Literature DB >> 26079150

Estimating Ebola Treatment Needs, United States.

Gabriel Rainisch, Jason Asher, Dylan George, Matt Clay, Theresa L Smith, Christine Kosmos, Manjunath Shankar, Michael L Washington, Manoj Gambhir, Charisma Atkins, Richard Hatchett, Tim Lant, Martin I Meltzer.   

Abstract

Entities:  

Keywords:  BED; Ebola virus; Ebola virus disease; Ebola virus infection; beds for Ebola disease; communicable diseases; epidemics; hemorrhagic fever; hospital bed capacity; hospital units; hospitalization; length of stay; models; statistical models; viruses

Mesh:

Year:  2015        PMID: 26079150      PMCID: PMC4816331          DOI: 10.3201/eid2107.150286

Source DB:  PubMed          Journal:  Emerg Infect Dis        ISSN: 1080-6040            Impact factor:   6.883


× No keyword cloud information.
To the Editor: By December 31, 2014, the Ebola epidemic in West Africa had resulted in treatment of 10 Ebola case-patients in the United States; a maximum of 4 patients received treatment at any one time (). Four of these 10 persons became clinically ill in the United States (2 infected outside the United States and 2 infected in the United States), and 6 were clinically ill persons medically evacuated from West Africa (Technical Appendix 1 Table 6). To plan for possible future cases in the United States, policy makers requested we produce a tool to estimate future numbers of Ebola case-patients needing treatment at any one time in the United States. Gomes et al. previously estimated the potential size of outbreaks in the United States and other countries for 2 different dates in September 2014 (). Another study considered the overall risk for exportation of Ebola from West Africa but did not estimate the number of potential cases in the United States at any one time (). We provide for practicing public health officials a spreadsheet-based tool, Beds for Ebola Disease (BED) (Technical Appendix 2) that can be used to estimate the number of Ebola patients expected to be treated simultaneously in the United States at any point in time. Users of BED can update estimates for changing conditions and improved quality of input data, such as incidence of disease. The BED tool extends the work of prior studies by dividing persons arriving from Liberia, Sierra Leone, and Guinea into the following 3 categories: 1) travelers who are not health care workers (HCWs), 2) HCWs, and 3) medical evacuees. This categorization helps public health officials assess the potential risk for Ebola virus infection in individual travelers and the subsequent need for post-arrival monitoring (). We used the BED tool to calculate the estimated number of Ebola cases at any one time in the United States by multiplying the rate of new infections in the United States by length of stay (LOS) in hospital (Table). The rate of new infections is the sum of the rate of infected persons in the 3 listed categories who enter the United States from Liberia, Sierra Leone, or Guinea. For the first 2 categories of travelers, low and high estimates of Ebola-infected persons arriving in the United States are calculated by using low and high estimates of both the incidence of disease in the 3 countries and the number of arrivals per month (Table). Calculating the incidence among arriving HCWs required estimating the number of HCWs treating Ebola patients in West Africa (Technical Appendix 1, Tables 2–4). For medical evacuations of persons already ill from Ebola, we calculated low and high estimates using unpublished data of such evacuations through the end of December 2014.
Table

Calculated monthly rates of Ebola disease among persons arriving in the United States and additional secondary cases, 2014

Arriving personsInput 1: infections/mo*Input 2: at-risk populationInput 3: US arrival rate/mo†Output 1: importations/mo‡Output 4: additional secondary cases§Output 2: total cases/mo‡
Non-HCWLow110,0002,0000.200.2

