1
|
Carlson CJ, Garnier R, Tiu A, Luby SP, Bansal S. Strategic vaccine stockpiles for regional epidemics of emerging viruses: A geospatial modeling framework. Vaccine 2024:S0264-410X(24)00690-X. [PMID: 38902187 DOI: 10.1016/j.vaccine.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
Multinational epidemics of emerging infectious diseases are increasingly common, due to anthropogenic pressure on ecosystems and the growing connectivity of human populations. Early and efficient vaccination can contain outbreaks and prevent mass mortality, but optimal vaccine stockpiling strategies are dependent on pathogen characteristics, reservoir ecology, and epidemic dynamics. Here, we model major regional outbreaks of Nipah virus and Middle East respiratory syndrome, and use these to develop a generalized framework for estimating vaccine stockpile needs based on spillover geography, spatially-heterogeneous healthcare capacity and spatially-distributed human mobility networks. Because outbreak sizes were highly skewed, we found that most outbreaks were readily contained (median stockpile estimate for MERS-CoV: 2,089 doses; Nipah: 1,882 doses), but the maximum estimated stockpile need in a highly unlikely large outbreak scenario was 2-3 orders of magnitude higher (MERS-CoV: ∼87,000 doses; Nipah ∼ 1.1 million doses). Sensitivity analysis revealed that stockpile needs were more dependent on basic epidemiological parameters (i.e., death and recovery rate) and healthcare availability than any uncertainty related to vaccine efficacy or deployment strategy. Our results highlight the value of descriptive epidemiology for real-world modeling applications, and suggest that stockpile allocation should consider ecological, epidemiological, and social dimensions of risk.
Collapse
Affiliation(s)
- Colin J Carlson
- Department of Biology, Georgetown University; Department of Epidemiology of Microbial Diseases, Yale University School of Public Health
| | | | - Andrew Tiu
- Department of Biology, Georgetown University
| | | | | |
Collapse
|
2
|
Deiner MS, Seitzman GD, Kaur G, McLeod SD, Chodosh J, Lietman TM, Porco TC. Sustained Reductions in Online Search Interest for Communicable Eye and Other Conditions During the COVID-19 Pandemic: Infodemiology Study. JMIR INFODEMIOLOGY 2022; 2:e31732. [PMID: 35320981 PMCID: PMC8931841 DOI: 10.2196/31732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/26/2022] [Accepted: 02/16/2022] [Indexed: 12/20/2022]
Abstract
Background In a prior study at the start of the pandemic, we reported reduced numbers of Google searches for the term “conjunctivitis” in the United States in March and April 2020 compared with prior years. As one explanation, we conjectured that reduced information-seeking may have resulted from social distancing reducing contagious conjunctivitis cases. Here, after 1 year of continued implementation of social distancing, we asked if there have been persistent reductions in searches for “conjunctivitis,” and similarly for other communicable disease terms, compared to control terms. Objective The aim of this study was to determine if reduction in searches in the United States for terms related to conjunctivitis and other common communicable diseases occurred in the spring-winter season of the COVID-19 pandemic, and to compare this outcome to searches for terms representing noncommunicable conditions, COVID-19, and to seasonality. Methods Weekly relative search frequency volume data from Google Trends for 68 search terms in English for the United States were obtained for the weeks of March 2011 through February 2021. Terms were classified a priori as 16 terms related to COVID-19, 29 terms representing communicable conditions, and 23 terms representing control noncommunicable conditions. To reduce bias, all analyses were performed while masked to term names, classifications, and locations. To test for the significance of changes during the pandemic, we detrended and compared postpandemic values to those expected based on prepandemic trends, per season, computing one- and two-sided P values. We then compared these P values between term groups using Wilcoxon rank-sum and Fisher exact tests to assess if non-COVID-19 terms representing communicable diseases were more likely to show significant reductions in searches in 2020-2021 than terms not representing such diseases. We also assessed any relationship between a term’s seasonality and a reduced search trend for the term in 2020-2021 seasons. P values were subjected to false discovery rate correction prior to reporting. Data were then unmasked. Results Terms representing conjunctivitis and other communicable conditions showed a sustained reduced search trend in the first 4 seasons of the 2020-2021 COVID-19 pandemic compared to prior years. In comparison, the search for noncommunicable condition terms was significantly less reduced (Wilcoxon and Fisher exact tests, P<.001; summer, autumn, winter). A significant correlation was also found between reduced search for a term in 2020-2021 and seasonality of that term (Theil-Sen, P<.001; summer, autumn, winter). Searches for COVID-19–related conditions were significantly elevated compared to those in prior years, and searches for influenza-related terms were significantly lower than those for prior years in winter 2020-2021 (P<.001). Conclusions We demonstrate the low-cost and unbiased use of online search data to study how a wide range of conditions may be affected by large-scale interventions or events such as social distancing during the COVID-19 pandemic. Our findings support emerging clinical evidence implicating social distancing and the COVID-19 pandemic in the reduction of communicable disease and on ocular conditions.
