When insight into the impact of an emerging infectious disease is limited, using statistical equations known as phenomenological models can be an accurate way of forecasting the disease’s transmission pattern, according to a recent study led by a mathematical epidemiology expert at the School of Public Health at Georgia State University.
[Photo: Dr. Gerardo Chowell]
During the study, researchers from the School of Public Health and other institutions analyzed daily counts of suspected Zika virus cases in the Antioquia region of Colombia, from January through April, to generate early forecasts of the Zika epidemic there. The equations used by the research team “provided forecasts within 22 percent of the actual epidemic size based on an assessment 30 days into the epidemic.”
Standard models that relied on early exponential growth dynamics were not able to capture the Zika virus’ early growth phase and did not accurately estimate the epidemic’s size, the authors stated. By tweaking the models to account for the possibility of slower growth in the early stages of the outbreak, researchers were able to generate more accurate forecasts than models that assumed an early exponential growth.
Antioquia is the second largest governmental unit in Colombia, with a population of about 6.3 million people. Its capital, Medellin, is the nation’s second-largest city. The Zika virus — declared an international public health concern by the World Health Organization in February — is characterized by symptoms that include fever and rash, and has been linked with severe birth defects, such as microcephaly, which is when a child is born with an abnormally small head.
The results of the study are published in PLOS Current Outbreaks in an article titled “Using Phenomenological Models to Characterize Transmissibility and Forecast Patterns and Final Burden of Zika Epidemics.” The study’s lead author is Dr. Gerardo Chowell, associate professor of epidemiology and biostatistics at the School of Public Health and research associate at the Fogarty International Center at the National Institutes of Health.
“[O]ur study suggests that in the absence of reliable information about transmission mechanisms of an emerging infection, simple phenomenological models can provide an early assessment of the potential scope of outbreaks in near real-time,” the authors noted.
In addition to Dr. Chowell, who is also a senior research fellow at the Fogarty International Center, the study’s authors included School of Public Health MPH graduate students Ms. Sushma Dahal and Ms. Amna Tariq; Dr. Alexandra Smirnova, with the department of mathematics and statistics at Georgia State University; Dr. Doracelly Hincapie-Palacio with the University of Antioquia, Medellin; Mr. Juan Ospina with EAFIT University, Medellin; Dr. Bruce Pell with Arizona State University; Dr. Seyed Moghadas with York University in Toronto; Dr. Lone Simonsen with the University of Copenhagen; and Dr. Cecile Viboud with the Fogarty International Center at NIH.