Mathematical models for devising the optimal Ebola virus disease eradication
- Shuo Jiang†^{1, 2},
- Kaiqin Wang†^{3},
- Chaoqun Li†^{4},
- Guangbin Hong†^{5},
- Xuan Zhang^{1},
- Menglin Shan^{1},
- Hongbin Li^{3} and
- Jin Wang^{1}Email author
https://doi.org/10.1186/s12967-017-1224-6
© The Author(s) 2017
Received: 17 March 2017
Accepted: 27 May 2017
Published: 1 June 2017
Abstract
Background
The 2014–2015 epidemic of Ebola virus disease (EVD) in West Africa defines an unprecedented health threat for human.
Methods
We construct a mathematical model to devise the optimal Ebola virus disease eradication plan. We used mathematical model to investigate the numerical spread of Ebola and eradication pathways, further fit our model against the real total cases data and calculated infection rate as 1.754.
Results
With incorporating hospital isolation and application of medication in our model and analyzing their effect on resisting the spread, we demonstrate the second peak of 10,029 total cases in 23 days, and expect to eradicate EVD in 285 days. Using the regional spread of EVD with our transmission model analysis, we analyzed the numbers of new infections through four important transmission paths including household, community, hospital and unsafe funeral.
Conclusions
Based on the result of the model, we find out the key paths in different situations and propose our suggestion to control regional transmission. We fully considers Ebola characteristics, economic and time optimization, dynamic factors and local condition constraints, and to make our plan realistic, sensible and feasible.
Keywords
Ebola Spread Eradication pathways Hospital isolation Mathematical modelBackground
The current Ebola virus disease (EVD) outbreak in West Africa is now the largest yet documented and has caught worldwide attention [1, 2] with the countries of Guinea, Liberia, and Sierra Leone most affected [3]. The current Ebola virus (EBOV) outbreak in western Africa has caused more than 28,000 infections and over 11,100 deaths by Dec, 2015 [4] and the epidemic is increasing rapidly due to socioeconomic disadvantage and health system inadequacies in the three main affected countries [5, 6]. This is by far the largest outbreak of the virus in history, the virus spreads through human-to-human transmission [7, 8]. A study of the current EBOV outbreak showed that the case fatality rate was 34.7% overall and was higher for patients with stage 3 disease with neurological symptoms (66.7%) [9]. The surveillance and outbreak response management system architecture has been used to support the control of the EVD outbreak in West Africa as framework for Ebola virus disease outbreak modeling [10]. The new discoveries of Ebola medication announced by the World Medical Association (WMA) brings hope to the Ebola infected area. There are various modeling studies of the EVD epidemic have been reported using a wide range of quantitative approaches and obtaining analysis of the reproduction number of Ebola outbreak [11–16]. Household structured epidemic models have also provided some interesting insights of demographic determinants of Ebola epidemic risk [17]. These mathematical models were developed for the largest epidemics reported and involved in original EVD epidemiological data and genomic data [2, 13, 18–28], which predicted many more cases than actually occurred, some models produced more accurate predictions, and others yielded valuable insights. Recently, a system review and meta-analysis of 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature has been applied to assess these key models, data and model performance [29].
Further to improve forecast accuracy and investigate the spread and eradication pathways of EVD, a mathematical model to devise the optimal Ebola eradication plan for implementation by local governments, pharmaceutical companies, logistics companies, and international organizations needs to take into consideration medical condition improvement, education campaign, personal protection, regulation promulgation, and other contributing factors and considered Ebola characteristics and its spread trend, local condition constraints, and economic optimization to make the models realistic, sensible and feasible. The purpose of our study reported here was to develop a mathematical model to devise the optimal Ebola virus disease eradication plan. Here, we only choose Liberia as our study country in our model, which is flexible for other countries in the same way and not only limited to Liberia.