Collapse
Affiliation(s)
- Michael S Deiner
- Francis I Proctor Foundation University of California San Francisco San Francisco, CA United States.,Department of Ophthalmology University of California San Francisco San Francisco, CA United States
| | - Gerami D Seitzman
- Francis I Proctor Foundation University of California San Francisco San Francisco, CA United States.,Department of Ophthalmology University of California San Francisco San Francisco, CA United States
| | - Gurbani Kaur
- School of Medicine University of California San Francisco San Francisco, CA United States
| | - Stephen D McLeod
- Francis I Proctor Foundation University of California San Francisco San Francisco, CA United States.,Department of Ophthalmology University of California San Francisco San Francisco, CA United States
| | - James Chodosh
- Department of Ophthalmology Massachusetts Eye and Ear Harvard Medical School Boston, MA United States
| | - Thomas M Lietman
- Francis I Proctor Foundation University of California San Francisco San Francisco, CA United States.,Department of Ophthalmology University of California San Francisco San Francisco, CA United States.,Department of Epidemiology and Biostatistics Global Health Sciences University of California San Francisco San Francisco, CA United States
| | - Travis C Porco
- Francis I Proctor Foundation University of California San Francisco San Francisco, CA United States.,Department of Ophthalmology University of California San Francisco San Francisco, CA United States.,Department of Epidemiology and Biostatistics Global Health Sciences University of California San Francisco San Francisco, CA United States
| |
Collapse
|
3
|
Korneta P, Rostek K. The Impact of the SARS-CoV-19 Pandemic on the Global Gross Domestic Product. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5246. [PMID: 34069182 PMCID: PMC8155974 DOI: 10.3390/ijerph18105246] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/04/2021] [Accepted: 05/08/2021] [Indexed: 01/10/2023]
Abstract
The rapid, unexpected, and large-scale expansion of the SARS-CoV-19 pandemic has led to a global health and economy crisis. However, although the crisis itself is a worldwide phenomenon, there have been considerable differences between respective countries in terms of SARS-CoV-19 morbidities and fatalities as well as the GDP impact. The object of this paper was to study the influence of the SARS-CoV-19 pandemic on global gross domestic product. We analyzed data relating to 176 countries in the 11-month period from February 2020 to December 2020. We employed SARS-CoV-19 morbidity and fatality rates reported by different countries as proxies for the development of the pandemic. The analysis employed in our study was based on moving median and quartiles, Kendall tau-b coefficients, and multi-segment piecewise-linear approximation with Theil-Sen trend lines. In the study, we empirically confirmed and measured the negative impact of the SARS-CoV-19 pandemic on the respective national economies. The relationship between the pandemic and the economy is not uniform and depends on the extent of the pandemic's development. The more intense the pandemic, the more adaptive the economies of specific countries become.
Collapse
Affiliation(s)
- Piotr Korneta
- Faculty of Management, Warsaw University of Technology, 02-524 Warszawa, Poland;
| | | |
Collapse
|
4
|
Browning R, Sulem D, Mengersen K, Rivoirard V, Rousseau J. Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19. PLoS One 2021; 16:e0250015. [PMID: 33836020 PMCID: PMC8034752 DOI: 10.1371/journal.pone.0250015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/29/2021] [Indexed: 12/24/2022] Open
Abstract
Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by modelling daily mortality counts as distinct phases in the COVID-19 outbreak. We first consider the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We then explore subsequent phases with more recent data. Various countries that have been adversely affected by the epidemic are considered, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.
