text:
\section*{Abstract}
Public health surveillance systems often fail to detect emerging infectious diseases, particularly in resource limited settings. By integrating relevant clinical and internet-source data, we can close critical gaps in coverage and accelerate outbreak detection. Here, we present a multivariate algorithm that uses freely available online data to provide early warning of emerging influenza epidemics in the US. We evaluated 240 candidate predictors and found that the most predictive combination does \textit{not} include surveillance or electronic health records data, but instead consists of eight Google search and Wikipedia pageview time series reflecting changing levels of interest in influenza-related topics. In cross validation on 2010-2016 data, this algorithm sounds alarms an average of 16.4 weeks prior to influenza activity reaching the Center for Disease Control and Prevention (CDC) threshold for declaring the start of the season. In an out-of-sample test on data from the rapidly-emerging fall wave of the 2009 H1N1 pandemic, it recognized the threat five weeks in advance of this surveillance threshold. Simpler algorithms, including fixed week-of-the-year triggers, lag the optimized alarms by only a few weeks when detecting seasonal influenza, but fail to provide  early warning in the 2009 pandemic scenario. This demonstrates a robust method for designing next generation outbreak detection algorithms. By combining scan statistics with machine learning, it identifies tractable combinations of data sources (from among thousands of candidates) that can provide early warning of emerging infectious disease threats worldwide.

\section*{Author summary}
Early detection of infectious disease outbreaks enable targeted interventions that prevent transmission and mitigate disease burden. However, we lack rapid surveillance systems for many global threats. This paper introduces a hierarchical statistical method for evaluating diverse data sources and incorporating them into powerful outbreak detection algorithms. We apply the method to design a next generation early warning system for influenza epidemics in the US. By monitoring online Google and Wikipedia search activity for information relating to influenza symptoms and treatment, our algorithm can detect the emergence of seasonal influenza months before the official start of the season.

\linenumbers

\section*{Introduction}
Emerging and re-emerging human viruses threaten global health and security. Early warning is vital to preventing and containing outbreaks. However, viruses often emerge unexpectedly in populations that lack resources to detect and control their spread. The silent Mexican origin of the 2009 pandemic \cite{Mexico2009, Fraser2009Pandemic}, unprecedented 2014-2015 expansion of Ebola out of Guinea \cite{Baize2014Ebola}, and the rapid spread of Zika throughout the Americas in 2016-2017 \cite{Zhang2017zika} highlighted critical shortcomings and the potential for life-saving improvements in global disease surveillance.

Traditionally, public health agencies have relied on slow, sparse and biased data extracted during local outbreak responses or collected via voluntarily reporting by healthcare providers. The 21st century explosion of health-related internet data--for example, disease-related Google searches, Tweets, and Wikipedia term visits--and the proliferation of pathogen molecular data and electronic health records have introduced a diversity of real-time, high-dimensional, and inexpensive data sources that may ultimately be integrated into or even replace traditional surveillance systems. In building 'nextgen' surveillance systems, we face the interdependent challenges of identifying combinations of data sources that can improve early warning and developing powerful statistical methods to fully exploit them.

Engineers have designed anomaly detection methods for statistical process control (SPC)---including the Shewhart \cite{Shewhart1931}, cumulative sum (CUSUM) \cite{page1954cusum, Lorden1971cusum}, and exponential weighted moving average (EWMA) methods \cite{Roberts1959ewma}---to achieve real-time detection of small but meaningful deviations in manufacturing processes from single or multiple input data streams. When the focal process is \textit{in-control}, these methods assume that the inputs are independent and identically distributed random variables with distributions that can be estimated from historical data. Anomalous events can thus be detected by scanning real-time data for gross deviations from these baseline distributions.

Biosurveillance systems similarly seek to detect changes in the incidence of an event (e.g., infections) as early and accurately as possible, often based on case \textit{count} data. By adjusting SPC methods to account for autocorrelations, researchers have developed algorithms that can detect the emergence or re-emergence of infectious diseases \cite{Fricker2013}. Such methods have been applied to influenza \cite{Cowling2006flu, Griffin2009flu, Boyle2011influenza, Pervaiz2012flu, Mathes2017}, Ross River disease \cite{Watkins2008ross, Pelecanos2010ross}, hand-foot-and-mouth disease \cite{Li2011handfoot, Zhang2013handfoot, Lai2017handfoot}, respiratory tract infections \cite{wieland2007respiratory, Spanos2012respiratory, Mathes2017}, meningitis \cite{karami2017meningitis}, and tuberculosis outbreaks \cite{Kammerer2013tuberculosis}. These models exploit a variety of public health data sources, including syndromic surveillance, case count and laboratory test data. While they achieve high sensitivity and precision, alarms typically sound once an outbreak has begun to grow exponentially and thus do not provide ample early warning. For annual influenza, CUSUM-derived detection methods applied to Google Flu Trends data sound alarms an average of two weeks prior to the official start of the influenza season \cite{Pervaiz2012flu}.

The Early Aberration Reporting System (EARS) \cite{Hutwagner2003ears} was launched by the CDC in 2000s to provide national, state, and local health departments with several CUSUM-derived methods to facilitate the syndromic surveillance. The BioSense surveillance system \cite{Bradley2005biosense} implements methods derived from EARS to achieve early detection of possible biologic terrorism attacks and other events of public health concern on a national level. Two other surveillance systems, ESSENCE and NYCDOHMH \cite{Bravata2004ESSENCE, Shmueli2010NYCDOHMH}, maintained by United States Department of Defense and the New York City Department of Health and Mental Hygiene, respectively, implement EWMA-based methods for outbreaks monitoring. Most of these systems are univariate (i.e., analyze a single input data source) and consider only public health surveillance data collected during local outbreak responses or via voluntarily reporting by healthcare providers. The time lag between infection and reporting can be days to weeks. Thus, the earliest warning possible for an emerging outbreak may be well after cases begin rising.

Over the last decade, public health agencies and researchers have begun to explore a variety of 'nextgen' disease-related data sources that might improve the spatiotemporal resolution of surveillance. Electronic health records (EHR) systems like athenahealth can provide near real-time access to millions of patient records, nationally, and have been shown to correlate strongly with influenza activity \cite{Santillana2016athena}. Participatory surveillance systems like Flu Near You, which asks volunteers to submit brief weekly health reports, also provide a near real-time view of ILI activity \cite{Chunara2013fny}. However, such data sources may be geographically, demographically or socioeconomically biased, depending on the profiles of participating healthcare facilities or volunteers \cite{Brownstein2017FNYbias}. Internet-source data such as Google Trends \cite{Ginsberg2009gft}, Wikipedia page views \cite{McIver2014wiki, Hickmann2015wiki}, and Twitter feeds \cite{Broniatowski2013twitter} exhibit correlations with disease prevalence, and have been harnessed for seasonal influenza nowcasting and forecasting. However, they have not yet been fully evaluated for early outbreak detection, and may be sensitive to sociological perturbations, including media events and behavioral contagion \cite{Chan2011, Chunara2012}.

Here, we introduce a hierarchical method for building early and accurate outbreak warning systems that couples a multivariate version of EWMA model with a forward feature selection algorithm (MEWMA-FFS). The method can evaluate thousands of data sources and identify small combinations that maximize the timeliness and sensitivity of alarms while achieving a given level of precision. It can be applied to any infectious disease threat provided sufficient data for the candidate predictors. For novel threats, the candidates may include a wide variety of proxies that are expected to produce dynamics resembling the focal threat (e.g., data on closely related pathogens, other geographic regions, or even social responses to non-disease events).

