56 research outputs found

    Inferred regional contact patterns at home.

    No full text
    <p>Countries of the world were group into 7 regions (East Asia & Pacific, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, North America, South Asia and Sub-Saharan Africa). The regional mean age-specific contact patterns at home (inferred) of individuals aged 5–10 (first column), 25–30 (second column) and 55–60 (third column) years were represented as bars.</p

    Population and household age distribution, and age-specific contacts at home.

    No full text
    <p>The population pyramids by age and gender (panels a–c), household age matrices (panels d–f) and age-specific contact patterns (panels g–i) are presented for Germany (first column, as a representative of the POLYMOD countries), Bolivia (second column, as a representative of DHS) and South Africa (third column, as a representative of ROW). The population pyramids, panels a–c, and household age matrices (for only POLYMOD and DHS), panels d–e, are observed data. The age-specific contacts at home for Germany (g) is estimated from our hierarchical model. The household age matrix for South Africa (f) and the age-specific contacts at home for Bolivia (h) and South Africa (i) were projected using the described methods. Darker color intensities indicate more likely events i.e. greater tendency of having a household member of that age, higher proclivity of making the age-specific contact.</p

    Age-specific contact patterns by location.

    No full text
    <p>The age-specific contact patterns at home (panels a–c), at the workplace (panels d–f), in school (panels g–i) and at other locations (panels j–l) are projected from the model. The contact pattern at all locations (panels m–o) is the sum across the four locations (home, work, school and others). Contact matrices for Bolivia (DHS country; in panels b,e,h,k) and South Africa (ROW country; in panels c,f,i,l) were projected and the age-specific mean contact rates for Germany (part of the POLYMOD; in panels a,d,g,j) were estimated from the German contact data. A comparison between the German empirical and modelled estimates can be found in the <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005697#pcbi.1005697.s001" target="_blank">S1 Text</a></b>. Darker color intensities indicate higher proclivity of making the age-specific contact.</p

    Methodology and data.

    No full text
    <p>Overview of the data sources and model framework in the manuscript is presented in this flow chart. The categories of the 152 countries are depicted on the world map (i.e. POLYMOD, Demographic and Health Survey (DHS), and Rest of the World (ROW) countries) and their data sources are listed in the table. A summary of the methodology is represented by the model framework: (A) POLYMOD model, (B) construction age-structured populations at home, work, and school in the 152 countries, and (C) projection of global estimates.</p

    Age-specific final epidemic size and percentage reduction.

    No full text
    <p>The age-specific final epidemic size and percentage reduction of infection for Germany (first column), Bolivia (second column) and South Africa (third column) are shown for the three interventions: No intervention (sum of orange and pink/blue bars), School closure and social distancing of younger individuals (blue bars) and Workplace distancing (pink bars) for two epidemics with <i>R</i><sub>0</sub> of 1.2 and 1.5. The percentage reduction of infection for the various intervention and <i>R</i><sub>0</sub> values are represented by the black lines.</p

    Predictions of epidemic trajectories for site D1.

    No full text
    <p>Predictions are based on observation windows of increasing length, comprising data from the first three (<b>A1</b>, <b>B1</b>, <b>C1</b>, <b>D1</b>), six (<b>A2</b>, <b>B2</b>, <b>C2</b>, <b>D2</b>), and nine (<b>A3</b>, <b>B3</b>, <b>C3</b>, <b>D3</b>) snapshots of disease. Three different assumptions (<b>A</b>, <b>B</b>, <b>C</b>) about our prior information on the future evolution of the system were used, each associated to a different model (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-t001" target="_blank">Table 1</a>). <b>A1–A3</b> Predictions based on model , assuming no prior information. The probability distributions for predicted trajectories are shown by gray shading, with intensity of shading representing probability of occurrence. The observational data (disease snapshots) used for prediction are marked by orange circles, the last snapshot used (the prediction time) by a larger red circle, and the observational data to be predicted by white circles. The total number of hosts in the site is <i>N</i> = 6056. <b>B1–B3</b> Predictions (same conventions as for panels <b>A1–A3</b>) based upon model , with the assumption that the value of <i>ω</i> (the linear decay rate of , cf. gray line in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-g003" target="_blank">Figure 3D,H</a>) is known from the beginning. <b>C1–C3</b>, <b>D1–D3</b> Predictions based upon model (<i>ΔT</i> = 1 month), with constant dispersal parameter <i>α</i>, and monthly rates of transmission (, ) (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-g003" target="_blank">Figure 3H</a>). <b>C1–C3</b> Predicted and observed trajectories (same conventions as in A1–A3). <b>D1–D3</b> The associated secondary infection rates , estimated from observed data, marked by orange circles (coinciding with the mode of the distributions; cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-g003" target="_blank">Figure 3H</a>). Predictions are made under the assumption that the positions and values of the peaks in the time series for (blue circles in panels <b>D1–D3</b>, same as the peaks in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-g003" target="_blank">Figure 3H</a>) are known in advance. A spline interpolator (dark red line in panels <b>D1–D3</b>) is used to impute missing values of .</p

    Main models used in the paper, classified according to the time-dependence of parameters.

    No full text
    <p>Main models used in the paper, classified according to the time-dependence of parameters.</p

    Posterior predictive distributions for the site D1.

    No full text
    <p>Results for the constant-dispersal model , <i>ΔT</i> = 6 months (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-t001" target="_blank">Table 1</a>) are shown for four different intervals (each delimited by times <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, with <i>t</i><sub>1</sub> = <i>t</i><sub>0</sub>+6 months). Parameter estimates obtained for each interval are used to run the model 1000 times between <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, and summary statistics calculated from the output are compared with the data. <b>A, C, F, I</b> Distributions of simulated disease progress between <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub> (shaded areas, with black corresponding to the median and different levels of gray to different quantiles) compared to observed disease progress (red circles; empty black circles mark data not used in the comparison). The total number of hosts in site D1 is <i>N</i> = 6056. <b>B, D, G, J</b> The autocorrelation function at time <i>t</i><sub>1</sub>, , estimated from observed data (thick red line), together with the 95% bootstrapped confidence interval (thin red lines), is compared with the distribution of estimated from simulated epidemics (shaded gray, same as for panels A, C, F, I). Dashed cyan lines represent the 95% significance interval found with random labelling techniques. <b>E, H, K</b> Time-lagged statistics calculated between times <i>t</i><sub>0</sub> and <i>t</i><sub>1</sub>, . Thick red lines are estimated from observed data, thin red lines mark the 95% confidence interval, dashed cyan lines mark the 95% significance intervals, and distributions of estimated from simulated epidemics are shown in shaded gray.</p

    Dispersal kernels as a function of distance.

    No full text
    <p><b>A–D</b> Estimated kernels for the exponential model (orange lines) and the Cauchy model (cyan lines), plotted together as a function of distance for each census site. The functional form of the kernels is based upon <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi.1003587.e009" target="_blank">Equations 3</a>, with a cut-off at very short distances as explained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi.1003587.s012" target="_blank">Text S1</a> (Equations S5). The mean of the posterior distribution for α for model (<i>ΔT</i> = 6 months; cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003587#pcbi-1003587-g003" target="_blank">Figure 3A</a> for the exponential model) is used as a point estimate to plot each kernel. The value of the two kernels at the point where they begin to diverge (“plus” symbol in <b>A–D</b>) is about 10<sup>3</sup> times the value at very short distances.</p
    corecore