High
3
10,000
3,000

0.9
2
2.7
HCWLow1100300.300.3

High
5
100
60

3.0
2
9.0
Medical evacuations¶LowNANA11.001
HighNANA33.003

*Infections in travelers who are not HCWs were based on the monthly incidence identified in World Health Organization situation reports during June–October 2014 (online Technical Appendix 1 Table 1) (5). The high value was the highest monthly incidence [September] rounded to the nearest whole number; the low value was set at 30% of the high value. Infections in HCWs were based on estimates of the number of HCWs in West Africa with and without Ebola virus infection at different times in the epidemic [online Technical Appendix 1 and Appendix 1 Tables 2–4]. HCW, health care worker; NA, not applicable.
†The low estimate of US arrival rates for travelers who are not HCWs and both the low and high rates for HCWs were based on the count of screened airline passengers originating in Liberia, Sierra Leone, and Guinea in the month from mid-October through mid-November 2014 (Centers for Disease Control and Prevention [CDC], unpub. data). For the high US arrival rate for travelers who are not HCWs, we assumed a 50% increase over the low value [3,000 = 2,000 × 1.5] to approximate the arrival rate in 2013, before the epidemic (3). Rates of HCW arrivals were based on travelers who identified themselves as having worked in a health care facility during the previous 21 d during screenings at their airport of entry to the United States during November 5–December 1, 2014, and the exposure risk category assigned to them according to CDC’s Interim US Guidance for Monitoring and Movement of Persons with Potential Ebola Virus Exposure (4,6). The low estimate value of arrivals of HCWs (30 arriving HCWs) was approximately the lowest rate of high-risk and some-risk HCWs entering the United States. The high estimate value (60 arriving HCWs) was approximately the highest rate of high-, some-, and low-risk HCWs entering the United States (CDC, unpub. data).
‡Output 1 = (Input 1 / Input 2) × Input 3; Output 2 = Output 1 + (Output 1 × Input 4). See online Technical Appendix 1 for further details.
§Assumed number of additional secondary transmissions occurring in the United States per primary case based on the range of experience from the outbreak: 1 imported case to the United States resulted in 2 secondary infections, and several case-patients have been treated without any secondary infections (7).
¶Number of medical evacuations was obtained from unpublished Medical Evacuation Missions Reports (US Department of Health and Human Services, unpub. data).

*Infections in travelers who are not HCWs were based on the monthly incidence identified in World Health Organization situation reports during June–October 2014 (online Technical Appendix 1 Table 1) (5). The high value was the highest monthly incidence [September] rounded to the nearest whole number; the low value was set at 30% of the high value. Infections in HCWs were based on estimates of the number of HCWs in West Africa with and without Ebola virus infection at different times in the epidemic [online Technical Appendix 1 and Appendix 1 Tables 2–4]. HCW, health care worker; NA, not applicable.
†The low estimate of US arrival rates for travelers who are not HCWs and both the low and high rates for HCWs were based on the count of screened airline passengers originating in Liberia, Sierra Leone, and Guinea in the month from mid-October through mid-November 2014 (Centers for Disease Control and Prevention [CDC], unpub. data). For the high US arrival rate for travelers who are not HCWs, we assumed a 50% increase over the low value [3,000 = 2,000 × 1.5] to approximate the arrival rate in 2013, before the epidemic (3). Rates of HCW arrivals were based on travelers who identified themselves as having worked in a health care facility during the previous 21 d during screenings at their airport of entry to the United States during November 5–December 1, 2014, and the exposure risk category assigned to them according to CDC’s Interim US Guidance for Monitoring and Movement of Persons with Potential Ebola Virus Exposure (4,6). The low estimate value of arrivals of HCWs (30 arriving HCWs) was approximately the lowest rate of high-risk and some-risk HCWs entering the United States. The high estimate value (60 arriving HCWs) was approximately the highest rate of high-, some-, and low-risk HCWs entering the United States (CDC, unpub. data).
‡Output 1 = (Input 1 / Input 2) × Input 3; Output 2 = Output 1 + (Output 1 × Input 4). See online Technical Appendix 1 for further details.
§Assumed number of additional secondary transmissions occurring in the United States per primary case based on the range of experience from the outbreak: 1 imported case to the United States resulted in 2 secondary infections, and several case-patients have been treated without any secondary infections (7).
¶Number of medical evacuations was obtained from unpublished Medical Evacuation Missions Reports (US Department of Health and Human Services, unpub. data). Although only 1 Ebola case has caused additional cases in the United States (), we included the possibility that each Ebola case-patient who traveled into the United States would cause either 0 secondary cases (low estimate) or 2 secondary cases (high estimate) (Table). Such transmission might occur before a clinically ill traveler is hospitalized or between a patient and HCWs treating the patient (). To account for the possibility that infected travelers may arrive in clusters, we assumed that persons requiring treatment would be distributed according to a Poisson probability distribution. Using this distribution enables us to calculate, using the BED tool, 95% CIs around the average estimate of arriving case-patients. The treatment length used in both the low and high estimate calculations was 14.8 days, calculated as a weighted average of the LOS of hospitalized case-patients treated in West Africa through September 2014 (Technical Appendix 1 Table 5) (). We conducted a sensitivity analysis using LOS and reduced case-fatality rate of patients treated in the United States (Technical Appendix 1 Table 6). For late 2014, the low estimate of the average number of beds needed to treat patients with Ebola at any point in time was 1 (95% CI 0–3). The high estimate was 7 (95% CI 2–13). In late 2014, the United States had to plan and prepare to treat additional Ebola case-patients. By mid-January 2015, the capacity of Ebola treatment centers in the United States (49 hospitals with 71 total beds []) was sufficient to care for our highest estimated number of Ebola patients. Policymakers already have used the BED model to evaluate responses to the risk for arrival of Ebola virus–infected travelers, and it can be used in future infectious disease outbreaks of international origin to plan for persons requiring treatment within the United States.