Collapse
Affiliation(s)
- Raiha Browning
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia
| | - Deborah Sulem
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia
| | | | - Judith Rousseau
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Ceremade, Université Paris-Dauphine, Paris, France
| |
Collapse
|
5
|
Kelly JD, Wannier SR, Sinai C, Moe CA, Hoff NA, Blumberg S, Selo B, Mossoko M, Chowell-Puente G, Jones JH, Okitolonda-Wemakoy E, Rutherford GW, Lietman TM, Muyembe-Tamfum JJ, Rimoin AW, Porco TC, Richardson ET. The Impact of Different Types of Violence on Ebola Virus Transmission During the 2018-2020 Outbreak in the Democratic Republic of the Congo. J Infect Dis 2020; 222:2021-2029. [PMID: 32255180 PMCID: PMC7661768 DOI: 10.1093/infdis/jiaa163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 04/05/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Our understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited. METHODS We used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests. Outcome was estimated daily reproduction number (Rt) by health zone. We fit linear time-series regression to model the relationship. RESULTS Average Rt was 1.06 (95% confidence interval [CI], 1.02-1.11). A mean of 2.92 violent events resulted in cumulative absolute increase in Rt of 0.10 (95% CI, .05-.15). More violent events increased EBOV transmission (P = .03). Considering violent events in the 95th percentile over a 21-day interval and its relative impact on Rt, Ebola-targeted events corresponded to Rt of 1.52 (95% CI, 1.30-1.74), while civilian-induced events corresponded to Rt of 1.43 (95% CI, 1.21-1.35). Untargeted events corresponded to Rt of 1.18 (95% CI, 1.02-1.35); among these, militia/political or ville morte events increased transmission. CONCLUSIONS Ebola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission.
Collapse
Affiliation(s)
- John Daniel Kelly
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
- F. I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA
- Institute of Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Rae Wannier
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
- F. I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA
| | - Cyrus Sinai
- Department of Geography, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Caitlin A Moe
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA
- Firearm Injury Policy and Research Program, Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington, USA
| | - Nicole A Hoff
- School of Public Health, University of California Los Angeles, Los Angeles, California, USA
| | - Seth Blumberg
- F. I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA
| | - Bernice Selo
- Ministry of Health, Kinshasa, Democratic Republic of Congo
| | | | - Gerardo Chowell-Puente
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA
| | - James Holland Jones
- Department of Earth Systems Science, Stanford University, Stanford, California, USA
| | | | - George W Rutherford
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
- Institute of Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas M Lietman
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | | | - Anne W Rimoin
- School of Public Health, University of California Los Angeles, Los Angeles, California, USA
| | - Travis C Porco
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
- F. I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA
| | - Eugene T Richardson
- Harvard Medical School, Boston, Massachusetts, USA
- Brigham and Women’s Hospital, Boston, Massachusetts, USA
| |
Collapse
|
6
|
Roosa K, Tariq A, Yan P, Hyman JM, Chowell G. Multi-model forecasts of the ongoing Ebola epidemic in the Democratic Republic of Congo, March-October 2019. J R Soc Interface 2020; 17:20200447. [PMID: 32842888 PMCID: PMC7482568 DOI: 10.1098/rsif.2020.0447] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
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 trajectory when it deviates from a traditional epidemic logistic curve. We fit seven dynamic models of increasing complexity to the incidence data published in the World Health Organization Situation Reports, after adjusting for reporting delays. These models include a simple logistic model, a Richards model, an endemic Richards model, a double logistic growth model, a multi-model approach and two sub-epidemic models. We analyse model fit to the data and compare real-time forecasts throughout the ongoing epidemic across 29 weeks from 11 March to 23 September 2019. We observe that the modest extensions presented allow for capturing a wide range of epidemic behaviour. The multi-model approach yields the most reliable forecasts on average for this application, and the presented extensions improve model flexibility and forecasting accuracy, even in the context of limited epidemiological data.