Methods
Characteristics of Ebola
For feasibility and usefulness of our approach, the important attributes of the Ebola virus and EVD before constructing our models were be summarized and included (1) Origin: the bat species, fruit bats in particular, are considered to be the natural reservoir of Ebola virus. Severe forest loss in Africa in recent years has brought potentially infectious wild animals into closer contact with human settlements; this increases the risk of virus transmission from wild animals to people; (2) transmission: Ebola spreads mainly through body fluids. In each outbreak, the virus is first introduced into the human population through close contact with bodily fluids of infected wild animals or infected people [30]. The common symptoms of EVD include diarrhea, fatigue, fever, muscle pain, severe headache, abdominal or stomach pain, vomiting, and unexplained hemorrhage. The course of EVD shows three phases in each case: (1) Incubation phase: infected patients generally don’t show any particular symptoms of EVD and patients don’t transmit the virus in this period; (2) Early infective phase: infected patients begin showing explicit symptoms such as fever and fatigue, and the EVD medication developed by WMA can cure the disease; (3) Advanced infective phase: infected patients are close to death, and cannot be cured by existing EVD medication.
Assumptions of modeling for EVD analysis
For the model for prediction of the numerical spread of Ebola and regional transmission of Ebola, given that births and deaths affect the total population and the duration of the outbreak is short, we won’t take the change of total population in consideration. Firstly, we assume the population of an infected region remains constant in the ongoing outbreak, which enables us to focus on the analysis of infected people. Since people in the incubation phase and early infective phase can be cured by EVD medication, and the incubation phase is assumed impossible to detect. Next, we assume that the vaccine could not provide 100% protection from the Ebola virus, which its true effective vaccination rate was at between 75 and 100% [31]. At the same time, it is expected that the treatment time of EVD will gradually reduce with improving medical condition in these countries.
Model for prediction of the numerical spread of Ebola
Definition of parameters for the modified epidemic model
Parameters | Definition |
---|---|
á | The isolation rate, i.e. the rate of people moved from the infective group to the hospital isolated group |
\(\hat{a}\) | The infection rate, i.e. the rate of the susceptible population get infected |
\(\tilde{a}_{E}\) | The outflow rate of early stage infected group, i.e. the rate that early infected people turn advanced infected |
\(\tilde{a}_{L}\) | The outflow rate of advanced infected group, i.e. the rate that advanced infected people die of the disease |
ό | The outflow rate of the incubation group to the early stage infective group |
ù | The outflow rate of the isolation group, that is the rate of isolated patients get cured. |
N | The total population |
Nc(t) | Number of other household in other countries of one un-hospitalized infected people at time t; |
c | Comprehensive treatment and immunization rate |
\(\Delta\) | The ratio of the case input (<0) or the output (>0) |
Model for regional transmission of Ebola
Definition of parameters for model two
Parameter | Definition |
---|---|
ë(t) | Infection force at time t |
â_{f} | Infection rate of un-hospitalized infected people with the household |
ό | Ratio of the transmission rate in the other households in the same community to that within the household |
\({\hat{\text{a}}}_{\text{h}}\) | Infection rate of hospitalized infected people |
\({\hat{\text{a}}}_{\text{b}}\) | Infection rate of an unsafe funeral |
N_{f(t)} | Number of household of one un-hospitalized infected people at time t |
N_{a(t)} | Number of other household in the community of one un-hospitalized infected people at time t |
N_{h(t)} | Number of contacts with hospitalized infected people at time t |
I_{(t)} | Number of infected people at time t |
∝ | Isolation rate |
f | Fatality rate |
u | Unsafe funeral ratio |
Results
Estimation of the numerical spread of Ebola
An SEIR (susceptible-exposed-infectious-recovered) model was previously established for the Ebola endemic, which provided the estimates of reproduction numbers in Guinea, Sierra Leone and Liberia (Additional file 1: Table S1) [30]. However, it didn’t take control interventions into consideration. We take the method as estimate infection rate \({\hat{\text{a}}}\) in model; then consider the function of resistance methods in our modified model, which will offer more reference for government planning of Ebola eradication. Since the period that people will stay in the incubation phase and in the infective phase differed little, we take the incubation period and infective period (1/ό = 5.3 days and \(1/\tilde{a} = 5.61\)) days in previous estimates from an outbreak of EVD in Congo in 1995 with our model [32]. We assume the value of \({\hat{\text{a}}}\) remains constant in the studied period. In this study, we first fitted our model against the real EVD total cases data of Liberia from 2nd July 2014–28th August 2014. We obtain the value of infection rate \({\hat{\text{a}}}\) as 1.754 in our basic SEIR model and Monte Carlo algorithm given the value of other parameters in the differential equations system, which our estimating value of infection rate \({\hat{\text{a}}}\) is a little higher, but acceptable, comparing with other results of related research. For examples, Althaus CL estimated the value to be 1.59 and WHO Ebola Response Team estimated the value to be 1.51 (both for Liberia) [33].
