EPI’s COVID-19 forecast model: May 5, 2020

A new COVID-19 forecast model by an international team of researchers shows that robust testing, contact tracing, and household quarantining could offset a second pandemic wave once social distancing rules are relaxed. University of Florida professors of biostatistics Natalie Dean and Ira Longini, of UF’s College of Public Health and Health Professions, College of Medicine and the Emerging Pathogens Institute, contributed to the work.

Without herd immunity or a vaccine, there are many susceptible people who could become infected if social distancing measures are lifted too quickly, the model shows. But if testing, contact tracing, and quarantine measures are carried out systematically and rigorously after social distancing measures are relaxed, then infections could be kept low enough to be manageable by health care systems.

The team combined data from mobile phones with demographic and U.S. Census data to design a hypothetical data-driven agent-based study model. They then used this to analyze how the epidemic may change over time under different mitigation scenarios, and to measure the effectiveness of social distancing interventions and what happens after they are lifted.

“Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system,” the authors write.

Read the full paper on the MOBS Lab website.

Figure 1: model components

A diagram that showcases the model components for Covid-19.
Figure 1: model components. Sourced from the paper titled, Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic.

Panel a is a schematic illustration of the weighted multilayer synthetic population built from mobility data in the metropolitan area of Boston. The agent-based system is made up by around 64,000 adults and 21,000 children, whose geographical distributions are shown in panel b. Nodes are connected by more than 5million weighted edges. Community layers (that include workplaces), are further classified into categories according to Foursquare’s taxonomy of places. Panel c displays the compartmental model used to describe the natural history of the disease as well as the transition rates between the different states. Specifically, we consider Susceptible(S), Latent asymptomatic (LA), Latent symptomatic (LS), Pre-symptomatic (PS), Infectious asymptomatic (IA), Infectious symptomatic (IS), Hospitalised (H), Hospitalized in intensive care (ICU) and Recovered (R) individuals. More details of the model and the transitions between compartments are provided in Methods and the SM.

Figure 2: impact of COVID-19 under different scenarios

A diagram of nine different line graphs that chart the impact of Covid-19 under different scenarios.
Figure 2: impact of COVID-19 under different scenarios. Sourced from the paper titled, Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic.

Evolution of the number of new cases (a, d, g), the outbreak size (b, e, h) and the effective reproductive number (c, f, i) as a function of time in each situation studied. Results of the SARS-CoV-2 transmission dynamics are shown for the unmitigated scenario (top panels a-c), and the two social distancing interventions considered, LIFT (d-f) and LET scenarios (g-h). In both cases, we considered the closure of schools and non-essential places for 8 weeks. This is the strictest lock-down period, which is followed by a partial lifting of the stay-at-home policy whose duration is set to 4 weeks. During the partial lifting, all places in the community layer are open except mass-gathering locations (restaurants, theaters, etc, see SM). Finally, a full reopening takes place after the period of partial lifting ends (relevant events are marked with the vertical lines). Panels d-f consider that no other measures are adopted concurrently to the lifting of the restrictions, whereas the results in panels g-i have been obtained when the reopening is accompanied by an active policy consisting of testing the symptomatic individuals, home isolating them, and quarantining their household and the households of a fraction of their contacts, as indicated in the legend of the bottom panels. Note that the vertical scales of panels a, d, and g are not the same and that both the number of new cases and total cases are per 1,000 inhabitants. In all panels the solid line represents the average over 10,000 simulations and the shaded region the 95% C.I.

*Natalie Dean is no longer a faculty member at the University of Florida and no longer a member within the Emerging Pathogens Institute*

Ira M Longini

Ira M Longini

Phone: (352) 294-1938

Dr. Longini received his Ph.D. in Biometry at the University of Minnesota in 1977. He began his career with the International Center for Medical Research and Training and the Universidad del Valle in Cali, Colombia, where he worked on tropical infectious disease problems and taught courses in biomathematics. Following that, he was a professor biostatistics at the University of Michigan, Emory University and the University of Washington. He currently is a professor of biostatistics at the University of Florida and Director of the Center for Statistical and Quantitative Infectious Diseases (CSQUID), the Emerging Pathogens Institute, at the University of Florida. His research interests are in the area of stochastic processes applied to epidemiological problems. He has specialized in the mathematical and statistical theory of epidemics–a process that involves constructing and analyzing mathematical models of disease transmission, disease progression and the analysis of infectious disease data based on these models. He works extensively in the design and analysis of vaccine and infectious disease prevention trials and observational studies. Dr. Longini has worked on the analysis of epidemics of COVID-19, Ebola, influenza, HIV, tuberculosis, cholera, dengue fever, malaria, rhinovirus, rotavirus, measles and other infectious agents. Dr. Longini is also working with the Department of Health and Human Services, the World Health Organization, the CDC and other public health organizations on mathematical and statistical models for the control of a possible bioterrorist attack with an infectious agent such as smallpox, and other natural infectious disease threats such as COVID-19, pandemic influenza or another SARS-like infectious agent. Dr. Longini is author or coauthor of over 245 scientific papers and he has won a number of awards for excellence in research, including the Howard M. Temin Award in Epidemiology for “Scientific Excellence in the Fight against HIV/AIDS,” two CDC Statistical Science Awards for both “Best Theoretical and Applied Papers,” the CDC James H. Nakano Citation “for an outstanding scientific publications” the Science Magazine, one of the top 10 “Breakthrough of the Year” for 2015, Guinea Ebola ring vaccination trial, the Aspen Institute Italia Award for scientific research and collaboration between Italy and the United States, 2016, and the David A. Paulus Lifetime Achievement Award, College of Medicine, University of Florida. April 25, 2022. He is a Fellow of the American Statistical Association and a Fellow of the American Association for the Advancement of Science. Dr. Longini has Erdős number = 3.