Technical Appendix 1

Data inputs and assumptions; sensitivity analysis (length of stay and case-fatality rate); comparison with other published estimates; and limitations.

Technical Appendix 2

Beds for Ebola Disease (BED) model.
  6 in total

1.  Assessing the international spreading risk associated with the 2014 west african ebola outbreak.

Authors:  Marcelo F C Gomes; Ana Pastore Y Piontti; Luca Rossi; Dennis Chao; Ira Longini; M Elizabeth Halloran; Alessandro Vespignani
Journal:  PLoS Curr       Date:  2014-09-02

2.  Assessment of the potential for international dissemination of Ebola virus via commercial air travel during the 2014 west African outbreak.

Authors:  Isaac I Bogoch; Maria I Creatore; Martin S Cetron; John S Brownstein; Nicki Pesik; Jennifer Miniota; Theresa Tam; Wei Hu; Adriano Nicolucci; Saad Ahmed; James W Yoon; Isha Berry; Simon I Hay; Aranka Anema; Andrew J Tatem; Derek MacFadden; Matthew German; Kamran Khan
Journal:  Lancet       Date:  2014-10-21       Impact factor: 79.321

3.  Ebola virus disease cluster in the United States--Dallas County, Texas, 2014.

Authors:  Michelle S Chevalier; Wendy Chung; Jessica Smith; Lauren M Weil; Sonya M Hughes; Sibeso N Joyner; Emily Hall; Divya Srinath; Julia Ritch; Prea Thathiah; Heidi Threadgill; Diana Cervantes; David L Lakey
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2014-11-21       Impact factor: 17.586

4.  Announcement: Interim U.S. guidance for monitoring and movement of persons with potential Ebola virus exposure.

Authors: 
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2014-10-31       Impact factor: 17.586

5.  Airport exit and entry screening for Ebola--August-November 10, 2014.

Authors:  Clive M Brown; Aaron E Aranas; Gabrielle A Benenson; Gary Brunette; Marty Cetron; Tai-Ho Chen; Nicole J Cohen; Pam Diaz; Yonat Haber; Christa R Hale; Kelly Holton; Katrin Kohl; Amanda W Le; Gabriel J Palumbo; Kate Pearson; Christina R Phares; Francisco Alvarado-Ramy; Shah Roohi; Lisa D Rotz; Jordan Tappero; Faith M Washburn; James Watkins; Nicki Pesik
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2014-12-12       Impact factor: 17.586

6.  Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections.