The effect of vaccination on eradicating Ebola
The effect of hospital isolation on eradicating Ebola
For analysis of isolation rate, we tested the effect of hospital isolation on eradicating Ebola. We set è = 0.01 and vary the isolation rate á. The general trend is very similar to our analysis about the vaccination rate in Fig. 4b, which the bigger the isolation rate á is, the sooner the disease can be eradicated; and the eradication process can be divided into three phases. When we set è = 0.02 and á = 0.1, the number of total cases decreases to 7706 in 4 days, increases to the second peak of 10,029 total cases in 23 days; we can expect to eradicate the disease in 285 days (number of total cases <1). The actual eradication process should be shorter as discussed above. Moreover, high isolation rate can resist the second rise (like á = 0.3). The determination of isolation rate á is subject to the medical condition of one country, i.e. the total number of hospital beds of Ebola. The demand for hospital beds by time is shown in the Fig. 4c. We assume there are 1000 isolated people in hospital on 1st Feb. We can notice from the figure above that high isolation rate means a faster rise and more quantity in demand of hospital beds. The numbers of maximum hospital beds needed are 3488 in 11 days, 2954 in 15 days and 2230 in 26 days for á = 0.3, á = 0.2, and á = 0.1, respectively. Therefore, each country can determine their isolation rate á based on their medical condition, i.e. the number of hospital beds.
Effect of Ebola infection rate on eradicating Ebola
Further, we set the value of the infection rate â as 1.754 in accordance with our model result. In fact, the infection rate can be controlled by public medical education and protection. We analysis the trend of total cases number with different \({\hat{\text{a}}}\) value (\({\hat{\text{a}}} = 1.75,\,1.55,\,1.35\)), we set á = 0.2 and è = 0.02. The result is shown in Fig. 4d. The general trend is also similar: (1) The smaller the infection rate \({\hat{\text{a}}}\), the sooner the disease can be eradicated: (2) The eradication process also can be divided into three phases as we have discussed above and low infection rate can resist the second rise.
Effect of Ebola treatment time on eradicating Ebola
We think that the average time from symptom onset to medical treatment will be shortened with the improvement of treatment, which will affect eradicating Ebola. Firstly, we simulate the average time from symptom onset to medical treatment by shortening the treatment time. Since treatment of EVD involves both of early and late stages in infection of EBOV, we set á = 0.2 and è = 0.02, and assume that treatment time will be shortened both in the early and late stages in infection of EBOV. Here, we simulate three models: one is the original model of the early treatment time (γE = 3 days) and late treatment time (γL = 2.61 days); if all the assuming is same, there are other two simulation results (Fig. 4e), which the early and late treatment time in the two models is reduced by 1/2 or one day. We investigate that the time of eradicating Ebola is shortened if the treatment time is shortened, and expect to eradicate EVD in 188, 152 and 120 days in the three models if the case is less than 1, respectively.
