Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization

Abstract

We present a stochastic transmission chain simulation model for Ebola viral disease (EVD) in West Africa, with the salutary result that the virus may be more controllable than previously suspected. The ongoing tactics to detect cases as rapidly as possible and isolate individuals as safely as practicable is essential to saving lives in the current outbreaks in Guinea, Liberia, and Sierra Leone. Equally important are educational campaigns that reduce contact rates between susceptible and infectious individuals in the community once an outbreak occurs. However, due to the relatively low R 0 of Ebola (around 1.5 to 2.5 next generation cases are produced per current generation case in naïve populations), rapid isolation of infectious individuals proves to be highly efficacious in containing outbreaks in new areas, while vaccination programs, even with low efficacy vaccines, can be decisive in curbing future outbreaks in areas where the Ebola virus is maintained in reservoir populations.

An Agent-Based Model of School Closing in Under-Vacccinated Communities During Measles Outbreaks

“…Going back to the work of Frost and Reed, a second approach to modeling epidemics has been to follow transmission chains (incidence and offspring distributions, transmission trees and branches), generally considered within the framework of semi-Markov branching process [5]. While the first paradigm is most useful for large-scale epidemics involving infection of a significant fraction of the susceptible population (i.e., > 1%), which is the case inter alia for influenza-HIV, tuberculosis, and measles in unvaccinated communities; the second is most useful for emerging diseases when the proportion infected is often very small (i.e., < 0.1%)-which includes inter alia, SARS [24], Ebola [15], and hantavirus, as well as measles in communities where vaccination rates are close to herd-immunity threshold levels [7,8].…”

“…5 (which only counts incidence at an aggregated group level). Here we took this individualbased approach, because we wanted to keep track of the 'next-generation distribution' to obtain estimates of R 0 from this distribution, with the details of how to do this described elsewhere [25,15]. We also wanted to follow individuals as they are influenced by spatial factors: in our case children in the local community environments with different vaccination coverage rates and including time spent at schools where transmission rates are higher.…”

“…As mentioned above, the model was built using the NOVA modeling platform, which is downloadable for free at the novamodeler website [35,15,14]. The NOVA file for the model is available at the Getz Lab Website (Nova Models download: Measles, Spring Simulation Conference, 2016).…”

Using Nova to construct agent-based models for epidemiological teaching and research

“…Over the total epidemic (i.e. a distribution generated from all cases), if the epidemic is sufficiently large (≥ 50) then we can expect R eff ≈ 1 (Getz et al 2015).…”

“…The first paradigm is most useful for large-scale epidemics involving infection of a significant fraction of the susceptible population (> 1%)-which, for example, is the case for influenza, HIV, tuberculosis, and measles prior to widespread vaccination. The Reed-Frost approach is more appropriate for emerging diseases (Jones et al 2008) when the proportion of infected individuals is often very small (< 0.1%)-which includes inter alia, SARS (Lloyd-Smith et al 2003), EVD (Getz et al 2015), and the recent measles outbreak in the US (McCarthy 2015).…”