To demonstrate the approach, we design a multivariate early warning system for seasonal influenza using eight years of historical data (2009-2017) and hundreds of predictors, including traditional surveillance, internet-source, and EHR data. The optimal combination of input data includes six Google and two Wikipedia time series reflecting online searches for information relating to the symptoms, biology and treatment of influenza. By monitoring these data, the algorithm is expected to detect the emergence of seasonal influenza an average of $16.4$ weeks (and standard deviatiation of $3.3$ weeks) in advance of the Center for Disease Control and Prevention (CDC) threshold for the onset of the season. In out-of-sample validation, the model detected the fall wave of the 2009 H1N1 pandemic and the 2016-2017 influenza season five and fourteen weeks prior to this threshold, respectively.

\section*{Materials and methods}
\subsection*{Early detection model}
The MEWMA model is derived from a method described in \cite{Joner2008mewma}. We define one time series as \textit{gold standard}, and one value in the range of the gold standard as the event threshold. Events (outbreaks) correspond to periods when observations in the gold standard cross and remain above the event threshold. We project the timing of events in the gold standard time series onto the candidate time series (predictors). We assume that the data falling outside the event periods follow a multivariate normal distribution $\boldsymbol{F}$ (null distribution) with a mean vector $\boldsymbol{\mu}$ and covariance matrix $\boldsymbol{\Sigma}$ that can be estimated from baseline (non-outbreak) data with equations [\ref{mu}] and [\ref{cov}]:

\begin{equation}
\boldsymbol{\mu} = \mathbb{E}(\boldsymbol{X_T} | y_{\boldsymbol{T}} < \varepsilon)
\label{mu}
\end{equation}

\begin{equation}
\boldsymbol{\Sigma} = \text{Cov}(\boldsymbol{X_T} | y_{\boldsymbol{T}} < \varepsilon)
\label{cov}
\end{equation}
Here, $\varepsilon$ is the value of the threshold defining outbreak events. $\boldsymbol{T}$ are all time points at which observations in gold standard $y$ are below event threshold $\varepsilon$. $\boldsymbol{X}_{\boldsymbol{T}}$ is a matrix of observations from candidate time series at time points $\boldsymbol{T}$.

At each time $t$, MEWMA calculates
\begin{equation}
\textbf{S}_t=
\begin{cases}
\max[\textbf{0}, \lambda(\textbf{X}_t - \boldsymbol{\mu})+(1-\lambda)\textbf{S}_{t-1}], & \text{for}\ t>0 \\
\textbf{0}, & \text{for}\ t=0
\end{cases}
\end{equation}
where $\boldsymbol{X}_t$ is a vector of current observation from candidate time series; $\lambda$ is the smoothing parameter $(0 < \lambda < 1)$; $\boldsymbol{S}_t$ is a weighted average of the current observation standardized around $\boldsymbol{\mu}$ and the previous $\boldsymbol{S}$ statistic. Then the multivariate EWMA test statistic $\boldsymbol{E}_t$ is calculated as
\begin{equation}
\boldsymbol{E}_t = \boldsymbol{S}_t^T \boldsymbol{\Sigma}^{-1}_{\boldsymbol{S}_{\infty}} \boldsymbol{S}_t
\end{equation}
\begin{equation}
\boldsymbol{\Sigma}_{\boldsymbol{S}_{\infty}} = \frac{\lambda}{2 - \lambda} \boldsymbol{\Sigma}
\label{covS}
\end{equation}
The MEWMA signals whenever $\boldsymbol{E}_t$ exceeds a predetermined threshold $h$. That is, the observation at time $t$ deviates significantly from the baseline distribution.

\subsection*{Performance measurement}
Given that our objective is to detect emerging outbreaks early and accurately, we evaluate data based on the timing of alarms relative to the start of events. Only alarms within detection windows are considered as true positive alarms. Specifically, we calculate performance of a candidate system (combination of predictors) as given by
\begin{equation}
P(\boldsymbol{X}, \lambda, h; y) = \frac{1}{N} \sum_{n=1}^{N}(1 - \frac{\Delta T_n} {T_{w}}),
\end{equation}
where $N$ is the total number of events in gold standard, $T_{w}$ is the length of the detection window (e.g., sixteen weeks surrounding the start of an event) and $\Delta T_n$ is the time between the start of the detection window and the first alarm for event $n$. If no alarm sounds during the detection window for event $n$, then $\Delta T_n = T_w$. Performance values range from zero to one. A perfect score of one indicates that alarms consistently sound during the first week of the detection window; 0.5 indicates that alarms occur, on average, right at the start of events; lower values indicate delayed alarms, triggered weeks after the event has begun.

\subsection*{Parameter optimization}
When implementing MEWMA-FFS, we must estimate the smoothing parameter $\lambda$ and the threshold $h$. The parameter pair $(\lambda, h)$ should maximize the performance of the model while minimizing the number of false positive alarms triggered outside detection windows for actual events.

To constrain the number of false positive alarms, we specify the Average Time between False Signals (ATFS) during the training process. This parameter is the expected number of time steps between signals during non-outbreak periods and is given by

\begin{equation}
ATFS \triangleq \mathbb{E}(t^{**} - t^{*} | \tau_s = \infty),
\end{equation}
where $t^{*}$ denotes the time an initial alarm is triggered; $t^{**}$ is the next time an alarm sounds; $\tau_s$ is the first day of an event, with $\tau_s = \infty$ indicating that an event never occurs. The value of ATFS can be estimated using simulations. We first generate samples from the null distribution (data outside event periods), then use the MEWMA procedure described in \ref{mu} - \ref{covS} to trigger alarms, and finally use the spacing between these false alarms to estimate ATFS \cite{Fricker2013}.

To calculate the optimal parameter pair, we begin with fixing a value of ATFS ($\varphi$). Given a set of time series $\boldsymbol{X}$, this constrains the possible choices for parameter pairs $(\lambda, h)$ to a curve $\Gamma(\varphi, \boldsymbol{X})$. The overarching optimization goal is given by
\begin{equation}
\boldsymbol{X}^*, \lambda^*, h^* = \arg \max_{\{\boldsymbol{X}\subset\boldsymbol{\Omega}:|\boldsymbol{X}|=k, (\lambda, h) \in \Gamma(\varphi; \boldsymbol{X}) \}} P(\boldsymbol{X}, \lambda, h; y)
\end{equation}
where $\boldsymbol{X}^*$ is the optimal combination of time series; $\boldsymbol{\Omega}$ is a set of all candidate time series; $k$ is the pre-determined number of time series in the optimization; $\lambda^*$ and $h^*$ are the optimal parameter pair.

To evaluate parameter pairs $(\lambda, h)$ on the curve $\Gamma(\varphi, \boldsymbol{X})$, we consider values of $\lambda$ between zero and one with a step size 0.1. Since ATFS is monotonically increasing in $h$, this allows us to efficiently find the corresponding approximate value of $h$ using the secant method \cite{Allen1998} with the tolerance value of 0.5 and the maximum number of iterations of 100. We plug each resulting parameter pair into the MEWMA model and measure in-sample performance. The parameter pair maximizing the in-sample performance is chosen for out-of-sample prediction.

\subsection*{Forward feature selection}
To choose the optimal combinations of time series for early warning, we implement stepwise forward feature selection algorithm in combination with MEWMA. We begin with no predictors and test the model performance (in terms of the average timing of early detection) when we add each of the possible candidate predictors on their own. We select the time series that most improves model performance as the first predictor. We then repeat the following until we reach a target number of predictors or the model performance levels off: (1) evaluate each \emph{remaining} candidate predictor in combination with predictors already selected for the system and (2) select the candidate that most improves model performance for inclusion in the system. Formally,

\begin{equation}
\boldsymbol{X}_0 := \emptyset\ \text{and}\ \boldsymbol{X}_{i+1} := \boldsymbol{X}_i \cup \Big\{ \arg \max_{x \in \boldsymbol{\Omega} \backslash  \boldsymbol{X}_i } P(\boldsymbol{X}_i \cup {\{x\}} , \lambda, h; y) \Big\}
\end{equation}
where $X_i$ is a set of selected candidate time series at step $i$; $\boldsymbol{\Omega}$ is a set of all candidate time series; $P(\boldsymbol{X}_i \cup {\{x\}} , \lambda, h; y)$ is the performance metric; $y$ is the gold standard; $\lambda$ is the smoothing parameter, and $h$ is the threshold for test statistic.