Effect of Ebola imported or exported cases rate on eradicating Ebola
During the outbreak of Ebola in Liberian in 2014, it is estimated that the imported and exported cases does not exceed 1% [34]. \(\Delta\) represents the ratio of the imported case (n < 0) or the exported case (n > 0). Here, we set á = 0.2 and è = 0.02 and simulate the three situations: one is no imported and exported case; one is only exported cases (1%) or imported cases (1%), which were shown in Fig. 4f. For eradicating Ebola, we can notice that the imported and exported effects are presented in two aspects: (1) the exported weakens the second peak, as the imported will strengthen the second peak; (2) the imported extends the outbreak time of EVD, and the exported can reduce its outbreak time. However, there is little difference among these three situations.
Force of infection of each Ebola transmission path
The analysis of infected people each day by different transmission paths
Paths | Household | Community | Hospital | Funeral |
---|---|---|---|---|
Infected people | (1 − á)I(t) | (1 − á)I(t) | áI(t) | ufI(t) |
Infection force ë(t) | â _{ f } /N _{ f(t) } | ó \(\hat{a}_{f}\) /N _{ a(t) } | \(\hat{a}_{h}\) /N _{ h(t) } | \(\hat{a}_{b}\) /N _{ f(t) } + ó \(\hat{a}_{b}\) /N _{ a(t) } |
Total infection cases | (1 − á)I(t) \(\hat{a}_{f}\) /N _{ f(t) } | (1 − á)I(t) ó \(\hat{a}_{f}\) /N _{ a(t) } | áI(t) \(\hat{a}_{h}\) /N _{ h(t) } | \(ufI(t)\left( {\hat{a} _{b} /N_{{f(t)}} + \acute{o} \hat{a} _{b} /N_{{a(t)}} } \right)\) |
Number of new infection cases through different paths with different values for hospital isolation rate (á)
Paths\isolation rate | á = 0.3 | á = 0.5 | á = 0.7 |
---|---|---|---|
Household | 33.6 | 24.0 | 14.4 |
Community | 26.5 | 19.0 | 11.4 |
Hospital | 13.2 | 22.0 | 30.8 |
Unsafe funeral | 18.0 | 17.0 | 16.3 |
Total cases | 91.3 | 82.0 | 72.9 |
Discussion
People in Western Africa are living in an abyss of misery because of the ongoing Ebola outbreak. Scientist have joyfully announced the invention of Ebola medication, including Ebola drug and vaccine and call on joint effort from all parties involved to eradicate Ebola as soon as possible [36, 37]. Local governments have the responsibility to set the deadline for the final eradication, and the Ebola epidemic in Western Africa rather reveals fundamental failures in establishing health policies in Western Africa [38]. Considering regional spread of Ebola is very important, prevention of infection on important paths can decrease the infection rate of Ebola. Thus, effective Ebola eradication must involve active cooperation between the government, pharmaceutical companies and international organizations. In this study, our approach can make eradication plans with different local constraints and eradication time goals. It shows great adaptability and feasibility and takes the efficient Ebola eradication as our first priority, and give weight on economic optimization.
Firstly, we consider both numerical and regional spread of Ebola virus, and our approach is based on Ebola transmission mechanism and characteristics. We assume comprehensive treatment and immunization rate is 98%. We further analyzed the effect of various á, è, \({\hat{\text{a}}}\), \(\gamma_{E}\),\(\gamma_{L}\) and \(\Delta\) values on Ebola eradication in this study, which provides a good instruction on the eradication effort for the local government. First, it must maintain its vigilance in the early phases and enhance detection to predict the potential second epidemic peak. Large quantities of drugs and hospital beds should be prepared in advance to handle the second peak. Second, high á, è, \(\Delta\)value and low \({\hat{\text{a}}}\), \(\gamma_{E}\), \(\gamma_{L}\) value can resist the second rise of total cases number. But their values are dependent on medical, educational, economic, transportation and cultural factors of each country, which should be balanced by local government for contributing to a faster eradication plan. Third, local government should keep tight monitor on the epidemic situation and must take immediate measure against it, such as delivery of drugs and vaccines in large quantity, and immediate isolation of newly infected people, in case there may be unexpected outbreaks in the eradication process. Regional transmission is the root of Ebola spread. We find out that the four important transmission paths are through household, community, hospital and unsafe funeral. The governments should look into these paths and control transmission through each. This can be managed by widespread Ebola protection education campaign, professional funeral treatment, protection regulation.
