Dan Yamin, PhD (*); Shai Gertler (*); Martial L. Ndeffo-Mbah, PhD (*); Laura A. Skrip, MPH; Mosoka Fallah, PhD; Tolbert G. Nyenswah, MPH; Frederick L. Altice, MD, MA; Alison P. Galvani, PhD
Grant Support: By the National Institutes of Health (U01 GM087719, U01 GM105627 and K24 DA017072).
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-2255.
Reproducible Research Statement:Study protocol: Not applicable. Statistical code and data set: Available in Supplement 6.
Requests for Single Reprints: Dan Yamin, PhD, Yale School of Public Health, 135 College Street, Suite 200, New Haven, CT 05610; e-mail, email@example.com.
Current Author Addresses: Drs. Yamin and Galvani, Mr. Gertler, Dr. Ndeffo Mbah, and Ms. Skrip: Yale School of Public Health, 135 College Street, Suite 200, New Haven, CT 05610.
Dr. Fallah and Mr. Nyenswah: Ministry of Health and Social Welfare, PO Box 10-9009, 1000 Monrovia 10, Liberia.
Dr. Altice: Section of Infectious Diseases, Yale University School of Medicine, 135 College Street, New Haven, CT 06510.
Author Contributions: Conception and design: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah.
Analysis and interpretation of the data: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah.
Drafting of the article: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah.
Critical revision of the article for important intellectual content: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah, L.A. Skrip, M. Fallah.
Final approval of the article: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah, M. Fallah.
Statistical expertise: D. Yamin, S. Gertler, M.L. Ndeffo-Mbah, L.A. Skrip.
Obtaining of funding: D. Yamin.
Administrative, technical, or logistic support: D. Yamin.
Collection and assembly of data: D. Yamin, M. Fallah.
The Ebola outbreak that is sweeping across West Africa is the largest, most volatile, and deadliest Ebola epidemic ever recorded. Liberia is the most profoundly affected country, with more than 3500 infections and 2000 deaths recorded in the past 3 months.
To evaluate the contribution of disease progression and case fatality on transmission and to examine the potential for targeted interventions to eliminate the disease.
Stochastic transmission model that integrates epidemiologic and clinical data on incidence and case fatality, daily viral load among survivors and nonsurvivors evaluated on the basis of the 2000–2001 outbreak in Uganda, and primary data on contacts of patients with Ebola in Liberia.
Montserrado County, Liberia, July to September 2014.
Ebola incidence and case-fatality records from 2014 Liberian Ministry of Health and Social Welfare.
The average number of secondary infections generated throughout the entire infectious period of a single infected case, R, was estimated as 1.73 (95% CI, 1.66 to 1.83). There was substantial stratification between survivors (RSurvivors), for whom the estimate was 0.66 (CI, 0.10 to 1.69), and nonsurvivors (RNonsurvivors), for whom the estimate was 2.36 (CI, 1.72 to 2.80). The nonsurvivors had the highest risk for transmitting the virus later in the course of disease progression. Consequently, the isolation of 75% of infected individuals in critical condition within 4 days from symptom onset has a high chance of eliminating the disease.
Projections are based on the initial dynamics of the epidemic, which may change as the outbreak and interventions evolve.
These results underscore the importance of isolating the most severely ill patients with Ebola within the first few days of their symptomatic phase.
National Institutes of Health.
The Ebola outbreak in West Africa is spiraling out of control. The need to determine how to deploy scarce resources to end this crisis is urgent.
A stochastic model of disease transmission that incorporated both clinical and epidemiologic data from Liberia, including incidence, case-fatality rate, and previous estimates of viral load, among both survivors and nonsurvivors was developed. By using these data, the model predicted that isolating the most severely ill patients during the first days of symptomatic illness would have the greatest effect on reducing viral transmission.
Targeted isolation may offer the best hope of ending the Ebola epidemic in West Africa.
Table. Parameters Used in the Stochastic Model
Infectivity according to day of infection and survivorship.
Top. Distribution of the reproductive number among survivors, RSurvivors, and among nonsurvivors, RNonsurvivors. Middle. Distribution of secondary cases per infected individual among survivors and nonsurvivors. Bottom. Average number of secondary cases per day of symptomatic disease.
Probability of disease elimination for different intervention strategies and coverages.
Top. Case isolation of nonsurvivors after symptom onset. Vertical dashed line indicates probability of disease elimination by isolating nonsurvivors within 4 days of symptom onset. Bottom. Percentage self-quarantine on first day of symptom onset. Vertical dashed line indicates probability of disease elimination achieved by a 60% reduction in contacts.
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Prasanta Raghab Mohapatra, Baijayantimala Mishra
All India Institute of Medical Sciences, India
October 27, 2014
Can there be Aerosol mediated Ebola Virus Transmission?