\subsection*{Optimizing early detection of influenza outbreaks in the US}
We demonstrate the MEWMA-FFS framework by designing an early detection system for influenza in the US using 2010-2016 data. Using national scale ILINet data as the gold standard (described under \emph{Data} below), outbreak events (influenza outbreaks) are defined as ILINet surpassing a specified threshold for at least three weeks. Candidate predictors are selected to detect the onset of influenza outbreaks as early as possible in a specific number of weeks leading up and following the start of each event.

When selecting candidate predictors, all time series are evaluated using six-fold cross-validation. For each fold, one of the six influenza seasons is held out for testing and the other five are used for training. The candidate model is evaluated by the timing of the alarm relative to the actual start of the event, averaged across the six out-of-sample predictions. To constrain false positives, we set the target ATFS to 20 weeks and then choose optimization parameter pairs $(\lambda, h)$ by running 1000 simulations. To reduce the stochasticity of simulation further, each optimization experiment is repeated 40 times and optimal combination of predictors is determined by the median of their ranks.

After building the early detection systems (i.e., selecting optimal combinations of predictors via MEWMA-FFS), we perform two additional rounds of model evaluation. Since the gold standard and predictor data overlap for only six influenza seasons  (2010-2016), we used this data twice: first, as described above, we use six-fold cross-validation (one season held out) to select optimal combinations of predictors for each model; second, we use three-fold cross-validation (two seasons held out) to compare the performance of different optimized models. We report the timing of alarms relative to the official start of each event, the proportion of events detected (recall), and the percentage of true alarms over all alarms (precision) across the three folds. In preliminary analysis, we found that the length of training data does significantly impact model performance (Fig~\ref{ts_length_performance}). Finally, following model construction and comparison on 2010-2016 data, we further evaluate the performance of the best models in comparison to simpler alternatives using true test data from the 2016-2017 influenza season and the fall wave of the 2009 H1N1 influenza pandemic.

Since we do not reset $\boldsymbol{S}_t$ to zero following alarms, systems tend to signal repeatedly until the observations return to baseline. Therefore, we track only the timing of the first alarm  during continuous clusters of alarms. MEWMA without resetting saves on computation during FFS optimization, as it allows us to reference a single set of stored null distribution calculations when testing for alarms. That is, if $\boldsymbol{F}$ is the null distribution for all candidate time series, we can compute and save the mean vector $\boldsymbol{\mu}$, covariance matrix $\boldsymbol{\Sigma}$, and $\boldsymbol{S}_t$ statistic with $\boldsymbol{X}_t$, the vector of observations from all candidate time series at time $t$. Given a subset $\boldsymbol{U}$ of candidate time series, the test statistic $E_t$ can be computed by using the pre-computed $\boldsymbol{S}_t$ and $\boldsymbol{\Sigma}$ directly.

\subsection*{Choosing an event threshold and detection window}
To speed up the optimization experiments, we tune the event threshold $\varepsilon$ and length of detection window $T_{w}$. We run optimization experiments using eleven ILINet time series across a range of values for $\varepsilon$ and $T_{w}$ (\nameref{threshold_detectionWindow_performance_comparison}). We constrain the $T_{w}$ so that the start of the window did not precede the lowest observation in the onset of a given outbreak. As in our primary analysis, predictors are selected using 6-fold cross validation and compared via a secondary round of 3-fold cross-validation. We considered ILINet event thresholds ranging from 1\% to 2\% and detection windows ranging from 4 to 20 weeks surrounding the onset of an event and found that a combination of $\varepsilon=1.25\%$and $T_{w}=16$ maximizes the timeliness, precision and recall (\nameref{threshold_detectionWindow_performance_comparison}).

\subsection*{Assessing the trade-off between run-time and performance}
To evaluate the impact of the ATFS on model performance, we run optimization experiments across ATFS values ranging from 5 to 150. In each experiment, predictors are selected and evaluated through cross-validation as described above. For each ATFS value, we run 40 replicates and record their compute time on the Olympus High Performance Compute Cluster~\cite{olympus}.

\subsection*{Sensitivity analysis}
To evaluate the impact of the training period duration, we run five optimization experiments following the procedures described above, while varying the length of the training time series from 12 years to 4 years: 2004-2016, 2006-2016, 2008-2016, 2010-2016, 2012-2016. To evaluate the importance of including recent data, we run a series of optimization experiments with variable time gaps between the end of a four-year training period and the beginning of a one-year testing period (\nameref{ts_gap_dataVis}).

\subsection*{Alternative models}
We compare our optimized early detection algorithms with three simpler alternatives. All three models were fit via 3-fold cross-validation on 2010-2016 ILINet data, with two seasons held out in each round. When computing performance, we follow the methods described above for the MEWMA-FFS model: We consider only the first alarm in each cluster and assume the same objective function, event threshold, detection window, and ATFS.

  \emph{Week-based trigger}: The model triggers alarms in the same week of every year. Week 34 maximizes the cross-validated performance.

  \emph{Rise-based trigger}: The model triggers alarms as soon as ILINet reports increase for $n$ consecutive weeks. We considered
  $n$ ranging from 2 to 20 weeks and determined that $n=4$ maximizes the cross-validated performance.
 \emph{Univariate-ILINet US}: we fit the MEWMA-FFS model using national level ILINet data as the sole predictor.

\subsection*{Data}
The method evaluates candidate data sources based on ability to detect events in a designated \emph{gold standard} data source. Throughout this study, we use CDC national-scale ILINet data as gold standard and consider the following five categories of candidate data: (a) ILINet; (b) NREVSS; (c) Google Trends; (d) Wikipedia access log; (e) athenahealth EHR.

  ILINet: The CDC complies information on the weekly number of patient visits to healthcare providers for influenza-like illness through the US Outpatient Influenza-like Illness Surveillance Network (ILINet). Current and historical ILINet data are freely available on FLUVIEW \cite{fluview}. We use weekly percentage of ILI patient visits to healthcare providers on both national and Health and Human Services (HHS) scales (which are weighted by state population). The national scale time series serve as our gold standard data, and both national and HHS data are considered as candidate data sources during optimization from 07/03/2009 through 02/06/2017.

  NREVSS: Approximately 100 public health and over 300 clinical laboratories in the US participate in virologic surveillance for influenza through either US World Health Organization (WHO) Collaborating Laboratories System or the National Respiratory and Enteric Virus Surveillance System (NREVSS). All participating labs issue weekly reports providing the total number of respiratory specimens tested and the percent positive for influenza. These data are publicly available on FLUVIEW \cite{fluview}. Our optimization considers both national and HHS scale time series of weekly percentage of specimens positive for influenza from 07/03/2009 through 02/06/2017.

  GT: Google Correlate~\cite{googleCorrelate} and Google Trends~\cite{googleTrends} are freely-available tools developed by Google that enable users to (1) find search terms correlated with user-provided time series and (2) obtain search frequency time series corresponding to user-provided search terms, respectively. We first applied Google Correlate to national scale ILINet data between 01/04/2004 and 5/16/2009 and retrieved the top 100 matches (Table~\nameref{GT_search_terms}). We then applied Google Trends to each of the top 100 search terms to obtain search frequency time series for 07/03/2009 through 02/06/2017. These serve as candidate data sources in our optimization.

