When new diseases emerge, the public reaction ranges from mild interest to alarm and panic, accompanied by extreme behavior like hoarding of medical supplies and higher than usual visits to doctors and hospitals. Now, a new study describes a computer model that predicts the social reaction to disease outbreaks.
The public reaction to a disease outbreak can sometimes be a bigger problem for health authorities than the disease itself, and tools are needed that help to anticipate and manage overreaction.
The new model is the work of a team that includes Marta Gonzalez, an assistant professor of civil and environmental engineering at Massachusetts Institute of Technology (MIT).
Prof. Gonzalez and colleagues write about the new model and how they tested it in the journal Interface.
To compile the model, the team used data from a range of sources, including hospitals and social media. The idea grew from other studies of how behavior spreads in social networks.
Prof. Gonzalez explains that the spread of information – and misinformation – about disease outbreaks has not been well studied, and it is not easy to get detailed information on panic reactions. Besides, she asks, “How do you quantify panic?”
One approach is to examine news reports, plus the messages people post on social media, and then compare that to the information in hospital records.
Sometimes the reaction to a disease outbreak can be counter-productive and do more harm than the disease itself.
For example, limiting travel and distribution of goods can cause economic damage. It can also lead to rioting, which can encourage the disease to spread even more.
Heightened public awareness of an epidemic can also lead people to go and see their doctor and visit hospitals for minor symptoms that they otherwise might not bother about. This can make it difficult for health care to reach the people that have the disease, say the researchers.
In the study, the team describes using the model to explore three disease outbreaks – the 2009 H1N1 swine flu pandemic as it spread in Mexico and Hong Kong, and the spread of SARS in 2003 in Hong Kong.
They showed how their model correctly predicted the public response to those outbreaks and that it was often out of proportion to the actual risk.
The results suggest that in general, the attention that a rare or unusual disease receives far exceeds that warranted by its actual risk.
For example, there was a much stronger public response to the SARS outbreak in Hong Kong than there was to the H1N1 swine flu, even though the rate of infection in the swine flu outbreak was hundreds of times greater.
Although they did not analyze our response to the current Ebola outbreak in West Africa, Prof. Gonzalez suggests that in this case also, the social response “is just not justified by the extent of the disease.”
The team has now embarked on a study of the Ebola situation. Prof. Gonazalez says she hopes in the future it will be possible to anticipate and counteract the effects of bad social and economic consequences of overreaction.
Although media coverage can sometimes spread panic, when it is the right kind of information, it can do the opposite, she notes.
Olivia Woolley Meza, a professor of computational social science at Eidgenössische Technische Hochschule (ETH) in Zurich and who was not involved in the study, says we need to transform how we think about and control disease.
With a “good understanding of the social response to epidemics, policies that fail to control the spread of disease due to counterproductive but foreseeable social responses can be avoided,” she adds.
Remarking on the new model, she notes that thanks to clever use of comparative studies to overcome the problem of the lack of data that usually thwarts such attempts “This paper stands out from others that have so far approached similar questions.”
The Defense Threat Reduction Agency helped to fund the study.
Meanwhile, Medical News Today learned how a team working on an epidemic model says Ebola in Liberia could end by June if current high rates of hospitalization and surveillance continue.