread of the novel coronavirus.

We used a global metapopulation disease transmission model to project the impact of mobility limitations on the national and international spread of the epidemic. The model was calibrated on the basis of internationally reported cases and showed that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February. Modelling results also indicate that sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

ICARUS disease incidence projections
Figure 2: Disease incidence projections in different scenarios

Those results were published on the Science magazine [2]: the picture above shows disease incidence projections in different scenarios depending on the transmissibility reduction due to various intervention strategies. Our team is in contact with several national and international public health institutions, to inform policy makers and contribute scientific expertise.

The next big step forward in modelling pandemic outbreaks, and in the development of powerful tools for pandemic preparedness, would be including detailed demographic structures for describing simulated populations and air travelling patterns. Important work is being done on the synthetic generation of age-stratified mixing patterns for the transmission of airborne infectious diseases [3], and being able to couple interaction patterns with accurate passengers distributions would significantly improve the quality of the results, allowing for more realistic predictions and evaluations of containment and mitigation scenarios.





Featured Image by Markus Spiske on Unsplash

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