  Wikipedia: Wikipedia is widely used as a online reference (nearly 506 million visitors per month) \cite{McIver2014wiki}. Researchers have demonstrated a correlation between US ILINet and time series of access frequencies for English-language Wikipedia articles relating to influenza \cite{McIver2014wiki, Hickmann2015wiki}. Using the Delphi Epidata API \cite{Farrow2016thesis}, we obtained the normalized weekly number of hits for each of 53 influenza-related Wikipedia pages listed in \cite{Hickmann2015wiki} from 07/03/2009 through 02/06/2017 (\nameref{wiki_articles}).

  Athena: athenahealth provides cloud-based services for healthcare providers and manages large volumes of electronic health records data. In collaboration with athenahealth, we obtained the following daily data for approximately 71939 healthcare providers across the US from 07/03/2010 to 02/06/2016: the total number of patient visits, the number of influenza vaccine visits, the number of visits billed with a influenza diagnosis code on the claim, the number of ILI visits, the number of visits ordered a influenza test, the number of visits with a influenza test result, the number of visits with a positive influenza test, and the number of visits with a flu-related prescription. We generated 77 time series total for the following seven variables, each aggregated by week and compiled at the national and HHS scale: (1) ILIVisit---the weekly count of ILI visits; (2) ILI\%---the ratio of the number of ILI visits and the total number of visits; (3) FluVaccine---the weekly count of visits with a influenza vaccine; (4) FluVisit---the weekly count of visits billed with a influenza diagnosis code on the claim; (5) Positive\%---the ratio of the number of visits with a positive influenza test result to the number of visits with a influenza test; (6) FluResult---the number of patient visits with a influenza test result; (7) FluRX---the number of patient visits with a flu-related prescription.

\section*{Results}

\subsection*{Early detection from single data sources}
We first fit the early detection model to each of the 240 candidate time series individually and assess their ability to anticipate when ILINet will cross a threshold of 1.25\%. Performance indicates the average timing of alarms based on six out-of-sample tests, with the range of zero to one corresponding to eight weeks after to eight weeks before the event reaching the threshold 1.25\%. The expected performance is highly variable across data sources (Fig~\ref{single_performance_threshold125}), with ILINet and Google source data generally providing earlier warning than laboratory, EHR and Wikipedia data. The Google Trends time series for 'human temperature' provides the best balance of timeliness, precision and recall (Fig~\ref{systems_and_alarms_threshold125}(A)~and~\nameref{alarms_threshold125_supplement}), with an average advanced warning of 14 weeks prior to the CDC's 2\% threshold for the onset of the influenza season \cite{cdcflu}. National scale ILINet data triggers alarms an average of 11.7 weeks prior to the 2\% threshold (Fig~\ref{systems_and_alarms_threshold125}). Several data sources failed to detect any of the seasons, including Wikipedia page views relating to non-seasonal influenza viruses and athenahealth counts of positive influenza tests in HHS regions 8 and 9.

\begin{figure}[!ht]
  \centering
  \includegraphics[width=110mm]{single_performance_threshold125.pdf}
  \caption{{\bf Early detection by single data sources, summarized by category.}
  For each of the 240 candidate predictors, we fit a univariate detection model and measured performance by averaging early warning across six-fold cross validation (2010-2016). Emergence events for optimization are defined by an ILINet threshold of 1.25\%. The expected performance is highly variable, ranging from 0 to 0.77. A value of one means that the system consistently sounded alarms a full eight weeks prior to the event threshold 1.25\%; a value of 0.5 indicates that, on average, the alarms sound at the time reaching the threshold 1.25\%; lower values indicate delayed alarms.}
  \label{single_performance_threshold125}
\end{figure}

\subsection*{Early detection from multiple data sources}
We selected optimal combinations of predictors from within each class of data. For CDC ILINet, we considered 11 candidate predictors and found that the optimized system included three time series: ILINet HHS region 7 (Iowa, Kansas, Missouri and Nebraska), ILINet HHS region 5 (Illinois, Indiana, Ohio, Michigan, Minnesota and Wisconsin), and ILINet US (Fig~\ref{data_selection_curve_threshold125}). Across all replicates, HHS region 7 was selected as the most informative predictor, which alone outperforms the optimized system using multiple NREVSS data sources (Fig.~\ref{data_selection_curve_threshold125}). HHS region 9 and US were not selected in all replicates, and just marginally elevate the performance of HHS region 7. Comparing the optimized internet-source systems (Google Trends and Wikipedia) to optimized EHR (athenahealth) system, we find that the best combination of Google Trends time series---\emph{human temperature}, \emph{normal body temperature}, \emph{break a fever}, \emph{fever cough}, \emph{flu treatments}, \emph{thermoscan}, \emph{ear thermometer}---outperforms the others (Fig~\ref{data_selection_curve_threshold125} and \ref{systems_and_alarms_threshold125}(A)).

\begin{figure*}[ht]
  \centering
  \includegraphics[width=\columnwidth]{data_selection_curve_threshold125.pdf}
  \caption{{\bf Performance curves for early detection systems.} Systems were optimized within each data category (ILINet, NREVSS, Google Trends, Wikipedia, and athenahealth) and across all data categories, including and excluding Google Trends. Performance is the average advanced warning within the 16 week detection window surrounding the week when ILINet reaches the event threshold of 1.25\%. Performance equal to one indicates that a model consistently signals eight weeks ahead of the event threshold and zero indicating failure to signal within the detection window. Early detection improves as forward selection sequentially adds the most informative remaining data source until reaching a maximum performance. For the optimal system, the first six predictors are Google Trends sources and the remaining two are Wikipedia sources; for the optimal system excluding Google Trends, the top sources are from Wikiperdia, athenahealth, wikipedia and ILINet, in that order.
  }
  \label{data_selection_curve_threshold125}
\end{figure*}

Across the three-fold out-of-sample tests, the ILINet system detected all six influenza outbreaks with an average advanced warning of 12.7 weeks prior to the CDC's season onset threshold, while the Google Trends system detected 83.3\% of outbreaks (five out of six), with an average advanced warning of 16.4 weeks (excluding missing outbreaks) prior to the official threshold (Fig~\ref{systems_and_alarms_threshold125}(A)~and~\nameref{alarms_threshold125_supplement}). The other systems each detected four to six of six test seasons (not always the same seasons), with average advanced warning ranging from 9.5 to 14.2 weeks (Fig~\ref{systems_and_alarms_threshold125}(A)~and~\nameref{alarms_threshold125_supplement}). Individual ILINet time series generally provide earlier warning than individual EHR and Wikipedia time series. However, performance reverses for optimized multivariate models, with the best ILINet algorithm underperforming both the EHR and Wikipedia algorithms (Fig~\ref{systems_and_alarms_threshold125}(A)~and~\nameref{alarms_threshold125_supplement}).

To build multi-category early detection systems, we applied the optimization method to the 'winners' of the previous experiments. That is, we considered the 26 predictors shown on the first five plots of Fig~\ref{data_selection_curve_threshold125}. The best model includes eight predictors. The top six are all Google Trends:
\emph{human temperature}, \emph{normal body temperature}, \emph{break a fever}, \emph{fever cough}, \emph{flu treatments}, \emph{thermoscan}; the remaining two are Wikipedia: \emph{orthomyxoviridae} and \emph{shivering}, which only improve the performance of the system marginally (Fig~\ref{data_selection_curve_threshold125}). None of the ILINet, NREVSS, or EHR time series made the cut. The combined system achieves comparable early warning to the optimized Google Trends system while detecting higher proportion of events with lower number of false alarms (Fig~\ref{systems_and_alarms_threshold125}). Furthermore, it sounds alarms earlier than all three alternative models in four out of six seasons. In 2012-2013 all models provide similar early warning; in 2015-2016, the \textit{week-trigger} and \textit{rise-trigger} algorithms signal two and three weeks ahead of our optimized algorithm, respectively (Fig~\ref{systems_and_alarms_threshold125}(B)). The optimized algorithm also produces fewer false alarms than the rise-trigger algorithm and detects a higher proportion of influenza seasons than week-trigger algorithm. (Fig~\ref{systems_and_alarms_threshold125}(B)). The MEWMA model using only ILINet data typically lags all other models in signalling events.

