Diagram for how antibiotic prophylaxis is modeled for YBF_S hosts.

<p>See the text and associated tables for more details.</p

Model structure.

<p>(A) Compartment model for single infection with pandemic influenza virus. (B) Compartment model for single infection with bacteria. (C) Compartment model for virus – bacterial co-infection in influenza pandemics. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029219#pone-0029219-t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029219#pone-0029219-t002" target="_blank">Table 2</a> for definition of the variables and parameters, and see the text for more details about the model description.</p

Parameters in the influenza – bacteria co-infection model.

<p>Parameters in the influenza – bacteria co-infection model.</p

Variables in the influenza virus – bacterial co-infection model.

<p>Variables in the influenza virus – bacterial co-infection model.</p

Sensitivity analysis.

<p>Tornado plot of number needed to be prophylaxed (NNP) to prevent one case of pneumococcal pneumonia with ±10% changes in parameters when the initial pneumococcal prevalence is 10%.</p

The estimated incidence of pneumococcal pneumonia (IPP) per 1000 in countries with and without a PCV program under different pneumococcal prevalence and effective reproductive number (R_E).

<p>The estimated incidence of pneumococcal pneumonia (IPP) per 1000 in countries with and without a PCV program under different pneumococcal prevalence and effective reproductive number (R_E).</p

The estimated number needed to be prophylaxed to prevent one case of pneumococcal pneumonia (NNP) in countries with and without a PCV program under different pneumococcal prevalence and effective reproductive number (R_E).

<p>The estimated number needed to be prophylaxed to prevent one case of pneumococcal pneumonia (NNP) in countries with and without a PCV program under different pneumococcal prevalence and effective reproductive number (R_E).</p

Symptoms associated with adverse dengue fever prognoses at the time of reporting in the 2015 dengue outbreak in Taiwan

<div><p>Background</p><p>Tainan experienced the most severe dengue epidemic in Taiwan in 2015. This study investigates the association between the signs and symptoms at the time of reporting with the adverse dengue prognoses.</p><p>Methods</p><p>A descriptive study was conducted using secondary data from the Dengue Disease Reporting System in Tainan, Taiwan, between January 1 and December 31, 2015. A multivariate stepwise logistic regression was used to identify the risk factors for the adverse prognoses: ICU admissions and mortality.</p><p>Results</p><p>There were 22,777 laboratory-confirmed reported cases (mean age 45.6 ± 21.2 years), of which 3.7% were admitted to intensive care units (ICU), and 0.8% were fatal. The most common symptoms were fever (92.8%), myalgia (26.6%), and headache (22.4%). The prevalence of respiratory distress, altered consciousness, shock, bleeding, and thrombocytopenia increased with age. The multivariate analysis indicated that being in 65–89 years old age group [Adjusted Odds Ratio (aOR):4.95], or the 90 years old and above age group (aOR: 9.06), and presenting with shock (aOR: 8.90) and respiratory distress (aOR: 5.31) were significantly associated with the risk of ICU admission. While old age (aOR: 1.11), respiratory distress (aOR: 9.66), altered consciousness (aOR: 7.06), and thrombocytopenia (aOR: 2.55) were significantly associated with the risk of mortality.</p><p>Conclusions</p><p>Dengue patients older than 65 and those with severe and non-specific signs and symptoms at the time of reporting were at a higher risk of ICU admission and mortality. First-line healthcare providers need to be aware of the varied presentations between the different age groups to allow early diagnosis and in-time management, which would prevent ICU admissions and fatalities in dengue patients.</p></div

Characteristics of the 22,777 patients with dengue in Tainan.

<p>Characteristics of the 22,777 patients with dengue in Tainan.</p

Multivariate stepwise logistic regression analysis for mortality (N = 22, 767).

<p>Multivariate stepwise logistic regression analysis for mortality (N = 22, 767).</p