• /
  • 7/12
Managing the Pandemic
Managing the Pandemic

Advancing Our Understanding of Pandemics and How to Control Them

Compiled by By Judy Gelman Myers and Bethany Augliere

As SARS-CoV-2, the virus that causes COVID-19, spawns new variants in its race across the globe, scientists at FAU are working to better understand how it’s transmitted, how

to design success into a clinical trial, and how different societies respond to a health emergency. Their research enhances global efforts to combat this pandemic and those that are sure to come.

eliminating covid-19
Photography by Alex Dolce


Necibe Tuncer, Ph.D.
Necibe Tuncer, Ph.D.

When determining if infectious diseases like the flu or COVID-19 could be eliminated by human behavior rather than pharmaceuticals, it’s the economy that might matter most, according to a recent study by FAU researchers.

The study, led by Necibe Tuncer, Ph.D., an associate professor in the department of mathematical sciences, Charles E. Schmidt College of Science, was recently published in the Journal of Biological Dynamics. Tuncer and collaborators compared two models, one that examined the impact of full social distancing
and another that incorporated an
economic component.

Results reveal that disease elimination might be possible, but it depends on the economy. According to the first model, if the entire population practices complete social distancing it’s possible to eliminate disease. However, that’s not realistic, according to the authors. The second model shows that if the economy is weaker than the social norms, elimination of the disease is only possible if the entire population practices complete social distancing. If the economy is stronger than the social norms, then elimination is possible with some portion of the population practicing complete social distancing at the expense of the economy.



Patrick Bernet, Ph.D.
Patrick Bernet, Ph.D.

For Florida communities that struggled with health and social challenges before the COVID-19 pandemic, things are even worse now due to a higher risk of infection and death, according to a recent study published in the journal Racial and Ethnic Health Disparities.

Lead author Patrick Bernet, Ph.D., associate professor in the College of Business, analyzed the Florida county COVID-19 infection and death counts reported through March 2021 and supplemented that data with socioeconomic characteristics and 2020 presidential results.

Results revealed that Florida counties with more Black residents had disproportionately higher COVID-19 infection and mortality rates. The disparities are even more pronounced in counties with larger Republican vote shares. Bernet said he hopes this data helps inform policy decisions that allocate resources where they will do the most good and customize messaging to improve protective measures, such as vaccination.

Janet Robishaw, Ph.D.
Janet Robishaw, Ph.D.


Beyond safe and effective vaccines, it’s possible that studying the genetic code of SARS-CoV-2, the virus that causes COVID-19, is also helpful to reduce serious illness and death, according to
FAU researchers.

All viruses have the ability to mutate as they spread through a population, leading to variants. Viruses with RNA as the genetic material, like influenza and SARS-CoV-2, can mutate faster than viruses with DNA.

In a new study, published in the journal Lancet, and led by first author, Janet D. Robishaw, Ph.D., senior associate dean for research and chair of the department of biomedical science in the Charles E. Schmidt College of Medicine, results show studying the genome means detecting different variants well before they spread. It also generally improves understanding of which variants are circulating, where, and their impact, according to the study.

Behnaz Ghoraani, Ph.D.
Behnaz Ghoraani, Ph.D.
Photography by Alex Dolce

Photography by Aleksandr_Vorobev


Early detection of a COVID-19 outbreak is important to save people’s lives and restart the economy. Since outbreaks depend to a large degree on people’s social behavior, a university-industry collaboration teamed scientists from FAU’s College of Engineering and Computer Science with researchers from LexisNexis, a global data analytics company. The team developed a deep-learning model combining data on people’s mobility — how much they move around by walking, cars or buses — with statistics on previous COVID-19 outbreaks, government policies and demographic information such as age. The results, recently published in Journal of Big Data, accurately predict county-level outbreaks in the U.S. two weeks in advance.

The findings show that when people move around, the virus reproduces more. Because an increase in mobility increases interactions between people, especially in areas with high population density, adding mobility data to forecasting models helps scientists estimate COVID-19 growth and evaluate the effectiveness of policies such as mask mandates, according to Behnaz Ghoraani, Ph.D., senior author of the publication, associate professor in the department of electrical engineering and computer science, and fellow of the FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE).

The study also demonstrates that the age of a population impacts the spread of the virus: average daily cases decrease in populations that have more retirees (65 and older) and increase with populations that have more young people (14 to 44).