When we exclude Google Trends candidates from optimization, the method selects Wikipedia pageviews of \textit{flu season} as the most informative predictor followed by a combination of EHR, Wikipedia and ILINet time series (Fig~\ref{data_selection_curve_threshold125}). Expected performance declines slightly without Google Trends data. In three-fold out-of-sample evaluation, the six influenza seasons are detected at an average of 14.8 weeks prior to the CDC's 2\% threshold without missing any events (Fig~\ref{systems_and_alarms_threshold125}).

\begin{figure*}[!ht]
  \centering
  \includegraphics[width=130mm]{systems_and_alarms_threshold125.pdf}
  \caption{{\bf Performance of optimized US influenza detection algorithms in three-fold cross validation (2010-2016).}
  (A) Distribution of system performance over six influenza outbreaks across 40 replicates, in terms of the timing of true alarms relative to the official onset of influenza seasons (excluding missed seasons), proportion of alarms indicating actual events (precision), and proportion of events detected (recall). (B) Timing of alarms relative to the official onset of each influenza season. Using US ILINet time series (blue curves) as a historical \textit{gold standard}, the detection models were trained to sound alarms as early as possible in the sixteen weeks surrounding the week when ILINet reaches 1.25\%.
  Bar plot (panel 1) shows the advanced warning provided by out-of-sample alarms in terms of weeks in advance of the CDC's 2\% ILINet threshold for declaring the onset the influenza season. Bars not shown indicate missed events. In the lower time series plots, dashed green lines indicate the CDC's seasonal influenza threshold of 2\%; numbers indicate the corresponding week of the year; short red lines indicate the timing of the alarms given by the optimized model.}
  \label{systems_and_alarms_threshold125}
\end{figure*}

\subsection*{Out-of-sample detection of the 2009 H1N1 pandemic and 2016-2017 influenza season}
We further validated our algorithms using held out ILINet data from two different epidemics. For the 2016-2017 influenza season, the optimized algorithm signaled the start of 2016-2017 season 14 weeks prior to ILINet reaching the CDC's 2\% threshold, which outperforms the univariate ILINet model. However, the week-trigger and rise-trigger algorithms beat the optimized algorithm by two weeks. For the atypical fall wave of transmission during the 2009 H1N1 pandemic, these two models failed to signal the emerging threat. It emerged much earlier in the year than seasonal influenza (thus tripping up the week-trigger algorithm) and at a higher epidemic growth rate (thus outpacing the rise-trigger algorithm) \cite{cdc09pandemic}. The optimal system was able to detect the the fall wave five weeks prior to ILINet reaching the 2\% threshold (Fig~\ref{final_detection_threshold125}). The univariate ILINet model again lags the best model by several weeks in out-of-sample test. This suggests that our optimized multivariate models are more robust for detecting anomalous influenza threats than the simpler alternatives.

\begin{figure*}[!ht]
  \centering
  \includegraphics[width=110mm]{final_detection_0910_threshold125_alter.pdf}
  \caption{{\bf Early detection of the 2009 H1N1 pandemic (out-of-sample).}
  The optimized model was trained on 2010-2016 ILINet data, and then tested on US ILINet reports (blue curve) during fall wave of the 2009 H1N1 pandemic. It triggered an alarm (triange) five weeks prior to ILINet reaching the official epidemic threshold of 2\% (dashed lines). Red markers indicate timing of alarms triggered by the optimized and baseline models.}
  \label{final_detection_threshold125}
\end{figure*}

\subsection*{Sensitivity to training period}
When we varied the length of the training period from four to twelve years, we selected overlapping sets of optimal predictors, with all five systems including ILINet data for HHS regions 6 and 7 (Table~\nameref{diff_time_window}). The systems detected similar proportions of events. However, the precision (the proportion of true alarms to all alarms) appears to increase with the length of the training period while, surprisingly, the alarms tend to sound later (Fig~\ref{ts_length_performance}). We also found system performance to be fairly insensitive to the gap between the training and testing periods (\nameref{ts_lagged_performance}), suggesting robust performance with only periodic system updates.

\begin{figure*}[!ht]
  \centering
  \includegraphics[width=60mm]{ts_length_performance_alter.pdf}
  \caption{{\bf Duration of training period impacts early detection.} Graphs compare the performance of five systems optimized using continuous training data ranging in length from four to twelve years (each ending in 2016), evaluated via cross-validation on 2012-2016 data. Alarm timeliness (top) unexpectedly declines as the training period increases (maximum likelihood linear regression, P=0.019), while the proportion of true alarms (middle) improves (maximum likelihood linear regression, P=0.000256). Training period does not significantly impact recall (not shown).
}
  \label{ts_length_performance}
\end{figure*}

\section*{Discussion}
This MEWMA-FFS framework is designed to build robust early outbreak detection systems that harness a variety of traditional and next generation data sources. For seasonal influenza in the US, we identified a combination of freely available internet-source data that robustly detects the start of the season an average of $16.4$ (SD $3.3$) weeks in advance of the national surveillance threshold (ILINet reaching $2\%$). This is five weeks earlier than previously published early detection algorithms based on ILINet and Google data \cite{Cowling2006flu, Pervaiz2012flu}. In a retrospective out-of-sample attempt to detect the fall wave of the 2009 H1N1 influenza pandemic, the optimized multivariate algorithm provided the earliest warning among the competing models. However, it sounded an alarm only five weeks prior to ILINet reaching the national $2\%$ threshold. The shorter lead time may stem from the anomalously rapid growth of the 2009 pandemic. Across the six influenza seasons between 2010 and 2017, ILINet took an average of 9.4 weeks to increase from $1.25\%$ to $2\%$, with a minimum of six weeks in seasons 2012-2013 and 2014-2015; in the fall of 2009, this transpired in a single week (week 34).

Public health surveillance data (e.g., ILINet and NREVSS) can detect emerging influenza seasons on their own, but a combination of eight Google query and Wikipedia pageview time series provided earlier warning across all eight epidemics tested. Although we cannot definitively explain the performance of internet data, we note that 59\% of flu-related Wikipedia English pageviews come from countries outside the US, including the United Kingdom, Canada, and India \cite{McIver2014wiki}. Perhaps earlier influenza seasons elsewhere provide advanced warning of imminent transmission in the US. The utility of Google and Wikipedia data may also stem from their large and diverse user bases and their immediate use following symptoms relative to seeking clinical care\cite{Ginsberg2009gft}. NRVESS is among the mostly costly and time lagged data sources; it performs poorest when considered individually and is never selected for inclusion in combined early detection systems. However, NRVESS provides critical spatiotemporal data for detecting and tracking novel viruses, including pandemic and antiviral resistant influenza, and informing annual vaccine strain selections. Thus, we speculate that NRVESS might rank among the most important sources when designing systems for virus-specific influenza nowcasting and forecasting objectives.

We emphasize that these algorithms are not designed to forecast epidemics, but rather to detect unexpected increases in disease-related activity that may signal an emerging outbreak~\cite{Fricker2013}. Early warning provides public health agencies valuable lead time for investigating and responding to a new threat.
For seasonal and pandemic influenza, such models can expedite targeted public health messaging, surge preparations, school closures, vaccine development, and antiviral campaigns. Influenza forecasting models  potentially provide more information about impending epidemics, including the week of onset, the duration of the season, the overall burden, and the timing and magnitude of the epidemic peak~\cite{shaman2013, brooks2015, ertme2018}. However, they are typically not optimized for early warning or for detecting outbreaks that are anomalous in either the timing or pace of expansion.

