How to really flatten the coronavirus curve
Professors at KAUST are employing differential equations and machine learning to help understand what’s coming next in the Covid-19 pandemic.
As countries around the world work to “flatten the curve,” a reference to slowing the progression of Covid-19 cases, David Ketcheson is using mathematical modeling to understand how transmission is playing out.
“I study things like waves,” says Ketcheson. “Of course, waves are made up of many tiny water molecules, and we cannot predict the movement of each of those molecules. But we can very accurately predict the motion of the whole wave.”
Ketcheson describes the evolution of highly complex systems through differential equations, something he brought to bear to help explain the progression of SARS-CoV-2, the strain of coronavirus currently infecting millions of people worldwide, resulting in the disease called Covid-19.
Ketcheson is an associate professor of applied mathematics and computational science at the King Abdullah University of Science and Technology (KAUST). He is not an epidemiologist but, with historical data and mathematical heft, he has some powerful points to make about the current Covid-19 situation.
“While we can’t predict the actions of individual people, in a large population, we can pretty accurately predict the overall spread,” says Ketcheson.
In late February, he found himself explaining to relatives and colleagues how what is now a global pandemic might progress. By mid-March, he had written a series of articles explaining, in layman’s terms, the SIR model, a classical differential equation that breaks people into three groups—susceptible, infected, and recovering—for the purposes of modeling the spread of infectious disease.
What Ketcheson explained in those articles is rapidly playing out—the disease is spreading at an exponential rate. At a time when Johns Hopkins is reporting that over 4.6 million cases have been confirmed worldwide, Ketcheson sees a world where many more are most likely already infected, even if many are not showing symptoms.
“We can’t predict the actions of individual people, but—as with a wave—we can pretty accurately predict the overall spread.”
“The model has been powerfully predictive. In the absence of interventions, the doubling time of the disease would be between three and four days,” says Ketcheson.
Early published estimates of the basic reproduction number for Covid-19 were around two to 2.5, but Ketcheson’s analysis found the data to be consistent with a reproduction number as high as five. More recent analyses by other experts have consistently suggested the number is significantly higher than originally thought. The reproduction rate is the number of people, on average, that an infected person will pass on the virus to—further demonstrating the need for social distancing, and in the case of high-risk individuals, isolation.
“Flattening the curve is all about reducing that reproduction number, reducing the average number of individuals that people come in contact with when they are sick,” says Ketcheson. “And, by reducing that, we slow down the exponential growth, so it takes longer to reach the peak, but we also reduce the height of that peak.”
The individual molecules in Ketcheson’s wave are vitally important—they are friends, colleagues and loved ones. But the individual experiences of people become less helpful as we try to gain clarity about how big a wave Covid-19 is likely to be, and how long it might take before we start to come down from the peak.
KETCHESON IS PART of a global academic collaboration that seeks to make powerful data accessible while encouraging happy accidents—the kinds of discoveries that come from multidisciplinary environments, where users turn disparate kinds of data into powerful insights. The team—made up of KAUST faculty from marine science, computer science, mathematics, genomics, and more—looks to publish a dashboard that does both.
One example might be the combination of some of Ketcheson’s data with changes in population sentiment. KAUST Associate Professor Xiangliang Zhang and her team are collecting data from millions of social media posts to understand trends in social sentiment throughout the pandemic. Their data—alongside that of colleagues and collaborators from around the world—could help inform public health interventions, institutional responses, and more.
Zhang works on flattening the “emotional curve,”and focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people’s interests.
“People are sharing how they feel on social media. We can use data from the tweets to help predict outbreaks.”
When the coronavirus outbreak started, she planned to launch a new project to analyze Covid-19 posts on Twitter. Her proposal immediately received strong support from KAUST Professors Xin Gao and Takashi Gojobori, who lead the University’s Computational Bioscience Research Center (CBRC), of which Zhang is a member. They quickly formed a team of over 10 people in the CBRC working on data collection and machine-learning models in different languages.
During these exceptional times marked by the coronavirus outbreak, the team’s research efforts have proved particularly useful to track and monitor people’s emotions.
“Amid this extraordinary crisis we are facing now, people may share how they feel on social media networks,” Zhang stated. “I want to help all the people understand how other people are feeling through sentimental analysis or emotional analysis on the Twitter platform.”
Although Zhang doesn’t work in the medical or biological fields, she uses machine learning to analyze the text-based data shared on social media by millions of Twitter users who use hashtags like #coronavirus or #covid19. Analysis of the data provides valuable information that can be used by governments and other organizations managing the Covid-19 pandemic.
Zhang cited an example of tweets shared by a medical doctor in Egypt last month that indicated many people were showing symptoms. He lacked testing material to confirm diagnoses, but the information could be used to help predict a localized outbreak. “I want to use the data from the tweets to help predict outbreaks,” says Zhang.
Twitter data analysis can be used as an early warning system—an additional tool in our arsenal in the global efforts to flatten the curve. Zhang was approached by Carlos Duarte, KAUST professor of marine science from the university’s Red Sea Research Center, to contribute her sentiment-analysis data to a global dashboard.
“We want to use color to demonstrate how people are feeling,” she says. “For example, blue means calm—denoting that users are not very anxious or panicked. Red means people are fearful, scared, or have negative feelings. We can demonstrate different portions of these emotions in different regions and different countries.”
Because tweets can be geolocated, the color-coded heat maps on the dashboard can be geographically accurate. Interestingly, sentiment data collected by collaborators in China currently shows that people have started to calm down. “We can show every day in different regions and countries how people’s emotions are changing,” says Zhang.