Collapse
Affiliation(s)
- Kimberlyn Roosa
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amna Tariq
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ping Yan
- Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Ottawa, Canada
| | - James M Hyman
- Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institute of Health, Bethesda, MD, USA
| |
Collapse
|
7
|
Pollett S, Johansson M, Biggerstaff M, Morton LC, Bazaco SL, Brett Major DM, Stewart-Ibarra AM, Pavlin JA, Mate S, Sippy R, Hartman LJ, Reich NG, Maljkovic Berry I, Chretien JP, Althouse BM, Myer D, Viboud C, Rivers C. Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action. Epidemics 2020; 33:100400. [PMID: 33130412 PMCID: PMC8667087 DOI: 10.1016/j.epidem.2020.100400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/24/2020] [Accepted: 06/25/2020] [Indexed: 01/26/2023] Open
Abstract
Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.
Collapse
Affiliation(s)
- Simon Pollett
- Viral Diseases Branch, Walter Reed Army Institute of Research, MD, USA.
| | - Michael Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, USA
| | | | - Lindsay C Morton
- Global Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; Cherokee Nation Strategic Programs, Tulsa, OK, USA; Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Sara L Bazaco
- Global Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; General Dynamics Information Technology, Falls Church, VA, USA
| | | | - Anna M Stewart-Ibarra
- Institute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, USA; InterAmerican Institute for Global Change Research (IAI), Montevideo, Department of Montevideo, Uruguay
| | - Julie A Pavlin
- National Academies of Sciences, Engineering, and Medicine, DC, USA
| | - Suzanne Mate
- Emerging Infectious Diseases Branch, Walter Reed Army Institute of Research, MD, USA
| | - Rachel Sippy
- Institute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, USA
| | - Laurie J Hartman
- Global Emerging Infections Surveillance, Armed Forces Health Surveillance Division, Silver Spring, MD, USA; Cherokee Nation Strategic Programs, Tulsa, OK, USA
| | | | | | | | - Benjamin M Althouse
- University of Washington, WA, USA; Institute for Disease Modeling, Bellevue, WA, USA; New Mexico State University, Las Cruces, NM, USA
| | - Diane Myer
- Johns Hopkins Center for Health Security, MD, USA
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, MD, USA
| | | |
Collapse
|
8
|
Rivers C, Pollett S, Viboud C. The opportunities and challenges of an Ebola modeling research coordination group. PLoS Negl Trop Dis 2020; 14:e0008158. [PMID: 32673319 PMCID: PMC7365411 DOI: 10.1371/journal.pntd.0008158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Caitlin Rivers
- Johns Hopkins Center for Health Security, Maryland, United States of America
| | - Simon Pollett
- Viral Diseases Branch, Walter Reed Army Institute of Research, Marlyand, United States of America
- Uniformed Services University of the Health Sciences, Maryland, United States of America
- Marie Bashir Institute, University of Sydney, New South Wales, Australia
| | - Cecile Viboud
- National Institutes of Health, Maryland, United States of America
| |
Collapse
|
9
|
Worden L, Wannier R, Blumberg S, Ge AY, Rutherford GW, Porco TC. Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.06.09.20125831. [PMID: 32577672 PMCID: PMC7302226 DOI: 10.1101/2020.06.09.20125831] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco's shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, -20.1%-81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.
Collapse
Affiliation(s)
- Lee Worden
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Rae Wannier
- 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
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Alex Y. Ge
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - George W. Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Travis C. Porco
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| |
Collapse
|
10
|
Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis. PLoS One 2020; 15:e0230406. [PMID: 32339195 PMCID: PMC7185698 DOI: 10.1371/journal.pone.0230406] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 03/01/2020] [Indexed: 01/18/2023] Open
Abstract
Ebola epidemics constitute serious public health emergencies. Multiple vaccines are under development to prevent these epidemics and avoid the associated morbidity and mortality. Assessing the potential impact of these vaccines on morbidity and mortality of Ebola is essential for devising prevention strategies. A mean-field compartmental stochastic model was developed for this purpose and validated by simulating the 2014 Sierra Leone epidemic. We assessed the impacts of prophylactic vaccination of healthcare workers (HCW) both alone and in combination with the vaccination of the general population (entire susceptible population other than HCW). The model simulated 8,706 (95% confidence intervals [CI]: 478–21,942) cases and 3,575 (95%CI: 179–9,031) deaths in Sierra Leone, in line with WHO-reported statistics for the 2014 epidemic (8,704 cases and 3,587 deaths). Relative to this base case, the model then estimated that prophylactic vaccination of only 10% of HCW will avert 12% (95% CI: 6%-14%) of overall cases and deaths, while vaccination of 30% of HCW will avert 34% of overall cases (95% CI: 30%-64%) and deaths (95% CI: 30%-65%). Prophylactic vaccination of 1% and 5% of the general population in addition to vaccinating 30% of HCW was estimated to result in reduction in cases by 44% (95% CI: 39%-61%) and 72% (95% CI: 68%-84%) respectively, and deaths by 45% (95% CI: 40%-61%) and 74% (95% CI: 70%-85%) respectively. Prophylactic vaccination of even small proportions of HCW is estimated to significantly reduce incidence of Ebola and associated mortality. The effect is greatly enhanced by the additional vaccination even of small percentages of the general population. These findings could be used to inform the planning of prevention strategies.