Authors:  Bruce Aylward; Philippe Barboza; Luke Bawo; Eric Bertherat; Pepe Bilivogui; Isobel Blake; Rick Brennan; Sylvie Briand; Jethro Magwati Chakauya; Kennedy Chitala; Roland M Conteh; Anne Cori; Alice Croisier; Jean-Marie Dangou; Boubacar Diallo; Christl A Donnelly; Christopher Dye; Tim Eckmanns; Neil M Ferguson; Pierre Formenty; Caroline Fuhrer; Keiji Fukuda; Tini Garske; Alex Gasasira; Stephen Gbanyan; Peter Graaff; Emmanuel Heleze; Amara Jambai; Thibaut Jombart; Francis Kasolo; Albert Mbule Kadiobo; Sakoba Keita; Daniel Kertesz; Moussa Koné; Chris Lane; Jered Markoff; Moses Massaquoi; Harriet Mills; John Mike Mulba; Emmanuel Musa; Joel Myhre; Abdusalam Nasidi; Eric Nilles; Pierre Nouvellet; Deo Nshimirimana; Isabelle Nuttall; Tolbert Nyenswah; Olushayo Olu; Scott Pendergast; William Perea; Jonathan Polonsky; Steven Riley; Olivier Ronveaux; Keita Sakoba; Ravi Santhana Gopala Krishnan; Mikiko Senga; Faisal Shuaib; Maria D Van Kerkhove; Rui Vaz; Niluka Wijekoon Kannangarage; Zabulon Yoti
Journal:  N Engl J Med       Date:  2014-09-22       Impact factor: 91.245

  6 in total
  8 in total

1.  The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

Authors:  Cécile Viboud; Kaiyuan Sun; Robert Gaffey; Marco Ajelli; Laura Fumanelli; Stefano Merler; Qian Zhang; Gerardo Chowell; Lone Simonsen; Alessandro Vespignani
Journal:  Epidemics       Date:  2017-08-26       Impact factor: 4.396

2.  Estimates of Outbreak Risk from New Introductions of Ebola with Immediate and Delayed Transmission Control.

Authors:  Damon J A Toth; Adi V Gundlapalli; Karim Khader; Warren B P Pettey; Michael A Rubin; Frederick R Adler; Matthew H Samore
Journal:  Emerg Infect Dis       Date:  2015-08       Impact factor: 6.883

3.  The introduction of dengue follows transportation infrastructure changes in the state of Acre, Brazil: A network-based analysis.

Authors:  Raquel Martins Lana; Marcelo Ferreira da Costa Gomes; Tiago França Melo de Lima; Nildimar Alves Honório; Cláudia Torres Codeço
Journal:  PLoS Negl Trop Dis       Date:  2017-11-17

4.  Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

Authors:  Simon Pollett; Michael A Johansson; Nicholas G Reich; David Brett-Major; Sara Y Del Valle; Srinivasan Venkatramanan; Rachel Lowe; Travis Porco; Irina Maljkovic Berry; Alina Deshpande; Moritz U G Kraemer; David L Blazes; Wirichada Pan-Ngum; Alessandro Vespigiani; Suzanne E Mate; Sheetal P Silal; Sasikiran Kandula; Rachel Sippy; Talia M Quandelacy; Jeffrey J Morgan; Jacob Ball; Lindsay C Morton; Benjamin M Althouse; Julie Pavlin; Wilbert van Panhuis; Steven Riley; Matthew Biggerstaff; Cecile Viboud; Oliver Brady; Caitlin Rivers
Journal:  PLoS Med       Date:  2021-10-19       Impact factor: 11.069

5.  Mathematical modeling of the West Africa Ebola epidemic.

Authors:  Jean-Paul Chretien; Steven Riley; Dylan B George
Journal:  Elife       Date:  2015-12-08       Impact factor: 8.140

6.  Potential for broad-scale transmission of Ebola virus disease during the West Africa crisis: lessons for the Global Health security agenda.

Authors:  Eduardo A Undurraga; Cristina Carias; Martin I Meltzer; Emily B Kahn
Journal:  Infect Dis Poverty       Date:  2017-12-01       Impact factor: 4.520

7.  Performance of materials used for biological personal protective equipment against blood splash penetration.

Authors:  Noriko Shimasaki; Katsuaki Shinohara; Hideki Morikawa
Journal:  Ind Health       Date:  2017-10-05       Impact factor: 2.179

8.  Technology to advance infectious disease forecasting for outbreak management.

Authors:  Dylan B George; Wendy Taylor; Jeffrey Shaman; Caitlin Rivers; Brooke Paul; Tara O'Toole; Michael A Johansson; Lynette Hirschman; Matthew Biggerstaff; Jason Asher; Nicholas G Reich
Journal:  Nat Commun       Date:  2019-09-02       Impact factor: 14.919

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.