The ebola virus is a horrific disease that is transmitted to people from wild animals. It was first identified in 1976 in 2 simultaneous outbreaks, one in what is now, Nzara, South Sudan, and the other in Yambuku, Democratic Republic of Congo. The latter occurred in a village near the Ebola River, from which the disease takes its name. Fruit bats may be the natural Ebola virus hosts, but Ebola is introduced into the human population through close contact with the blood, secretions, organs or other bodily fluids of a range of other animals such as primates (chimpanzees, gorillas and monkeys) and forest antelope found in the rain forest. Hunting for bush meat is implicated in this process.
The animal/human contact route of transmission is usually responsible for the initial “signal” cases. However the disease then spreads in the human population through human-to-human transmission. Unlike zombies, Ebola patients are usually too sick to move. However their bodily fluids are highly contagious, even after death.
Fatality rates vary from between 80% and 50%, depending to some extent on the initial health of affected individuals.
A useful introductory fact sheet on the disease is available on the WHO website.
http://www.who.int/mediacentre/factsheets/fs103/en/
The virus does undergo an incubation period of between 2 and 21 days between first contact and symptoms developing, during which the patient is not contagious. However to simplify modelling this can be ignored, as cases are not usually detectable until symptoms occur.
In May 1995, an international team characterized an outbreak of Ebola hemorrhagic fever (EHF) in Kikwit, Democratic Republic of the Congo.
The key data from this outbreak have been included in the modelling tool linked below.
The total population of Kikwit is around 200,000. However the population at risk of exposure through contact with the initial cases was between 200 and 500 people, as some general isolation measures were fairly quickly put in place such as closing schools and preventing all movement into the area directly affected.
At the time of this outbreak no vaccination was available. Relatively little was still known about the illness.
The data set therefore serves as a baseline “worst case” scenario. Our increased knowledge of the nature of the disease should allow more effective containment of future outbreaks. However general isolation measures were less effective initially in the 2014-2016 outbreak that affected seven West African countries and led to over 10,000 deaths in multiple clusters of cases.
You can track the geographical spread of the 2014- 2016 outbreak using this mapping application.
Your task is to apply the SIR model to predict the outcome of different potential interventions, using the real case data as a baseline. During this early outbreak the only real intervention used by workers on the ground involved limiting the size of the population at risk.
A simple SIR model (ignoring asymptomatic incubation period) has been set up and starting parameters estimated for you using a Bayesian framework in order to match the observed pattern during the 1999 Kikwit outbreak as closely as possible. This is the baseline model.
http://r.bournemouth.ac.uk:3838/Epidemics/Ebola/
The figures can be downloaded from the interface in order to illustrate your results section. This will be explained and demonstrated in the lab session.
Write up your report using the guidance provided in the assignment brief. You should ….
Research the key characteristics of Ebola in order to understand the nature of the disease, its form of transmission and the means through which epidemiological modelling can guide measures taken to control an outbreak. Find some peer reviewed academic references from the web of science and other sources that help deepen your knowledge of the disease. Include this information in your discussion section and fully reference all your sources including any additional non peer reviewed information from public health bodies if used.
Based on your research into the background suggest three different sets of control measures aimed at altering key parameters in the model. Your interventions may either reduce the impact of the disease or control it completely. Use the interface to assess the likely effect of your measures. For example simply isolating the general area where the outbreak would reduce the overall size of the population at risk, but may not affect the pattern of development of the outbreak. Treating the patients symptoms may reduce mortality, but may have little effect on morbidity. Preventing person to person contagion within the population at risk (lowering beta) may reduce spread. Vaccination of the population may produce herd immunity and prevent the outbreak completely, however at lower levels of vaccination the outbreak may still occur. Changes to the length of time that patients are contagious may interact with changes in the probability of contagion to alter dynamics. There are wide a range of possible scenarios available.
All these scenarios (and others) can be modelled mathematically using the SIR model and the results obtained from the model compared with the baseline case history using the interface provided.
Explain how your interventions could affect the model parameters in your methods section by comparing the results of modelling with the empirical data from the Kikwit outbreak. You may also wish to consider an estimate of approximate monetary costs of the interventions and other impacts on the population and economy of the area in general.
In the results and discussion section produce figures that allow clear comparisons to be made between the outcomes. Explain the patterns to the reader concisely and clearly. You can download figures from the interface and investigate actual data values during the development of the outbreak either by hovering over the dynamic figures or by looking at the numerical data tables (which can be exported to Excel).
Discuss the implications of your results, and suggest any improvements to the mathematical modelling framework in order to improve its utility in the real life context of predicting the affect of epidemiological intervention to prevent mortality, morbidity and contagion. You should include a criticism of the model limitations based on your understanding of the assumptions that the model is based on.
The report will consist of three sections. The overall word limit is approximately 2000 words excluding references and figure captions.
Please note that the IBMs used for modelling the Zombie apocalypse at the start of the practical are provided for didactic purposes only, in order to clarify the logic of the SIR model and deepen intuitive understanding. DO NOT INCLUDE ANY NETLOGO OUTPUT IN THE FINAL REPORT. You should only use output from the Ebola model interface in your final report, although you should make notes on the Zombie modelling exercise and the Insight make model in order to answer the questions fully and deepen your understanding of the underlying model.
If you are still unclear with regards to any aspect of this assignment please send an email to dgolicher@bournemouth.ac.uk. The answers to any queries will be posted to the whole class on Brightspace.