Collapse
|
11
|
Measles transmission during a large outbreak in California. Epidemics 2019; 30:100375. [PMID: 31735584 PMCID: PMC7211428 DOI: 10.1016/j.epidem.2019.100375] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 10/26/2019] [Accepted: 10/29/2019] [Indexed: 02/07/2023] Open
Abstract
A large measles outbreak in 2014–2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates.
Collapse
|
12
|
Kelly JD, Park J, Harrigan RJ, Hoff NA, Lee SD, Wannier R, Selo B, Mossoko M, Njoloko B, Okitolonda-Wemakoy E, Mbala-Kingebeni P, Rutherford GW, Smith TB, Ahuka-Mundeke S, Muyembe-Tamfum JJ, Rimoin AW, Schoenberg FP. Real-time predictions of the 2018-2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models. Epidemics 2019; 28:100354. [PMID: 31395373 PMCID: PMC7358183 DOI: 10.1016/j.epidem.2019.100354] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 07/11/2019] [Accepted: 07/11/2019] [Indexed: 12/17/2022] Open
Abstract
As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.
Collapse
Affiliation(s)
- J Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA; F.I. Proctor Foundation, University of California, San Francisco, CA USA.
| | - Junhyung Park
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Ryan J Harrigan
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA
| | - Nicole A Hoff
- Department of Epidemiology, University of California, Los Angeles, CA, USA
| | - Sarita D Lee
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Rae Wannier
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | | | | | | | | | - George W Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Thomas B Smith
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA
| | | | | | - Anne W Rimoin
- Department of Epidemiology, University of California, Los Angeles, CA, USA
| | | |
Collapse
|
13
|
Worden L, Wannier R, Hoff NA, Musene K, Selo B, Mossoko M, Okitolonda-Wemakoy E, Muyembe Tamfum JJ, Rutherford GW, Lietman TM, Rimoin AW, Porco TC, Kelly JD. Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019. PLoS Negl Trop Dis 2019; 13:e0007512. [PMID: 31381606 PMCID: PMC6695208 DOI: 10.1371/journal.pntd.0007512] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 08/15/2019] [Accepted: 06/03/2019] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak. METHODS For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts. RESULTS During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. CONCLUSIONS Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.
Collapse
Affiliation(s)
- Lee Worden
- F. I. Proctor Foundation, University of California, San Francisco (UCSF), San Francisco, California, United States of America
| | - Rae Wannier
- F. I. Proctor Foundation, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- School of Medicine, UCSF, San Francisco, California, United States of America
| | - Nicole A. Hoff
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Kamy Musene
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Bernice Selo
- Ministry of Health, Directorate of Primary Health Care Development, Kinshasa, Democratic Republic of Congo
| | - Mathias Mossoko
- Ministry of Health, Directorate of Primary Health Care Development, Kinshasa, Democratic Republic of Congo
| | | | | | | | - Thomas M. Lietman
- F. I. Proctor Foundation, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- School of Medicine, UCSF, San Francisco, California, United States of America
| | - Anne W. Rimoin
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Travis C. Porco
- F. I. Proctor Foundation, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- School of Medicine, UCSF, San Francisco, California, United States of America
| | - J. Daniel Kelly
- F. I. Proctor Foundation, University of California, San Francisco (UCSF), San Francisco, California, United States of America
- School of Medicine, UCSF, San Francisco, California, United States of America
| |
Collapse
|
14
|
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.
Collapse
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.
| |
Collapse
|