Our conclusions may not be readily applied to influenza detection outside the US or to other infectious diseases. However, the general framework could be similarly deployed to address such challenges. Even for seasonal influenza in the US, our results pertain to only early detection of seasonal influenza activity as estimated from ILINet, and stem from only six seasons of historical data. If we changed the optimization target (i.e., gold standard data) to an EHR or regional ILINet source, the resulting data systems and corresponding performances may differ considerably. Furthermore, as alternative data and longer time series become available, the optimal systems could potentially improve. Early detection systems should therefore be regularly reevaluated and tailored to the specific objectives and geopolitical jurisdictions of public health stakeholders, and our optimization framework can facilitate easy and comprehensive updates.

This approach requires domain-knowledge in the selection of candidate data sources. Next generation \emph{proxy} data should be relevant to the focal disease and population, such as symptom or drug related search data. Climate and environmental factors may prove predictive for directly transmitted and vector borne diseases, and may be a promising direction for enhancing the early detection systems developed here. This \emph{black box} approach can select data sources with spurious or misleading relationships to the gold standard data. Thus, it may be prudent to screen data sources before and after optimization that are unlikely to correlate reliably with the target of early detection.
We implemented this MEWMA-FFS framework as an user-friendly app in the Biosurveillance Ecosystem (BSVE) built by the US Defense Threat Reduction Agency (DTRA) \cite{bsve}. Military bioanalysts can now use it to evaluate and integrate diverse data sources into targeted early detection systems for a wide range of infectious diseases worldwide. The versatility of this \textit{plug-and-play} method stems from two assumptions: (1) it simply scans for deviations from underlying distributions rather than modeling a complex epidemiological process, and (2) it does not require seasonality, just historical precedents with which to train the model. We can now more easily harness the growing volumes of health-related data to improve the timeliness and accuracy of outbreak surveillance and thereby improve global health.

\section*{Supporting information}

\paragraph*{S1 Fig.}
\label{threshold_detectionWindow_performance_comparison}
{\bf Comparison of system performances with different pairs of event threshold $\varepsilon$ and detection window $T_w$ in three-fold cross validation (2010-2016).} Distribution of average system performance over six influenza seasons across 40 replicates, in terms of the timing of true alarms(excluding missed seasons), proportion of alarms indicating actual events (precision), and proportion of events detected (recall).
\begin{figure*}[!hp]
  \centering
  \includegraphics[width=120mm]{threshold_detectionWindow_performance_comparison.pdf}
\end{figure*}

\paragraph*{S2 Fig.}
\label{alarms_threshold125_supplement}
{\bf Out-of-sample detection of US influenza seasons by single source and single category early warning systems.} Using US ILINet time series (blue curves) as a historical gold standard, the detection models were optimized to sound alarms as early as possible in the sixteen weeks surrounding the threshold 1.25\% for optimization. The bar plot (panel 1) shows the alarm timing for each influenza season from 2010-2016 relative to the official ILINet threshold of 2\%. Bars not shown indicate missed events in early detection, while positive values show alarms are triggered prior to the official start of each influenza season. In panel 2, horizontal green dashed lines represent the threshold of 2\%, while vertical green dashed lines indicate the onset of influenza seasons according to the threshold of 2\%; numbers indicate the corresponding week of the year; red short lines show alarm timings for flu seasons from the optimized model.
\begin{figure*}[!ht]
  \centering
  \includegraphics[width=120mm]{alarms_threshold125_supplement.pdf}
\end{figure*}

\paragraph*{S3 Fig.}
\label{ATFS_performance_tradeoff}
{\bf The trade-off between timeliness, and precision, recall, running time.} Each system was optimized using different values of ATFS. The three plots show the trade-off between alarms timings and the proportion of alarms indicating actual events (precision), proportion of events detected (recall), and running time of each optimization with 40 repeats running in parallel, respectively.
Each run selected different combinations of predictors (~\nameref{ATFS_tradeoff_table}) and detected influenza emergence an average of 11-14 weeks prior to the official onset of influenza seasons. There is a weak trade-off between timeliness and precision and minimal trade-off between timeliness and recall. The precision is always below 0.9 while recall is equal to one for most of values of ATFS. This is because we consider the timing of only the first alarm in a cluster; the ATFS is expected to impact the total number of alarms but not neccessarily the number of alarm clusters~\cite{Fricker2013}. Meanwhile, a larger value of ATFS requires longer running time for optimization. An optimization experiment with ATFS set to 50 (the value that maximizes timeliness and preceision) requires twice the run time of an experiment using ATFS 20; however, the gain is only one additional week of early warning. Thus, it is valuable to balance performance and compute time when setting ATFS for optimization.

\begin{figure*}[!ht]
  \centering
  \includegraphics[width=\columnwidth]{ATFS_performance_tradeoff.pdf}
\end{figure*}

\paragraph*{S4 Fig.}
\label{ts_gap_dataVis}
{\bf Diagram of training and testing periods used in sensitivity analysis.}
\begin{figure*}[!hp]
  \centering
  \includegraphics[width=90mm]{ts_gap_dataVis.pdf}
\end{figure*}

\clearpage

\paragraph*{S5 Fig.}
\label{ts_lagged_performance}
{\bf Sensitivity to the training period.} Each of five systems was optimized using training and testing periods diagrammed in \nameref{ts_gap_dataVis}. The three graphs show performance in terms alarm timing (top), proportion of alarms that correspond to actual events (middle), and proportion of events detected (bottom). Gap between testing and training periods does not appear to significantly impact performance.
\begin{figure*}[!ht]
  \centering
  \includegraphics[width=60mm]{ts_lagged_performance.pdf}
\end{figure*}

\paragraph*{S1 Table.}
\label{ATFS_tradeoff_table}
{\bf Time series selected for early detection systems across different values of ATFS.} Time series are listed in order of selection, assuming an ILINet threshold of 1.25\% for optimization.
\begin{table}[!hp]
\begin{adjustwidth}{-0.75in}{0in}
\centering
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|}
\hline
\multicolumn{10}{|c|}{\bf Value of ATFS}\\ \hline
5 & 10 & 20 & 30 & 40 & 50 & 80 & 100 & 120 & 150\\ \hline
HHS 7 & HHS 7 & HHS 7 & HHS 7 & HHS 7 & US & HHS 7 & US & US & US\\
 & HHS 6 & HHS 5 & US & US & HHS 4 & HHS 6 & HHS 4 & HHS 9 & HHS 4\\
 & HHS 2 & US & HHS 6 & HHS 10 & HHS 10 & HHS 4 & HHS 6 & HHS 6 & HHS 6\\
 & & & & HHS 4 & HHS 6 & US & HHS 7 & HHS 5 & HHS 7\\
 & & & & HHS 6 & & & & HHS 1 & \\
 & & & & HHS 8 & & & & & \\ \hline
\end{tabular}
\end{adjustwidth}
\end{table}

\clearpage

\paragraph*{S2 Table.}
\label{diff_time_window}
{\bf Data sources selected for early detection systems across variable length training periods.} Time series are listed in order of selection, assuming an ILINet event threshold of 1.25\%
\begin{table}[!hp]
\centering
\begin{tabular}{|l|l|l|l|l|}
\hline
\multicolumn{5}{|c|}{\bf Model Training Period} \\ \hline
2004-2016 & 2006-2016 & 2008-2016 & 2010-2016 & 2012-2016\\ \hline
HHS 5 & US & HHS 7 & HHS 7 & HHS 3\\
HHS 7 & HHS 6 & HHS 1 & HHS 9 & HHS 7\\
HHS 9 & HHS 7 & HHS 6 & HHS 6 & HHS 10\\
HHS 6 & HHS 9 & & & HHS 2\\
HHS 8 & HHS 8 & & & HHS 6\\
 & HHS 2 & & & US\\ \hline
\end{tabular}
\end{table}