Yamin and colleagues(1) evaluated the probability of achieving disease elimination from case isolation of 50% to 100% of the non-survivors compared with of all infected individuals, when the type of contact is dominated through conversation or by sharing a meal or by sharing a bed. But have not mentioned the transmission of virus during conversion or aerosol transmission. The use of mask is protective only when it is of good quality and properly used. These filoviruses are classified as Category-A Bioterrorism agents by CDC and thought to be aerosol mediated. Countering aerosolized filovirus infection has been a major priority of biodefense research.(2) The evidence of Marburg virus transmission by exposure to bat inhabited cave and mines was already reported.(3) In a study by Reed et al Cynomolgus macaques, rhesus macaques, and African green monkeys when exposed to aerosolized Zaire Ebola Virus, all the three species developed similar clinical signs and symptoms and the outcome were comparable to that of parenteral inoculation for each species.(4)Virus-containing droplets of 0.8-1.2 microns when experimentally inhaled by rhesus monkeys became rapidly fatal in few days due to clinical EVD. Immunocytochemistry has also shown Ebola virus antigens in airway epithelium, alveolar pneumocytes, and macrophages in the lung and mediastinal lymph nodes. Aggregates of characteristic filamentous virus were present within type I pneumocytes, macrophages, and air spaces of the lung by electron microscopy.(5) Similar lethal infections were reported by aerosol exposure in 18 rhesus macaques including early infection of the respiratory lymphoid tissues.(6)Similar lethal infections were reported by aerosol exposure to Ebola Zaire to rhesus monkeys. Two of them exposed to a dose of 400 plaque-forming units (pfu) and other two to a dose of 50,000 pfu. All four monkey developed diseases and died after 7 to 9 days after exposure.(7) In a fatal disease like Ebola, where human experiment is not possible to prove the route of transmission, so evidences available with non-human primate should be taken seriously. But the absence of evidence in human is certainly not the evidence for absence of aerosol transmission. Demonstration of fatal aerosol transmission of this virus in monkeys reinforces the importance of taking appropriate precautions to prevent its potential aerosol transmission to humans. We request authors to discuss these issues related to their study to clear the air.REFERENCES1. Yamin D, Gertler S, Ndeffo-Mbah ML, Skrip LA, Fallah M, Nyenswah TG, et al. Effect of Ebola Progression on Transmission and Control in LiberiaEbola Disease: Progression and Control. Annals of Internal Medicine. 2014;N/A(N/A):N/A-N/A.2. Leffel EK, Reed DS. Marburg and Ebola viruses as aerosol threats. Biosecur Bioterror. 2004;2(3):186-91.3. Hartman AL, Towner JS, Nichol ST. Ebola and marburg hemorrhagic fever. Clin Lab Med. 2010;30(1):161-77.4. Reed DS, Lackemeyer MG, Garza NL, Sullivan LJ, Nichols DK. Aerosol exposure to Zaire ebolavirus in three nonhuman primate species: differences in disease course and clinical pathology. Microbes Infect. 2011;13(11):930-6.5. Johnson E, Jaax N, White J, Jahrling P. Lethal experimental infections of rhesus monkeys by aerosolized Ebola virus. Int J Exp Pathol. 1995;76(4):227-36.6. Twenhafel NA, Mattix ME, Johnson JC, Robinson CG, Pratt WD, Cashman KA, et al. Pathology of experimental aerosol Zaire ebolavirus infection in rhesus macaques. Vet Pathol. 2013;50(3):514-29.7. Jaax N, Jahrling P, Geisbert T, Geisbert J, Steele K, McKee K, et al. Transmission of Ebola virus (Zaire strain) to uninfected control monkeys in a biocontainment laboratory. Lancet. 1995;346(8991-8992):1669-71.
All India Institute of Medical Sciences
Carlos Polanco, Ph.D.
Departament of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México.
January 12, 2015
Considerations about a design of an epidemiologic predictor
We have read the interesting Yamin et al.'s article concerns to the isolating the most severely ill patients with Ebola within the first few days of their symptomatic phase (1). Of course we agree that the progression of the disease lies in an early identification of the severe cases, however, in practice this is very difficult to achieve. For this reason we would like to emphasize the importance of the design predictive models to assist in this assessment with minimal count of cases, and mainly formed by random variables related to time and space (2); a model that we call Hidden Markov Models-Cumulative Sums (3). In our opinion it will allow the assessment of isolated cases in a matter of hours, with high level of success. Nowadays, with the current means of transport, an individual unaware of being infected can travel from one continent to another in just few hours (4). Therefore it would be important to locate, as efficiently as possible, the place of a probable pandemic outbreak, and the only feasible way to do so would be with stochastic prediction models.Sincerely yours,Carlos Polanco, Ph.D. ** Departament of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México.References1. Yamin D Gertler, Ndeffo-Mbah ML, Skrip, Fallah M, Nyenswah TG, Altice FL, Galvani AP. Effect of progression on transmission and control in Liberia ebola. Ann Intern Med. Jan 2015 6; 162 (1): 11-7. DOI: 10.7326/M14-2255 (2015). 2. Castanon-Gonzalez JA, Polanco C (Letter to the Editor) Dobson to Mathematical models for emerging disease. Science. 2014; 346 (6215) 1294-1295. DOI: 10.1126/science.aaa3441. (2014) 3. Polanco C, Castanon-González JA, Samaniego JL, Buhse T (Letter to the Editor) Howard SJ, Hopwood S, Davies SC Antimicrobial Resistance: A Global Challenge Sci. Transl. Med DOI: 10.1126/scitranslmed. 3009315 (2014).4. Polanco C, Castanon-González JA (Letter to the Editor) Hantel A, Olopade CO. Drug and Vaccine Access in the Ebola Epidemic: Advising Caution in Compassionate Use. Ann Intern Med. 2014 Oct 14. doi: 10.7326/M14-2002 (2014).
Harry B. Burke, MD, PhD
Uniformed Services University of the Health Sciences
Case isolation has been the method of choice for dealing with disease epidemics for hundreds of years. The investigators improve on our knowledge of how to truncate the human-to-human transmission of a communicable disease by providing an estimate of the time within which case isolation is most effective for Ebola patients, namely, within 4 days of symptom onset. Since rapidly fatal diseases for which there is no effective treatment tend to be self-limiting, can the investigators provide an estimate of how long the Ebola pandemic will continue before it dies out on its own?
Yamin D, Gertler S, Ndeffo-Mbah ML, Skrip LA, Fallah M, Nyenswah TG, et al. Effect of Ebola Progression on Transmission and Control in Liberia. Ann Intern Med. 2015;162:11–17. doi: 10.7326/M14-2255
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Published: Ann Intern Med. 2015;162(1):11-17.
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