chowell et al . ( 1 ) developed a deterministic seir ( susceptible - exposed - infectious - recovered ) compartmental model and a stochastic continuous - time markov chain version ( figure ) . a transmission coefficient , 0 , was assumed to be constant before interventions and reduced transmission after intervention at a constant rate , 1 . the model was fit to cases from the 1995 democratic republic of congo ( drc ) outbreak and the 2000 uganda outbreak by using least squares . the authors concluded that a 2-week delay in the timing of interventions would have increased the final size of the outbreak by a factor of 2 . seir , susceptible - exposed - infectious - removed ; seihfr , susceptible - exposed - infectious - hospitalized - funeral - removed . lekone and finkenstdt ( 4 ) modified the model of chowell et al . for discrete - time , stochastic progression . they fit their model to daily incidence and mortality time series from the 1995 drc outbreak using markov chain monte carlo . this exercise concluded that interventions shortened the epidemic from 950 days to 200 days and reduced total number of cases from 3.5 million to just over 300 . effective reproduction number ( re ) was estimated to decrease to < 1 five days after intervention onset . legrand et al . ( 5 ) accounted for transmission in different contexts through a stochastic model with 6 compartments : susceptible , exposed , infectious , hospitalized , dead - but - not - yet - buried , removed ( figure ) . three transmission coefficients corresponded to community transmission , nosocomial transmission , and transmission at funerals . interventions were assumed to be completely efficient from their onset : no transmission occurred at burials and hospitals , and community transmission was reduced by a multiplier estimated by model fitting . parameters were estimated by fitting the model to incidence data ( drc , 1995 ; uganda , 2000 ) , by using approximate maximum likelihood , and an expression for r0 was derived . after interventions , community transmission was estimated to have been reduced to 88% and 12% of its initial value in the drc and uganda outbreaks , respectively , with respective re of 0.4 ( 95% ci 0.30.6 ) and 0.3 ( 95% ci 0.20.4 ) . the authors acknowledged that the 95% cis around transmission and efficacy estimates were wide and conducted a sensitivity analysis of intervention parameters . this analysis indicated that community transmission was key to epidemic dynamics in uganda , whereas funerals contributed more to transmission in the drc . rapid hospitalization significantly reduced community transmission and barrier nursing practices along with effective isolation of ebola patients controlled the epidemics ( 5 ) . they assumed homogeneous mixing of the population , exponentially or geometrically distributed incubation and infectious periods , and a sudden decay in transmission after intervention . future exercises should explore the consequences of these assumptions . with ideal data , fitted models would be stress - tested to assess their validity , for instance challenging models to predict out - of - fit data . eichner et al . ( 10 ) assumed a log  normally distributed incubation period . ferrari et al . ( 11 ) used maximum likelihood with a chain binomial distribution and regression to estimate r0 . white and pagano ( 12 ) assumed that the number of secondary cases produced by a patient followed a poisson distribution , with expected value r0 . collectively , these studies underscore that practical decisions in modeling dictate trade - offs between fitting to limited data and explicit representation of reality , including interventions . a model with a single transmission rate might fit well to data but might not be useful for decision making that evaluates intervention effects in different transmission contexts . a model with 3 transmission rates might represent transmission in community , nosocomial , and funeral contexts ( e.g. , ) , but the 3 parameters are unlikely to be uniquely identifiable . because ebov typically amplifies during nosocomial transmission , a model with 2 transmission parameters ( community transmission , comprising funeral and household transmission in 1 parameter , and nosocomial transmission ) might represent the best compromise . such a model would enable interventions , such as personal protective equipment and efficient hospitalization of persons with community - acquired ebov infection , to be considered . these include nonexponential incubation and infectious periods ( 13 ) and individual heterogeneity in the generation of secondary infections , including  super - spreaders  ( 14 ) . the extent to which these approximations introduce bias could be understood by developing a range of models , perhaps using versions of the chain binomial model or other generalized contagion processes . all extensive ebov outbreaks involved multiple scales of transmission . at the smallest scale , persons most at risk for infection are those caring for an ebola patient . transmission occurs among households in communities , at hospitals , or at funerals . understanding these between - household contacts finally , understanding connections between settlements by human movements is needed to determine the paths and speed of large - scale spatial spread and therefore the total infected area and domain for surveillance and monitoring . although the assumption of population homogeneity can be justified for models of historical ebov outbreaks , given the limited geographic extent of those outbreaks , models for the 2014 outbreak might need to address heterogeneity in population density and human movements because of the extensive geography involved . two issues new to the 2014 ebov epidemic are underreporting and compliance . to assess underreporting , perhaps comprehensive contact tracing can be performed in a small number of locales and extrapolated . if cases can be identified through 2 independent routes , then case matching can be used to identify the total number of cases ( 15 ) . concerning compliance , the fraction of patients admitted to hospitals and , of those remaining in the community compliance of personal protective equipment among health care workers is central to understanding the role of nosocomial transmission . model fitting is craft as well as science . modeling demands decisions , including what mathematical representations to use , the type and magnitude of variation to be considered , and the values that can be taken as given versus the values still to be estimated . in the face of data scarcity , we suggest that construction of models of the 2014 outbreak would have benefited from a minimal dataset that included 1 ) the mean and variance of the incubation period and symptomatic period , respectively ; 2 ) the probability distribution of secondary cases by infection setting ; and 3 ) compliance with recommendations . for secondary cases , in addition to the average , the commonness of outliers ( super - spreaders ) , the frequency of zeros , and the variance in the distribution need to be known .
to guide the collection of data under emergent epidemic conditions , we reviewed compartmental models of historical ebola outbreaks to determine their implications and limitations . 
 we identified future modeling directions and propose that the minimal epidemiologic dataset for ebola model construction comprises duration of incubation period and symptomatic period , distribution of secondary cases by infection setting , and compliance with intervention recommendations .