Default model parameters of the basic model.

*<p>Average transmission and discharge rate in the US (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#s2" target="_blank">methods</a> section); for age-class dependent rates see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat-1003134-g001" target="_blank">Figure 1</a>.</p>**<p>Average number of antibiotic prescriptions in the US (data from the data from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS)); see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat.1003134.s005" target="_blank">Table S1</a>.</p>***<p>Polk et al <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat.1003134-Polk1" target="_blank">[20]</a> measured antibiotic use in 130 US hospitals. They found that 60% of all discharged patients received at least one dose of an antibacterial drug. With an average length of stay of 4 days (see *) a daily treatment rate of 0.2 leads to 0.55% of patients receiving treatment during their stay.</p>#<p><i>f_X,Y</i> was calculated as the fraction of drugs used in setting X that are effective against the strain Y (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#s2" target="_blank">methods</a> section).</p>##<p>The basic reproductive ratio of CA-MRSA in community was determined under the assumption that this strain pays no cost of resistance; i.e. it has the same transmission rate as methicillin-sensitive <i>S aureus</i>. The latter colonizes about 30% of individuals in the community and with an R₀ of 1.4 the expected prevalence is 1-1/1.4 = 0.29.</p>###<p>The single-admission basic reproductive number of HA-MRSA in the hospital corresponds to the number of secondary infections caused by a single colonized individual admitted to a hospital containing only susceptible individuals and is given by the dominant eigenvalue of the next-generation matrix B V⁻¹. The matrix B is given by B_ij = <i>β_HA,H^ij Sⁱ,</i> [with <i>Sⁱ</i> the frequency of susceptibles of age-class <i>i</i> in the disease-free equilibrium in the hospital. In the absence of age structure <i>Sⁱ = a/d for all i</i>. In the presence of age structure <i>Sⁱ = a_i/d_i *</i>(the proportion of the total population in age group <i>i</i>) (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat-1003134-g002" target="_blank">Figure 2</a>)]. The matrix V is given by V_ij = <i>d_j+c_BL+τ_H c_T f_H,HA</i> for <i>i = j</i> and V_ij = 0 for <i>i≠j</i>.</p

Coexistence between HA-MRSA and CA-MRSA in the transient phase after the introduction of CA-MRSA into the HA-MRSA-infected host population in the treatment- and age-structured model (corresponding to the red area in Figure 5).

<p>Colors indicate which strains have frequencies >5% among the colonized patients in the hospital (HA-MRSA) and the community (CA-MRSA): Blue indicates coexistence (i.e. both strains >5%), dark grey indicates HA-MRSA only, and light grey CA-MRSA only. The dashed orange line delimits the parameter region in which HA-MRSA can invade MSSA (criterion for invasion: frequency of MRSA >5%, 50 years after its introduction; see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat.1003134.s003" target="_blank">Figure S2</a>).</p

Distribution of the US population over age classes, age dependency of hospitalizations, treatment rates, and durations of hospitalization in the US.

<p>Data are shown for the 18 age classes used in the age-structured model.</p

Flow diagram of the age structured model.

<p>Susceptible individuals (S), which are structured into multiple age classes, can be colonized by CA- or HA-MRSA in the community or in the hospital. Colonized individuals, which are also structured by age, clear the pathogen either by treatment or through natural immune clearance. Individuals move between the hospital and the community at the same rate regardless of colonization status.</p

The blue area indicates the parameter combinations for which HA-MRSA and CA-MRSA coexist in the treatment-structured model.

<p>The red area indicates coexistence in the treatment- and age-structured model. Axes and parameter values are the same as in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat-1003134-g003" target="_blank">Figure 3</a>.</p

Parameter range for which HA-MRSA and CA-MRSA coexist.

<p>The blue area indicates the parameter-combinations for which HA-MRSA and CA-MRSA coexist. The dark grey region indicates the parameter-combinations in which HA-MRSA cannot be invaded by CA-MRSA. The light-grey region indicates parameter-combinations in which CA-MRSA cannot be invaded by HA-MRSA. The range between the two red lines corresponds to fitness costs for which selection in the hospital and community act in opposite directions (i.e. CA-MRSA is fitter in the community and HA-MRSA is fitter in the hospital). The x-axis corresponds to the fitness disadvantage of HA-MRSA compared to CA-MRSA in the absence of effective therapy. The y-axis corresponds to the average number of secondary infections caused by a single colonized individual admitted to a hospital containing only susceptible individuals (single-admission reproduction number <i>R₀^HA,H</i><a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat.1003134-Cooper1" target="_blank">[41]</a>, see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003134#ppat-1003134-t001" target="_blank">Table 1</a>).</p

A) The blue area indicates the parameter combinations for which HA-MRSA and CA-MRSA coexist in the basic model.

<p>The red area indicates coexistence in the age-structured model. <b>B</b>) The blue area indicates the parameter combinations for which HA-MRSA and CA-MRSA coexist in the treatment-structured model. The red area indicates co-existence in the treatment- and age-structured model. The x-axis corresponds to the fitness disadvantage of HA-MRSA compared to CA-MRSA in the community in the absence of effective therapy. The y-axis corresponds to ratio between the fitness costs of HA-MRSA in hospital and community.</p

Coexistence between MSSA, HA-MRSA and CA-MRSA in the transient phase after the introduction of CA-MRSA into the HA-MRSA/MSSA-infected host population in the treatment- and age-structured model.

<p>Colors (see legend) indicate which strains have frequencies >5% among the colonized patients in the hospital (HA-MRSA) and the community (MSSA/CA-MRSA).</p

RIPASSO

Table S4. Complete table of data extracted from each publication included in this review. (XLSX 203 kb

Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US

<div><p>Introduction</p><p>Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.</p><p>Material and Methods</p><p>We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5–29, 30–59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.</p><p>Results</p><p>Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).</p><p>Conclusion</p><p>Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.</p></div