Based on the model, to lower the infection force of the four paths, such as house hold, community, hospital and unsafe funeral, house hold pathway needs a widespread education campaign on the basic knowledge of Ebola throughout the region because early detection of Ebola symptoms and isolation can better prevent other household from being infected. Community pathway suggested that people should meet as fewer people as possible. When meeting with other people, they should take protective actions, like wearing a respirator. Hospital pathway should be enough isolation of Ebola patients from other patients to decrease infection, more concerned about protecting doctors and nurses from being infected, especially when incubation rate is higher, and should limit or even forbidden household company in the hospital [39, 40].
Conclusions
In summary, we have found out the key paths in different situations and proposed our suggestion to control regional transmission. Ebola eradication needs systematic thinking, effective hospital isolation, and effective EVD drug and vaccination. The desired eradication deadline based on our models can determine the demand of the three weapons against Ebola virus.
Notes
Declarations
Authors’ contributions
Conceived and designed the experiments: JW, SJ, KW. Performed the experiments: SJ, GH Analyzed the data: SJ, CL, GH, XZ, MS Contributed analysis tools: HL, CL. Wrote the manuscript: JW, SJ. All authors read and approved the final manuscript.
Acknowledgements
This research was supported by this work was sponsored by the Grant (16PJ1408800) from the Shanghai Pujiang Program, Shanghai, China. The authors would like thank the Joint Effort on Eradicating Ebola with Government Officers and Colleagues in Medical Field from World Medical Association.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated during and/or analyzed during the current study are available in WHO (http://www.who.int/csr/disease/ebola/situation-reports/en/) (Additional file 1: Table S1).
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- Gatherer D. The 2014 Ebola virus disease outbreak in West Africa. J Gen Virol. 2014;95(Pt 8):1619–24.View ArticlePubMedGoogle Scholar
- Baize S, Pannetier D, Oestereich L, Rieger T, Koivogui L, Magassouba N, et al. Emergence of Zaire Ebola virus disease in Guinea. N Engl J Med. 2014;371(15):1418–25.View ArticlePubMedGoogle Scholar
- WHO. Situation report, Ebola response roadmap. Geneva: World Health Organisation. 2014. http://www.who.int/csr/resources/publications/ebola/response-roadmap/en/. Accessed 5 Feb 2015.
- WHO. Ebola response roadmap situation report. World Health Organization. 2015. http://www.who.int/csr/disease/ebola/situation-reports/en/. Accessed 5 Feb 2015.
