Complexity
May/June 2015 Volume 20, Issue 5 Pages C1–C1, 1–76
http://onlinelibrary.wiley.com/doi/10.1002/cplx.v20.5/issuetoc

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The Simply Complex
Ebola—challenge and revival of theoretical epidemiology: Why Extrapolations from early phases of epidemics are problematic
Peter Schuster*
Article first published online: 28 APR 2015
DOI: 10.1002/cplx.21694
[Initial text]
At the beginning of the second half of the 20th century, there was a widespread belief that science and in particular medicine had progressed so far that Nature could be brought under complete control. It seemed that healthcare and pharmacology were in the position to prevent or to cure almost all diseases. In the 1980s, for example, the pharmaceutical industry stopped the search for new antibiotic drugs that would be badly needed nowadays in the light of the universal capabilities of bacteria to develop resistance factors. At about the same time previously unknown or unnoticed virus transmitted infectious human diseases appeared: acquired immunodeficiency syndrome caused by human immunodeficiency virus (HIV), Ebola caused by Ebola virus (EBOV) and four related other strains of filoviridae, as well as severe acquired respiratory syndrome (SARS) brought about by SARS coronavirus. Caused by prions and not by a virus is been bovine spongiform encephalopathy (BSE). Nevertheless, it gave rise to an equally serious new epidemic. These and other cases as well as the consequences of the “antivaccination movement” [1, 2], for example, the recent reoccurrence of pertussis and measles, revived a need of reliable models in epidemiology. In particular, the recent Ebola epidemic starting in December 2013 in West Africa [3] initiated a new boom in theoretical work on infectious disease dynamics [4]. In PLoS Currents Outbreaks I counted 27 articles between the first publication on the recent Ebola epidemics on May 02, 2014 until March 09, 2015. In December 2014, researchers became aware that the predictions made 3 months earlier, in Fall 2014, apparently overstated the numbers of cases and deaths. A recent theoretical paper aims at an analysis of the prediction errors and provides suggestions how to make better forecasts [5]. In this essay, we shall be concerned with the predictive power of one frequently used model denoted as susceptible-exposed-infectious-removed (SEIR) model, and try to analyze typical general problems of predictions from early stages of exponentially growing systems to the final outcomes of the processes. In the focus are the model inherent limitations of reliabilities and not the lack of information or external problems like insufficient data or the uncertainty about the effectiveness of intervention strategies or countermeasures…