“We can demonstrate, for example, in China, Singapore, Japan, Korea and Hong Kong, that people’s emotions were showing up as very red one month ago, but we have more and more blue recently. We expect to have some similar
trends in all the other regions.”
Zhang and her team of researchers are currently focusing on English and Arabic tweets, but they are considering adding more widely used languages in the future, such as French and Spanish. To further refine the data, Zhang also plans to work with sociologists and demographers, as these individuals can provide more layered and textured explanations underlying the collected data.
“Basically, we think that the data can tell us something. We can build data-rich models to do the technical analysis, but we want to understand the reasons behind what we get. We need help from others,” says Zhang.
For more information, visit KAUST online.
Is the corona drug already out there?
Unpicking the genetic structure of the virus could help pinpoint existing medicines to treat patients.
The search is on for a coronavirus vaccine—but a stopgap solution could be to use existing drugs to fight the pandemic.
That is the hope of researchers at KAUST, who are focusing on the genetic structure and topography of SARS-CoV-2—the virus that causes Covid-19 disease. They believe this could identify key genes that could be used for detection of the virus, and to then propose readily available drugs to help patients whilst a dedicated vaccine is developed and approved.
“In the KAUST Computational Bioscience Research Center (CBRC), we have expertise in analyzing genomic data in a synergetic and collaborative way,” says Distinguished Professor Takashi Gojobri, acting director of the CBRC. “We have the resources—computer clusters, specialized analytic, and visualization software—and the expertise that is being applied to this global challenge.”
Vaccines are essential to helping stop the spread of viruses through the development of “herd immunity,” but can take months or years to develop and test before becoming available. With this in mind, KAUST scientists are focusing their efforts on finding key genes that could be used for both detection and treatment of Covid-19 where people are already infected.
“I have been leading the development of the KAUST metagenome analysis platform (KMAP),” says Intikhab Alam, senior bioinformatician in the CBRC. “It is a platform for analysis of massive sequence data. KMAP can deal with data from all types of organisms, from viruses to bacteria, to animals and plants, that is collected from moderate and extreme aquatic and terrestrial environments.”
The emergence of SARS-CoV-2 in late 2019, and the subsequent publishing of its genome sequence data, provided an opportunity for Alam to use KMAP in conjunction with CBRC computational resources to begin investigating the genetic structure of the virus in detail. Their work is already available as an open research tool.
“We are using the university’s supercomputer and specially designed software to not only compare and analyze coronaviruses but also to scan billions of environmental samples for traces of SARS-CoV-2,” says Alam.
“This information will enable us to develop a concept and system to identify available drugs that are already approved for human use that can be repurposed to attack a new disease—in this case, Covid-19. The development and approval of new drugs is likely to take time, so if we can reuse existing drugs, we can begin benefiting Covid-19 patients immediately and even hope to save lives.”
There’s a map for that
Computer modeling can play a key role in developing a surveillance system to unlock the secrets of Covid-19.
Where will the next Covid-19 hotspot be, and which health care authorities will need to scramble to provide extra beds and ventilators? It’s almost impossible to say with certainty—but computer modeling can play a pivotal role in predicting the course of the pandemic.
That’s the ultimate aim of a multidisciplinary KAUST research team that is creating a coronavirus dashboard showing indicators such as the number of confirmed cases, deaths, recovered cases, and health care facilities, as well as making forecasts.
“This dashboard will be accessible to public health authorities [and] also to the general public,” says Paula Moraga, who is part of the team.
“Public health authorities will be able to see how their regions are doing and how they need to plan their response to this epidemic, while the general public will get a sense of what is the real situation of the disease. They will be able to modify their behavior and take measures to protect themselves.”
Moraga previously worked on projects examining malaria in Africa and leptospirosis in Brazil, and the models she develops rely on in-depth knowledge about each disease.
“With malaria, for example, we know that it’s transmitted by mosquitoes,” says Moraga. “So, if some regions have a high density of mosquitoes, are close to bodies of water or have a lot of humidity, we know that we are going to probably see a high risk of this disease.”
In many places, malaria and leptospirosis transmission is also seasonal, peaking after the rainy season. Covid-19 is different.
“We don’t know much about it; we don’t know if it is affected by temperature or humidity, or if it has seasonal patterns—so we cannot use this information in the model,” says Moraga. “But we know that this is a disease transmitted through close contact with infected persons or contaminated surfaces, so if people come into contact, their probability of infection will be higher.”
This is where mobility data can provide valuable insights. “We have been in contact with cellphone companies who can provide us with data about a population’s movement in a region,” Moraga explains. “We can then include this information in the model to predict the number of cases in the following days and weeks, and to plan the resources we are going to need, including number of beds, ventilators and medical staff.
“[Cellphone] information is very sensitive and we respect people’s privacy. So, while the cellphone companies have exact coordinates of the movement, we will get aggregated data only.” Moraga is currently a lecturer in statistics at the University of Bath in the UK, but will join KAUST in September as a lecturer in statistics for public health.
Although traditional surveillance systems have their place, they have a limitation: time. From the point that a person gets the disease, goes to the doctor, and does required laboratory tests and that information enters the system, the process may take several weeks.
“This information is not useful for taking action in real time,” says Moraga. “But we now have access to digital data—for example, Google searches, where people search about treatment of the condition, or social media, where people speak about their symptoms. This information is not produced for epidemiological research, but we can use it to understand disease activity levels in real time.”