Using this model to predict an outbreak of COVID-19 two weeks in advance helps manage this pandemic, as well as future pandemics, by ensuring that healthcare facilities are well prepared for what’s to come.


Clinical trials are necessary for determining the safety and efficacy of a drug, vaccine or device, but they are costly and time consuming, according to researchers in the College of Engineering and Computer Science. The scientists recently developed a computational model that can predict whether a clinical trial will succeed or be withdrawn due to a variety of factors, including insufficient enrollment, lack of funding/resources and business/sponsor decisions. The model can help clinicians design trials that optimize their efforts and reduce resources.

The researchers built a testbed of 4,441 COVID-19 trials from ClinicalTrial.gov with the objective of creating features that characterize successful COVID-19 clinical trial reports. Four types of features (statistics, keywords, drugs and embedding) were formulated to represent each clinical trial. The features characterize each clinical trial by considering clinical trial administration, eligibility criteria, clinical study design (e.g., whether placebo groups are involved), drug types, study keywords, as well as embedding features commonly used in state-of-the-art machine learning. Results show that keyword features are most informative for COVID-19 trial prediction, followed by drug features, statistics features, and embedding features. The study was recently published in PLOS ONE.

Xingquan “Hill” Zhu, Ph.D., senior author and a professor in the College of Engineering and Computer Science, points out that clinical trials involve a great deal of resources and time, including planning and recruiting human subjects. Being able to predict the likelihood of whether a trial might be terminated or not down the road will help stakeholders better manage their resources and procedures. Such computational approaches may even help society as a whole redirect efforts toward combating the global COVID-19 pandemic, he said.

Xingquan “Hill” Zhu, Ph.D.
Xingquan “Hill” Zhu, Ph.D.
Len Treviño, Ph. D
Len Treviño, Ph. D


Some nations have been more effective than others at curbing the spread of COVID-19 within their borders. To understand how some countries successfully protect the lives of their citizens during a pandemic, researchers at FAU’s School of Business, in collaboration with colleagues at other universities, combined insights from cross-cultural research, social psychology and public health literature to explain the factors that impact a nation’s response to
an epidemic.

The study analyzed COVID-19 case data for the first three months of infection in 107 nations, along with factors such as stringency of government policies and how quickly they were implemented. The study was recently published in the Journal of International Business Studies.

The researchers categorized countries according to whether they prioritize individual or collective behavior, the extent to which they accept unequal

distribution of power, status and authority (power distance), the extent to which they feel threatened by ambiguities (uncertainty avoidance), and whether they prioritize ego goals versus social goals (masculinity-femininity). The study showed that stringent government policies applied early on attenuated pandemic growth, and that this effect was more pronounced in collectivistic than individualistic nations, and in high rather than low power
distance nations.

Len TreviÑo, Ph.D., director of international business programs in the College of Business, said he believes that countries should provide clear messages early on regarding the potential mortality and morbidity of a pandemic, and that in order to have the intended impact, the focus and content of these messages should be tailored to the nation’s cultural context. Nevertheless, TreviÑo and his coauthors argue that all countries should attempt to unify their citizens by pointing out that adhering to pandemic control measures and a healthy economy go hand-in-hand.


Public health efforts to stop pandemics like COVID-19 depend heavily on predicting how they spread. Borivoje “Borko” Furht, Ph.D., a professor in the department of electrical engineering and computer science in FAU’s College of Engineering and Computer Science, and director of the National Science Foundation Research Center, and his team collaborated with data analytics company LexisNexis to create a mathematical algorithm that models and tracks the spread of COVID-19 from the county level to the global.

Using big data about personal relationships, health center locations and known mechanisms for spread of the disease, the model looks forward, predicting infection from a given patient into the community, and backward, tracing verified infections to a possible patient zero.

“Let’s say someone gets COVID. In a matter of 20 minutes, we can see the whole network around this person — neighbors, friends, employees — so we can immediately start tracking who they may be in contact with,” Furht said. “Our mathematical model immediately calculates how far anyone is from this infected person and the possibility that they might
get COVID.”

Furht, who is basing the COVID-19 algorithm on a previous successful model of Ebola, said the Global COVID-19 Spread Tracking project has been released as a publicly available open-source website.

Borivoje “Borko” Furht, Ph.D.
Borivoje “Borko” Furht, Ph.D.