\clearpage

\paragraph*{S3 Table.}
\label{GT_search_terms}
{\bf Candidate Google Trends data sources for early detection of seasonal influenza.} Optimization experiments evaluated 100 time series based on each of these search terms.
\begin{table}[!hp]
\begin{adjustwidth}{-1.5in}{0in}
\centering
\begin{tabular}{|l|l|l|l|}
\hline
\multicolumn{4}{|c|}{\bf Google Search Terms} \\[3pt] \hline
influenza type a & \shortstack[l]{how long is the\\flu contagious} & signs of flu & pneumonia \\[3pt]
exposed to flu & low body & early flu symptoms & flu report \\[3pt]
symptoms of flu & get over the flu & how long does flu last & flu headache \\[3pt]
flu duration & treating flu & normal body temperature & flu cough\\[3pt]
flu contagious & flu vs. cold & get rid of the flu & flu last \\[3pt]
\shortstack[l]{incubation period\\for flu} & flu coughing & break a fever & flu contagious period \\[3pt]
flu fever & having the flu & type a influenza & ear thermometer \\[3pt]
treat the flu & treatment for flu & i have the flu & \shortstack[l]{how to get rid\\of the flu} \\[3pt]
how to treat the flu & human temperature & after the flu & flu how long \\[3pt]
signs of the flu & dangerous fever & when you have the flu & symptoms of bronchitis \\[3pt]
\shortstack[l]{influenza incubation\\period} & cold versus flu & flu in children & \shortstack[l]{what to do if\\you have the flu}\\[3pt]
over the counter flu & the flu & taking temperature & cold and flu \\[3pt]
how long is the flu & remedies for flu & if you have the flu & \shortstack[l]{over the counter\\flu medicine} \\[3pt]
symptoms of the flu & contagious flu & how long flu & flu type \\[3pt]
flu recovery & \shortstack[l]{how long does\\the flu last} & flu germs & treating the flu \\[3pt]
flu and fever & flu lasts & \shortstack[l]{incubation period\\for the flu} & do i have the flu \\[3pt]
flu medicine & have the flu & cold vs. flu & flu care \\[3pt]
flu or cold & oscillococcinum & flu and cold & how long contagious \\[3pt]
is flu contagious & \shortstack[l]{how long is\\flu contagious} & thermoscan & fight the flu \\[3pt]
how long does the flu & flu treatments & flu complications & reduce a fever \\[3pt]
cold symptoms & how to reduce a fever & upper respiratory & fever dangerous \\[3pt]
treat flu & influenza symptoms & high fever & cure the flu \\[3pt]
is the flu contagious & cold vs flu & flu children & medicine for flu \\[3pt]
flu treatment & braun thermoscan & the flu virus & flu length \\[3pt]
flu vs cold & fever cough & how to treat flu &  cure flu \\[3pt]
\hline
\end{tabular}
\end{adjustwidth}
\end{table}

\clearpage

\paragraph*{S4 Table.}
\label{wiki_articles}
{\bf Candidate Wikipedia data sources for early detection of seasonal influenza.} Optimization experiments evaluated 53 time series based on access frequency for each of these Wikipedia articles.
\begin{table}[!hp]
\begin{adjustwidth}{-1in}{0in}
\centering
\begin{tabular}{|l|l|l|l|}
\hline
\multicolumn{4}{|c|}{\bf Wikipedia Articles} \\[3pt] \hline
Antiviral drugs & Gastroenteritis & \shortstack[l]{Influenza A virus\\subtype H5N1} & Influenza-like illness \\[3pt]
Avian influenza &  Headache & \shortstack[l]{Influenza A virus\\subtype H7N2} & Influenzavirus A \\[3pt]
Canine influenza & \shortstack[l]{Hemagglutinin\\(influenza)} & \shortstack[l]{Influenza A virus\\subtype H7N3} & Influenzavirus C \\[3pt]
Cat flu & Human flu & \shortstack[l]{Influenza A virus\\subtype H7N7} & Malaise \\[3pt]
Common cold & Influenza A virus & \shortstack[l]{Influenza A virus\\subtype H9N2} & Nasal congestion \\[3pt]
Chills & Influenza & \shortstack[l]{Influenza A virus\\subtype H7N9} & Myalgia \\[3pt]
Cough & \shortstack[l]{Influenza A virus\\subtype H1N1} & \shortstack[l]{Influenza A virus\\subtype H10N7} & Nausea \\[3pt]
Equine influenza & \shortstack[l]{Influenza A virus\\subtype H1N2} & Influenza B virus & Neuraminidase inhibitor \\[3pt]
Fatigue (medical) & \shortstack[l]{Influenza A virus\\subtype H2N2} & Influenza pandemic & Orthomyxoviridae \\[3pt]
Fever & \shortstack[l]{Influenza A virus\\subtype H3N8} & Influenza prevention & Oseltamivir\\[3pt]
Flu season & \shortstack[l]{Influenza A virus\\subtype H3N2} & Influenza vaccine & Paracetamol\\[3pt]
Rhinorrhea & Rimantadine & Shivering  & Sore throat \\[3pt]
Swine influenza & Viral neuraminidase & Viral pneumonia & Vomiting \\[3pt]
Zanamivir & & &\\[3pt]
\hline
\end{tabular}
\end{adjustwidth}
\end{table}

\section*{Acknowledgments}
We thank athenahealth, Inc. for providing Electronic Health Records data. Funding was provided by US Department of Defense the Defense Threat Reduction Agency contract HDTRA-14-C-0114, and US National Institute of General Medical Sciences Models of Infectious Disease Agent Study Grant U01GM087719.

\nolinenumbers

\clearpage

redpajama_set_name: RedPajamaArXiv


text:
Here's the lineup for this spring's Hingham town election
James E. Kukstis
Wicked Local
HINGHAM – Voters will decide on who should fill available seats on the school committee, the board of health, the sewer commission and the board of assessors at this spring's town election, in which only six of 12 open seats are contested by more than one candidate.
Four candidates will vie for three open seats on the Hingham School Committee. Committee member Nes Correnti will seek reelection, and Alyson Anderson, Matthew Cosman and Matthew LeBretton will also run.
School committee members Liza O'Reilly and Carlos Da Silva are not seeking reelection.
Kirk Shilts is running for reelection to both the board of health and the sewer commission. Rosemary Byrne is challenging him for his seat on the board of health and Joseph P. Kelly is challenging for a spot on the sewer commission.
Carlton Chambers is running for reelection to a one-year term on the board of assessors. He is challenged by Randall Winters.
More:Meet Opry, the puppy on duty with the Hingham Police Department
Select board member Joseph Fisher will run unopposed for reelection, as will Town Moderator Michael Puzo, planning board member Gordon Carr, assessor Chrissy Roberts and municipal light board member Michael Reive. Stephen Buckley will run unopposed for a seat on the recreation commission and Michelle Larned is unopposed for a spot on the housing authority.
The town election is scheduled for April 30, but town officials have applied to the state for permission to push the election to a date between April 30 and June 30.