- Lewnard JA, Ndeffo Mbah ML, Alfaro-Murillo JA, Altice FL, Bawo L, Nyenswah TG, et al. Dynamics and control of Ebola virus transmission in Montserrado, Liberia: a mathematical modelling analysis. Lancet Infect Dis. 2014;14(12):1189–95.View ArticlePubMedPubMed CentralGoogle Scholar
- Lough S. Lessons from Ebola bring WHO reforms. CMAJ. 2015;187(12):E377–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Stahelin RV. The Ebola virus: from basic research to a global health crisis. PLoS Pathog. 2015;11(8):e1005093.View ArticlePubMedPubMed CentralGoogle Scholar
- World Health Organisation. Ebola virus disease. Aug 2015. Geneva: World Health Organization; 2015.Google Scholar
- Schieffelin JS, Jacob ST. Raising the standard for clinical care of patients with Ebola virus disease. Lancet Infect Dis. 2015;15(11):1247–8.View ArticlePubMedGoogle Scholar
- Fahnrich C, Denecke K, Adeoye OO, Benzler J, Claus H, Kirchner G, et al. Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa. Euro Surveill. 2015;20(12):21071View ArticlePubMedGoogle Scholar
- Towers S, Patterson-Lomba O, Castillo-Chavez C. Temporal variations in the effective reproduction number of the 2014 west Africa ebola outbreak. PLoS Curr. 2014. doi:https://doi.org/10.1371/currents.outbreaks.9e4c4294ec8ce1adad283172b16bc908
- Webb G, Browne C, Huo X, Seydi O, Seydi M, Magal P. A model of the 2014 ebola epidemic in west Africa with contact tracing. PLoS Curr. 2015. doi:https://doi.org/10.1371/currents.outbreaks.846b2a31ef37018b7d1126a9c8adf22a.PubMedPubMed CentralGoogle Scholar
- Yamin D, Gertler S, Ndeffo-Mbah ML, Galvani AP. Effect of Ebola progression on transmission and control in Liberia. Ann Intern Med. 2015;162(1):11–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Pandey A, Atkins KE, Medlock J, Wenzel N, Townsend JP, Childs JE, et al. Strategies for containing Ebola in West Africa. Science. 2014;346(6212):991–5. doi:https://doi.org/10.1126/science.1260612.View ArticlePubMedPubMed CentralGoogle Scholar
- Rivers CM, Lofgren ET, Marathe M, Eubank S, Lewis BL. Modeling the impact of interventions on an epidemic of ebola in sierra leone and liberia. PLoS Curr. 2014. doi:https://doi.org/10.1371/currents.outbreaks.fd38dd85078565450b0be3fcd78f5ccf.Google Scholar
- Hsieh YH. Temporal course of 2014 Ebola virus disease (EVD) outbreak in West Africa elucidated through morbidity and mortality data: a tale of three Countries. PLoS ONE. 2015;10(11):e0140810. doi:https://doi.org/10.1371/journal.pone.0140810.View ArticlePubMedPubMed CentralGoogle Scholar
- Adams B. Household demographic determinants of Ebola epidemic risk. J Theor Biol. 2015. doi:https://doi.org/10.1016/j.jtbi.2015.11.025.Google Scholar
- Faye O, Boelle PY, Heleze E, Faye O, Loucoubar C, Magassouba N, et al. Chains of transmission and control of Ebola virus disease in Conakry, Guinea, in 2014: an observational study. Lancet Infect Dis. 2015;15(3):320–6.View ArticlePubMedPubMed CentralGoogle Scholar
- WHO, Ebola Response Team. Ebola virus disease in West Africa–the first 9 months of the epidemic and forward projections. N Engl J Med. 2014;371(16):1481–95. doi:https://doi.org/10.1056/NEJMoa1411100.View ArticleGoogle Scholar
- WHO, Ebola Response Team. West African ebola epidemic after one year–slowing but not yet under control. N Engl J Med. 2015;372(6):584–7. doi:https://doi.org/10.1056/NEJMc1414992.View ArticleGoogle Scholar
- Baize S, Pannetier D, Oestereich L, Rieger T, Koivogui L, Magassouba N, et al. Emergence of zaire ebola virus disease in guinea. N Engl J Med. 2014;371(15):1418–25.View ArticlePubMedGoogle Scholar
- Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345(6202):1369–72.View ArticlePubMedPubMed CentralGoogle Scholar
- Simon-Loriere E, Faye O, Faye O, Koivogui L, Magassouba N, Keita S, et al. Distinct lineages of ebola virus in guinea during the 2014 West African epidemic. Nature. 2015;524(7563):102–4.View ArticlePubMedGoogle Scholar
- Tong YG, Shi WF, Liu D, Qian J, Liang L, Bo XC, et al. Genetic diversity and evolutionary dynamics of ebola virus in sierra leone. Nature. 2015;24(7563):93–6.View ArticleGoogle Scholar
- Hoenen T, Safronetz D, Groseth A, Wollenberg KR, Koita OA, Diarra B, et al. Virology. Mutation rate and genotype variation of ebola virus from mali case sequences. Science. 2015;348(6230):117–9.View ArticlePubMedGoogle Scholar
- Park DJ, Dudas G, Wohl S, Goba A, Whitmer SL, Andersen KG, et al. Ebola virus epidemiology, transmission, and evolution during seven months in sierra leone. Cell. 2015;161(7):1516–26.View ArticlePubMedPubMed CentralGoogle Scholar
- Carroll MW, Matthews DA, Hiscox JA, Elmore MJ, Pollakis G, Rambaut A, et al. Temporal and spatial analysis of the 2014–2015 ebola virus outbreak in west africa. Nature. 2015;524(7563):97–101.View ArticlePubMedGoogle Scholar
- Kugelman JR, Wiley MR, Mate S, Ladner JT, Beitzel B, Fakoli L, Taweh F, et al. Monitoring of ebola virus makona evolution through establishment of advanced genomic capability in liberia. Emerg Infect Dis. 2015;21(7):1135–43.View ArticlePubMedPubMed CentralGoogle Scholar
- Chretien JP, Riley S, George DB. Mathematical modeling of the West Africa Ebola epidemic. Elife. 2015. doi:https://doi.org/10.7554/eLife.09186.PubMedPubMed CentralGoogle Scholar
- WHO. (2016) Ebola virus disease outbreak. World Health Organization. http://www.who.int/csr/disease/ebola/en/.
- Pavot Vincent. Ebola virus vaccines: where do we stand? Clinical. Immunology. 2016;173:44–9.Google Scholar
- Chowell G, Hengartner NW, Castillo-Chavez C, Fenimore PW, Hyman JM. The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. J Theor Biol. 2004;229(1):119–26.View ArticlePubMedGoogle Scholar
- Althaus, C.L. Estimating the reproduction number of Ebola virus (EBOV) during the 2014 outbreak in West Africa. PLoS Curr. 2014;6. doi:https://doi.org/10.1371/currents.outbreaks.91afb5e0f279e7f29e7056095255b288
- Backer JA, Wallinga J. Spatiotemporal analysis of the 2014 Ebola epidemic in West Africa. PLoS Comput Biol. 2016;12(12):e1005210.View ArticlePubMedPubMed CentralGoogle Scholar
- Merler S, Ajelli M, Fumanelli L, Gomes MF, Piontti AP, Rossi L, et al. Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis. Lancet Infect Dis. 2015;15(2):204–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Deen J, Dondorp AM, White NJ. Treatment of Ebola. N Engl J Med. 2015;372(17):1673–4.View ArticlePubMedGoogle Scholar
- Chen RT, Carbery B, Mac L, Berns KI, Chapman L, Condit RC, et al. The Brighton collaboration viral vector vaccines safety working group (V3SWG). Vaccine. 2015;33(1):73–5.View ArticlePubMedGoogle Scholar
- Flessa S, Marx M. Ebola fever epidemic 2014: a call for sustainable health and development policies. Eur J Health Econ. 2016;17:1–4.View ArticlePubMedGoogle Scholar
- Henderson T, Campbell S. Laboratory preparedness: ebola and other emerging infectious diseases. Now that the immediate crisis has passed, what have hospitals in the United States learned? MLO Med Lab Obs. 2015;47(3):8–9.PubMedGoogle Scholar
- Shrivastava SR, Shrivastava PS, Ramasamy J. Ebola disease: infection prevention and control in hospital and community settings. Iran J Nurs Midwifery Res. 2015;20(4):526–7.View ArticlePubMedPubMed CentralGoogle Scholar