<sec>
<title>Objectives</title>
<p>This study aimed to extend an epidemiological model (SEIHFR) to analyze epidemic trends, and evaluate intervention efficacy.</p></sec>
<sec>
<title>Methods</title>
<p>SEIHFR was modified to examine disease transmission dynamics after vaccination for the Ebola outbreak. Using existing data from Liberia, sensitivity analysis of various epidemic scenarios was used to inform the model structure, estimate the basic reproduction number ℜ<sub>0</sub> and investigate how the vaccination could effectively change the course of the epidemic.</p></sec>
<sec>
<title>Results</title>
<p>If a randomized mass vaccination strategy was adopted, vaccines would be administered prophylactically or as early as possible (depending on the availability of vaccines). An effective vaccination rate threshold for Liberia was estimated as 48.74% among susceptible individuals. If a ring vaccination strategy was adopted to control the spread of the Ebola virus, vaccines would be given to reduce the transmission rate improving the tracing rate of the contact persons of an infected individual.</p></sec>
<sec>
<title>Conclusion</title>
<p>The extended SEIHFR model predicted the total number of infected cases, number of deaths, number of recoveries, and duration of outbreaks among others with different levels of interventions such as vaccination rate. This model may be used to better understand the spread of Ebola and develop strategies that may achieve a disease-free state.</p></sec>
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Objectives
Ebola and Marburg viruses (EBOVs and MARVs, respectively) are causative agents of severe hemorrhagic fever with high mortality rates in humans and nonhuman primates. In 2014, there was a major Ebola outbreak in various countries in West Africa, including Guinea, Liberia, Republic of Sierra Leone, and Nigeria. EBOV and MARV are clinically difficult to diagnose and distinguish from other African epidemic diseases. Therefore, in this study, we aimed to develop a method for rapid identification of the virus to prevent the spread of infection. Methods
We established a conventional one-step reverse transcription-polymerase chain reaction (RT-PCR) assay for these pathogens based on the Superscript Reverse Transcriptase-Platinum <i>Taq</i> polymerase enzyme mixture. All assays were thoroughly optimized using <i>in vitro</i>-transcribed RNA. Results
We designed seven primer sets of nucleocapsid protein (NP) genes based on sequences from seven filoviruses, including five EBOVs and two MARVs. To evaluate the sensitivity of the RT-PCR assay for each filovirus, 10-fold serial dilutions of synthetic viral RNA transcripts of EBOV or MARV NP genes were used to assess detection limits of viral RNA copies. The potential for these primers to cross react with other filoviruses was also examined. The results showed that the primers were specific for individual genotype detection in the examined filoviruses. Conclusion
The assay established in this study may facilitate rapid, reliable laboratory diagnosis in suspected cases of Ebola and Marburg hemorrhagic fevers.
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Objectives
This study aims to create a mathematical model to better understand the spread of Ebola, the mathematical dynamics of the disease, and preventative behaviors. Methods
An epidemiological model is created with a system of nonlinear differential equations, and the model examines the disease transmission dynamics with isolation through stability analysis. All parameters are approximated, and results are also exploited by simulations. Sensitivity analysis is used to discuss the effect of intervention strategies. Results
The system has only one equilibrium point, which is the disease-free state (<i>S</i>,<i>L</i>,<i>I</i>,<i>R</i>,<i>D</i>) = (<i>N</i>,0,0,0,0). If traditional burials of Ebola victims are allowed, the possible end state is never stable. Provided that safe burial practices with no traditional rituals are followed, the endemic-free state is stable if the basic reproductive number, <i>R</i><sub>0</sub>, is less than 1. Model behaviors correspond to empirical facts. The model simulation agrees with the data of the Nigeria outbreak in 2004: 12 recoveries, eight deaths, Ebola free in about 3 months, and an <i>R</i><sub>0</sub> value of about 2.6 initially, which signifies swift spread of the infection. The best way to reduce <i>R</i><sub>0</sub> is achieving the speedy net effect of intervention strategies. One day's delay in full compliance with building rings around the virus with isolation, close observation, and clear education may double the number of infected cases. Conclusion
The model can predict the total number of infected cases, number of deaths, and duration of outbreaks among others. The model can be used to better understand the spread of Ebola, educate about prophylactic behaviors, and develop strategies that alter environment to achieve a disease-free state. A future work is to incorporate vaccination in the model when the vaccines are developed and the effects of vaccines are known better.
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All Global Health Security Agenda (GHSA) Steering Group Members remain strongly committed to accelerating measurable progress and implementing concrete commitments toward a world safe and secure from infectious disease threats, recognizing the devastation of the Ebola epidemic and the clear interdependence of health in the 21<sup>st</sup> century. All GHSA Steering Group members reinforced that GHSA is supportive of International Health Regulations implementation, as well as components of other global health security frameworks such as the World Organization for Animal Health Performance of Veterinary Services pathway. The GHSA will continue to focus on multilateral engagement. The GHSA Steering Group is committed to engaging non-state actors and agreed to discuss next steps toward engaging the private sector.
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Objectives
Ebola hemorrhagic fever has killed over 1300 people, mostly in equatorial Africa. There is still uncertainty about the natural reservoir of the virus and about some of the factors involved in disease transmission. Until now, a maximum incubation period of 21 days has been assumed. Methods
We analyzed data collected during the Ebola outbreak (subtype Zaire) in Kikwit, Democratic Republic of the Congo, in 1995 using maximum likelihood inference and assuming a log-normally distributed incubation period. Results
The mean incubation period was estimated to be 12.7 days (standard deviation 4.31 days), indicating that about 4.1% of patients may have incubation periods longer than 21 days. Conclusion
If the risk of new cases is to be reduced to 1% then 25 days should be used when investigating the source of an outbreak, when determining the duration of surveillance for contacts, and when declaring the end of an outbreak.
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