Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

• Published in: PloS one Vol. 15; no. 11; p. e0241949
• Main Authors: Sewell, Daniel K., Miller, Aaron
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.11.2020; Public Library of Science (PLoS)
• Subjects: Age; Algorithms; Betacoronavirus - isolation & purification; Computer and Information Sciences; Computer applications; Control; Coronavirus Infections - diagnosis; Coronavirus Infections - epidemiology; Coronavirus Infections - prevention & control; Coronavirus Infections - virology; Coronaviruses; COVID-19; Decision making; Disease Outbreaks; Disease transmission; District of Columbia - epidemiology; Ebola virus; Engineering and Technology; Epidemic models; Epidemics; Epidemiology; Estimates; Evaluation; Health planning; Humans; Infectious diseases; Masks; Mathematical models; Medicine and Health Sciences; Mitigation; Models, Theoretical; Pandemics; Pandemics - prevention & control; Pneumonia, Viral - diagnosis; Pneumonia, Viral - epidemiology; Pneumonia, Viral - prevention & control; Pneumonia, Viral - virology; Population; Public health; Public health administration; Public policy; Quarantine; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Social distancing; Social Sciences; Software; Statistical analysis; Washington, D.C; Windows (intervals); District of Columbia; Washington, D.C; Washington DC; United States > US; Iowa City Iowa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0241949

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. R code for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.