Allele importance for host reservoir classification of SARS-like coronaviruses.

<p>The alleles which were identified as significant for classification by the feature selection algorithm are represented by red points.</p

Probability of 9-year olds testing positive [P(T_a⁺)] for different test sensitivity and specificities, for a true probability of being seropositive of 0.7.

<p>Probability of 9-year olds testing positive [P(T_a⁺)] for different test sensitivity and specificities, for a true probability of being seropositive of 0.7.</p

Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses

<div><p>Despite environmental, social and ecological dependencies, emergence of zoonotic viruses in human populations is clearly also affected by genetic factors which determine cross-species transmission potential. RNA viruses pose an interesting case study given their mutation rates are orders of magnitude higher than any other pathogen – as reflected by the recent emergence of SARS and Influenza for example. Here, we show how feature selection techniques can be used to reliably classify viral sequences by host species, and to identify the crucial minority of host-specific sites in pathogen genomic data. The variability in alleles at those sites can be translated into prediction probabilities that a particular pathogen isolate is adapted to a given host. We illustrate the power of these methods by: 1) identifying the sites explaining SARS coronavirus differences between human, bat and palm civet samples; 2) showing how cross species jumps of rabies virus among bat populations can be readily identified; and 3) <i>de novo</i> identification of likely functional influenza host discriminant markers.</p></div

Best fits for various models to US data at the county level of aggregation and the best fit for the MK model at the state level.

<p>Log likelihoods are quoted relative to that of the saturated model. County-level saturated model log likelihood = −975239008, State-level saturated model log likelihood = −467836121.</p

Proportion of model fits (of 100) which result in the correct vaccination recommendation for a range of transmission settings defined by seroprevalence in 9 year olds (low: P9 = 10%, to high: P9 = 90%).

<p>Rows show results for different test sensitivities and specificities (Se%/Sp%). Columns for survey sizes of 500 and 2000, estimated from a fixed age range of 0–20 year olds. The black dashed lines represent seroprevalence thresholds of 50% and 70%. Red = vaccination is not recommended, green = vaccination is recommended. Low risk no = upper 95% CrI < 50%, med risk no = central estimate <50%, high risk no = upper 95% CrI < 50%, high risk yes = upper 95% CrI above 70%, med risk yes = central estimate >70%, low risk yes = lower 95% CrI >70%. CrI = credible interval.</p

Change in seroprevalence with age at different transmission intensities.

<p>Transmission intensity values are given at the end of each seroprevalence curve.</p

Dengue force of infection estimated at different transmission intensities, from a range of ages and test sensitivities and specificities, fixed survey size of 1000.

<p>A) Very low transmission 10% seroprevalence at age 9, B) medium transmission 50% seroprevalence at age 9, C) high transmission 70% seroprevalence at age 9, D) very high transmission 90% seroprevalence at age 9. The point shows the mean of the 100 mean posterior distribution of the force of infection, the bar the standard deviation, and the horizontal blue line shows the true force of infection.</p

Maximum likelihood parameter estimates for different models to UK commuting data at the district level of aggregation and for the MK, locally constrained model across different levels of aggregation.

<p>Log likelihood values quoted relative to that of the saturated model. Saturated model log likelihood = −9087628 (county), −14941875 (district), −24483851 (ward).</p

Mean time to infection difference matrix for the US.

<p>For each network, the time to infection between 2 nodes is averaged across all connections within a square. Colours in the matrix represent the difference between mean times on the A) MK network B) Assortative network and the data network. Positive values represent slower transmission on the synthetic network. Times to infection are calculated from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002699#pcbi.1002699.e014" target="_blank">equation 5</a>.</p

Times to infection for different nodes for the assortative model in the US at the county level of aggregation with initial seeding A) Los Angeles County, B) Clinton County, Iowa.

<p>Grey dots represent 95% intervals across simulation realisations for the times to infection on the data network.</p