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Wannier SR, Worden L, Hoff NA, Amezcua E, Selo B, Sinai C, Mossoko M, Njoloko B, Okitolonda-Wemakoy E, Mbala-Kingebeni P, Ahuka-Mundeke S, Muyembe-Tamfum JJ, Richardson ET, Rutherford GW, Jones JH, Lietman TM, Rimoin AW, Porco TC, Kelly JD. Estimating the impact of violent events on transmission in Ebola virus disease outbreak, Democratic Republic of the Congo, 2018-2019. Epidemics 2019; 28:100353. [PMID: 31378584 PMCID: PMC7363034 DOI: 10.1016/j.epidem.2019.100353] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/22/2019] [Accepted: 07/09/2019] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION As of April 2019, the current Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) is occurring in a longstanding conflict zone and has become the second largest EVD outbreak in history. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. Here, we use spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak. METHODS We collected daily EVD case counts from DRC Ministry of Health. A time-varying indicator of recent violence in each health zone was derived from events documented in the WHO situation reports. We used the Wallinga-Teunis technique to estimate the reproduction number R for each case by day per zone in the 2018-2019 outbreak. We fit an exponentially decaying curve to estimates of R overall and by health zone, for comparison to past outbreaks. RESULTS As of 16 April 2019, the mean overall R for the entire outbreak was 1.11. We found evidence of an increase in the estimated transmission rates in health zones with recently reported violent events versus those without (p = 0.008). The average R was estimated as between 0.61 and 0.86 in regions not affected by recent violent events, and between 1.01 and 1.07 in zones affected by violent events within the last 21 days, leading to an increase in R between 0.17 and 0.53. Within zones with recent violent events, the mean estimated quenching rate was lower than for all past outbreaks except the 2013-2016 West African outbreak. CONCLUSION The difference in the estimated transmission rates between zones affected by recent violent events suggests that violent events are contributing to increased transmission and the ongoing nature of this outbreak.
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Affiliation(s)
- S Rae Wannier
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Lee Worden
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA
| | - Nicole A Hoff
- Department of Epidemiology, School of Public Health University of California, Los Angeles, CA, USA
| | - Eduardo Amezcua
- Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Bernice Selo
- Ministry of Health, Kinshasa, Democratic Republic of Congo
| | - Cyrus Sinai
- Department of Geography at University of North Carolina, Chapel Hill, NC, USA
| | | | - Bathe Njoloko
- Ministry of Health, Kinshasa, Democratic Republic of Congo
| | | | | | - Steve Ahuka-Mundeke
- Insitut National de Recherche Biomedicale, Kinshasa, Democratic Republic of Congo
| | | | | | - George W Rutherford
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA
| | - James H Jones
- Department of Earth System Science, Stanford University, Stanford, CA, USA; Woods Institute for the Environment, Stanford University, Stanford, CA, USA
| | - Thomas M Lietman
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Anne W Rimoin
- Department of Epidemiology, School of Public Health University of California, Los Angeles, CA, USA
| | - Travis C Porco
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - J Daniel Kelly
- Francis I. Proctor Foundation for Research in Ophthalmology, San Francisco, University of California, CA, USA; Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, USA.
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Abstract
Mathematical epidemiology is a well-recognized discipline to model infectious diseases. It also provides guidance for public health officials to limit outbreaks. Nevertheless, epidemics take societies by surprise every now and then, for example, when the Ebola virus epidemic raged seemingly unrestrained in Western Africa. We provide insight to this capricious character of nature by describing the epidemic as a natural process, i.e., a phenomenon governed by thermodynamics. Our account, based on statistical mechanics of open systems, clarifies that it is impossible to predict accurately epidemic courses because everything depends on everything else. Nonetheless, the thermodynamic theory yields a comprehensive and analytical view of the epidemic. The tenet subsumes various processes in a scale-free manner from the molecular to the societal levels. The holistic view accentuates overarching procedures in arresting and eradicating epidemics.
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Affiliation(s)
- Mikko Koivu-Jolma
- Department of Physics, University of Helsinki, Helsinki, FI-00014, Finland
| | - Arto Annila
- Department of Physics, University of Helsinki, Helsinki, FI-00014, Finland; Department of Biosciences, University of Helsinki, Helsinki, FI-00014, Finland.
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Ackley SF, Mayeda ER, Worden L, Enanoria WTA, Glymour MM, Porco TC. Compartmental Model Diagrams as Causal Representations in Relation to DAGs. EPIDEMIOLOGIC METHODS 2017; 6:20060007. [PMID: 30555771 PMCID: PMC6294476 DOI: 10.1515/em-2016-0007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.
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Affiliation(s)
- S F Ackley
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - E R Mayeda
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - L Worden
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - W T A Enanoria
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - M M Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - T C Porco
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
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