Estimating the protective effect of case isolation with transmission tree reconstruction during the Ebola outbreak in Nigeria, 2014.

The mainstream interventions used during the 2014-2016 Ebola epidemic were contact tracing and case isolation. The Ebola outbreak in Nigeria that formed part of the 2014-2016 epidemic demonstrated the effectiveness of control interventions with a 100% hospitalization rate. Here, we aim to explicitly estimate the protective effect of case isolation, reconstructing the time events of onset of illness and hospitalization as well as the transmission network. We show that case isolation reduced the reproduction number and shortened the serial interval. Employing Bayesian inference with the Markov chain Monte Carlo method for parameter estimation and assuming that the reproduction number exponentially declines over time, the protective effect of case isolation was estimated to be 39.7% (95% credible interval: 2.4%-82.1%). The individual protective effect of case isolation was also estimated, showing that the effectiveness was dependent on the speed, i.e. the time from onset of illness to hospitalization.

Dynamics of the pneumonic plague epidemic in Madagascar, August to October 2017.

Transmission potential and severity of pneumonic plague in Madagascar were assessed. Accounting for reporting delay, the reproduction number was estimated at 1.73. The case fatality risk was estimated as 5.5%. Expected numbers of exported cases from Madagascar were estimated across the world and all estimates were below 1 person from August to October, 2017.

A subpopulation model to analyze heterogeneous cell differentiation dynamics.

MOTIVATION: Cell differentiation is steered by extracellular signals that activate a cell type specific transcriptional program. Molecular mechanisms that drive the differentiation can be analyzed by combining mathematical modeling with population average data. For standard mathematical models, the population average data is informative only if the measurements come from a homogeneous cell culture. In practice, however, the differentiation efficiencies are always imperfect. Consequently, cell cultures are inherently mixtures of several cell types, which have different molecular mechanisms and exhibit quantitatively different dynamics. There is an urgent need for data-driven mathematical modeling approaches that can detect possible heterogeneity and, further, recover the molecular mechanisms from heterogeneous data. RESULTS: We develop a novel method that models a heterogeneous population using homogeneous subpopulations that evolve in parallel. Different subpopulations can represent different cell types and each subpopulation can have cell type specific molecular mechanisms. We present statistical methodology that can be used to quantify the effect of heterogeneity and to infer the subpopulation specific molecular interactions. After a proof of principle study with simulated data, we apply our methodology to analyze the differentiation of human Th17 cells using time-course RNA sequencing data. We construct putative molecular networks driving the T cell activation and Th17 differentiation and allow the cell populations to be split into two subpopulations in the case of heterogeneous samples. Our analysis shows that the heterogeneity indeed has a statistically significant effect on observed dynamics and, furthermore, our statistical methodology can infer both the subpopulation specific molecular mechanisms and the effect of heterogeneity. AVAILABILITY AND IMPLEMENTATION: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/subpop/ CONTACT: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.