Kimberlyn Roosa

Kimberlyn Roosa
University of Tennessee | UTK · One Health Initiative

PhD in Public Health (Epidemiology)

About

27
Publications
12,870
Reads
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1,312
Citations
Introduction
I am a post-doctoral research associate with the One Health Initiative at University of Tennessee. In addition to research and outreach with the One Health Initiative, I work in the Fefferman Lab, which includes collaboration with the Department of Ecology and Evolutionary Biology, the Department of Mathematics, and the National Institute of Mathematical and Biological Synthesis (NIMBioS).
Additional affiliations
September 2020 - present
University of Tennessee
Position
  • PostDoc Position
August 2017 - August 2020
Georgia State University
Position
  • PhD Student
August 2016 - December 2016
Georgia State University
Position
  • Graduate Teaching Assistant
Description
  • Teaching assistant for Epidemiological Methods II
Education
August 2017 - August 2020
Georgia State University
Field of study
  • Public Health - Epidemiology
August 2015 - May 2017
Georgia State University
Field of study
  • Epidemiology
August 2011 - May 2015
University of South Carolina
Field of study
  • Mathematics

Publications

Publications (27)
Preprint
We analyze an ensemble of n -sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, includi...
Chapter
Mathematical models have been widely used to understand the dynamics of the ongoing coronavirus disease 2019 (COVID-19) pandemic as well as to predict future trends and assess intervention strategies. The asynchronicity of infection patterns during this pandemic illustrates the need for models that can capture dynamics beyond a single-peak trajecto...
Article
Full-text available
Background Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Mat...
Article
Full-text available
The 2018–2020 Ebola outbreak in the Democratic Republic of the Congo is the first to occur in an armed conflict zone. The resulting impact on population movement, treatment centres and surveillance has created an unprecedented challenge for real-time epidemic forecasting. Most standard mathematical models cannot capture the observed incidence traje...
Preprint
Full-text available
Mathematical models have been widely used to understand the dynamics of the ongoing coronavirus disease 2019 (COVID-19) pandemic as well as to predict future trends and assess intervention strategies. The asynchronicity of infection patterns during this pandemic illustrates the need for models that can capture dynamics beyond a single-peak trajecto...
Preprint
Full-text available
The 2018-20 Ebola outbreak in the Democratic Republic of the Congo is the first to occur in an armed conflict zone. The resulting impact on population movement, treatment centers, and surveillance has created an unprecedented challenge for real-time epidemic forecasting. Most standard mathematical models cannot capture the observed incidence trajec...
Preprint
Full-text available
After weeks under lockdown, metropolitan areas fighting the spread of COVID-19 aim to balance public health goals with social and economic standards for well-being. Mathematical models of disease transmission seeking to evaluate mitigation strategies must assess the possible impacts of social distancing, economic lockdowns and other measures. Howev...
Article
Full-text available
Background: As of March 31, 2020, the ongoing COVID-19 epidemic that started in China in December 2019 is now generating local transmission around the world. The geographic heterogeneity and associated intervention strategies highlight the need to monitor in real time the transmission potential of COVID-19. Singapore provides a unique case example...
Article
Full-text available
In China, the doubling time of the coronavirus disease epidemic by province increased during January 20-February 9, 2020. Doubling time estimates ranged from 1.4 (95% CI 1.2-2.0) days for Hunan Province to 3.1 (95% CI 2.1-4.8) days for Xinjiang Province. The estimate for Hubei Province was 2.5 (95% CI 2.4-2.6) days.
Preprint
We investigate how individual protective behaviors, different levels of testing, and isolation influence the transmission and control of the COVID-19 pandemic. Based on an SEIR-type model incorporating asymptomatic but infectious individuals (40%), we show that the pandemic may be readily controllable through a combination of testing, treatment if...
Article
Background The ongoing Coronavirus disease (COVID-19) pandemic has severely impacted the United States. In cases of infectious disease outbreak, forecasting models are often developed for resources utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that the majority of American women ch...
Preprint
Full-text available
Background: As of March 31, 2020 the ongoing COVID-19 epidemic that started in China in December 2019 is now generating local transmission around the world. The geographic heterogeneity and associated intervention strategies highlight the need to monitor in real time the transmission potential of COVID-19. Singapore provides a unique case example...
Article
Full-text available
The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epide...
Article
Full-text available
The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city...
Preprint
Full-text available
The initial cluster of severe pneumonia cases that triggered the 2019-nCoV epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The ongoing outbreak presents a challenge for modelers, as limited d...
Preprint
Full-text available
We analyzed the epidemic doubling time of the 2019-nCoV outbreak by province in mainland China. Mean doubling time ranged from 1.0 to 3.3 days, being 2.4 days for Hubei (January 20-February 2, 2020). Trajectory of increasing doubling time by province indicated social distancing measures slowed the epidemic with some success.
Article
Full-text available
Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approac...
Article
in the Democratic Republic of the Congo, has been exacerbated by deliberate attacks on healthcare workers despite vaccination efforts. Using a mathematical/statistical modelling framework, we present the quantified effective reproduction number (Rt) at national and regional levels as at 29 September. The weekly trend in Rt displays fluctuations whi...
Article
Full-text available
in the Democratic Republic of the Congo, has been exacerbated by deliberate attacks on healthcare workers despite vaccination efforts. Using a mathematical/statistical modelling framework, we present the quantified effective reproduction number (Rt) at national and regional levels as at 29 September. The weekly trend in Rt displays fluctuations whi...
Article
Full-text available
The Poisson distribution is commonly assumed as the error structure for count data; however, empirical data may exhibit greater variability than expected based on a given statistical model. Greater variability could point to model misspecification, such as missing crucial information about the epidemiology of the disease or changes in population be...
Article
Full-text available
On August 1, 2018, the Democratic Republic of Congo declared its 10th and largest outbreak of Ebola inflicting North Khivu and Ituri provinces. The spread of Ebola to Congolese urban centers along with deliberate attacks on the health care workers has hindered epidemiological surveillance activities, leading to substantial reporting delays. Reporti...
Article
Full-text available
Background Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability...
Article
Full-text available
We assess the relationship between epidemic size and the scaling of epidemic growth of Ebola epidemics at the level of administrative areas during the 2014–16 Ebola epidemic in West Africa. For this purpose, we quantify growth scaling parameters from the ascending phase of Ebola outbreaks comprising at least 7 weeks of epidemic growth. We then stud...

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