redpajama_set_name: RedPajamaCommonCrawl


In What Point of View and Tense Is the Story Great Told?
The Fitzgeralds then moved to Rome for the winter. He also reminds himself of his fathers words of wisdom. Even reading this in high school I came away thinking that she was hot. Writers like John O'Hara were showing its influence and younger men like Edward Newhouse and Budd Schulberg, who would presently be deeply affected by it, were discovering it. Understanding the Great Gatsby: A Student Casebook to Issues, Sources, and Historical Documents. His dream Daisy's reciprocal love, which he will never receive anym … ore, is his motivation never to give up during his penny less moments in the war. She conducts a secret life with Tom, wherein she exhibits all the power and dominance she finds lacking in her everyday life.
Nick's uniqueness is probably best illustrated by Gatsby's funeral. Nick Carraway, the story's narrator, has a singular place within The Great Gatsby. Although the nineteen twenties are a time of economic prosperity, they appear to be a time of corruption and crime as well. Nick romanticizes Gatsby in the exact same way that Gatsby romanticizes Daisy. He is an educated man who desires more out of life than the quiet Midwest can deliver although it is interesting that before living in the city any length of time he retreats to the country.
Jordan Baker Professional golfer of questionable integrity. His life is parallel to that of Jay Gatsby in the way that both met someone… Words 4815 - Pages 20 Nick Carraway Nick Carraway is the narrator of the story, the protagonist of his own plot, and the moral judge of the events that surround him. Fitzgerald's neighbors in Great Neck included such prominent and newly wealthy New Yorkers as writer , actor , and comedian. Scott Fitzgerald, the narrator, Nick Carraway, tells a story in which Jay Gatsby tries to attain happiness through wealth. The New York Review of Books.
Without Nick's narration we would assume Gatsby not to be Great but the way in which Nick interprets Gatsby allows to think that he could be. Gatsby's past is, quite literally, an enigma wrapped in a paradox and the reader is only given a few clues as to what events have occured in Gatsby's past which have led him to the events in the novel. His aim to be truthful and objective makes the reader trust him. One possibility is Land's End, a notable where Fitzgerald may have attended a party. For several years afterward, the general public believed The Great Gatsby to be nothing more than a nostalgic period piece. I had two others, Gold-hatted Gatsby and The High-bouncing Lover, but they seemed too slight.
Through his observation of people at Gatsby's party, at the beginning of chapter three, Nick seems to feel that the wealthy are clones of a stereotype accepted and created by themselves. The Slate Group, a Division of the Washington Post Company. This would be the end of chapter two, before he meets, and falls instantly in love with, Gatsby. The honesty that Nick attributes to himself must be a nearly perfect one, by dint of both its rarity… 1651 Words 7 Pages The narrative point of view adopted by F. The setting is during the Jazz Age as well, where popularity, fashion, and commerce are a primary inclination.
The story takes place in 1922, during the Roaring Twenties, a time of prosperity in the United States after World War I. First published by in April 1925, The Great Gatsby received mixed reviews and sold poorly; in its first year, the book sold only 20,000 copies. Moi qui pensais lire des news récentes. Associated Press articles: Copyright © 2016 The Associated Press. A little-known artist named was commissioned to illustrate the book while Fitzgerald was in the midst of writing it.
Nick's own process of initiation Even if Gatsby is the novel's main protagonist, the novel bears witness to the process of initiation undergone by Nick. At times, he is able to narrate scenes despite not being present — but he rarely takes advantage of this fact. During the events of the novel, Gatsby lives in West Egg, an area across the bay from an area associated with New Money and gaudiness. Various characters mention Gatsby in passing to Nick before Nick ever meets him. He begins by commenting on himself, stating that he learned from his father to reserve judgment about other people, because if he holds them up to his own moral standards, he will misunderstand them.
Myrtle's husband, George Wilson, falsely concludes that the driver of the yellow car is the secret lover he suspects his wife had. As usual with this device, the main protagonist remains strange and shady. In truth, I was so pleased with myself for developing my theory that the notion had not occurred to me. He had begun to plan the novel in June, 1923, saying to Maxwell Perkins, 'I want to write something new — something extraordinary and beautiful and simple and intricately patterned. Given this background, it is interesting that Nick would come to be regarded as a level-headed and caring man, enough of a dreamer to set goals, but practical enough to know when to abandon his dreams.
Though Tom is himself an adulterer, he is outraged by his wife's infidelity. Like Ginevra's father, whom Fitzgerald resented, Buchanan attended and is a. Nick is gradually pulled into the lives of the rich socialites of the East and West Egg. At the last minute, he had asked his editor if they could change the new novel's title to Under the Red, White and Blue, but it was too late. After an initially awkward reunion, Gatsby and Daisy begin an affair over the summer. Already at the beginning of the story Nick admits that he does not judge Gatsby, because he admires him greatly, making him more loyal to him than to any other character. Nick's encounter with Gatsby is a decisive step in the narrator's progress towards adulthood.
They develop characters more thoroughly, comprehensively, and give. Tom tries to interest the others in a book called The Rise of the Colored Empires by a man named Goddard. She holds a Master of Arts in English from the University of Louisiana at Lafayette and currently works in higher education as a writing consultant. Nick's subjective account Nick is unreliable: he has a romantic turn of mind pushing him to idealize certain characters. Archived from on 13 October 2013. Archived from on 13 October 2013. Through Jordan, Nick later learns that Gatsby knew Daisy through a purely chance meeting in 1917 when Daisy and her friends were doing volunteer service work with young officers headed to Europe.


In the mornings when I awake, it's my goal to set the atmosphere for myself and my family for the day. Indeed we say our own individual prayers that we offer up to the Lord but it's something about being a woman, wife, mother and child of the Most High that wants my spirit to be aligned with His. To have such intimacy, comfort, teaching and guidance sets my heart ablaze that my inner being rejoices from the inside out!
I can't help but think about the goodness of the Lord. Jesus came to give us life so that we could have it more abundantly. (John 10:10) When He left this earth, He left us with the promise of the Holy Spirit.
I was reading my Bible earlier in the Book of Acts and I began to think about how much the Holy Spirit does in my life. He guides, convict, teaches, comfort, and leads me in all areas of my life. He was given to me when I accepted Jesus Christ as my Lord and Savior long ago. He was a part of my decision-making to marry the love of my life. Where would I be? How could I make it? I don't know but Jesus does. He knew exactly what He was doing before leaving behind His disciples. Being a witness to the profound blessings and growth in Christ Jesus has done so much for me in my daily walk and that of others I cross paths with. In person and here on the blog. I am touching lives. I am speaking LIFE and ENCOURAGING in the mighty name of Jesus! Hallelujah!!! When I write, I am led by the Holy Spirit. When a share a word, it is the Holy Spirit who leads me and gives me the words to say. I count myself to be nothing. I credit myself nothing that I would be glorified in it. It all goes to the Lord above. To Him I give my all and glorify His name on high! My soul is blessed when a scripture or song is brought to memory or a new one comes to mind. Now that just blows me away!
I love some praise and worship music as most of you may have to come notice on various posts. It lifts me and stirs up my belly to spring forth an abundance of praise, prayer and worship at times out of my mouth with a joyous dance in my feet! Words simply cannot express my gratitude and love. I'd love to share a song that was placed on my heart this morning that keeps playing in my head.
I still rejoice over and over again!
Dear reader, I lift you up to the Lord today. Whatever it may be in your life, Jesus can fix it. We have the help and comfort from the Holy Spirit. He will lead us into peace, a place of solitude to go into our prayer closet and share our hearts to the fullest even if our lips cannot utter a single word. He understands and speaks on our behalf to the Father. Be blessed and encouraged! If you do not have a personal relationship with Christ, I encourage you to pull away and invite Him into your life. Today. He's awesome and you too will receive the power of the Holy Spirit in your life.


Samsung is a specialist when it comes to decline its range with the high-end Galaxy S and many variants to reach all consumers strata. Sometimes there are disappointments, but here with the Galaxy Core Prime, the manufacturer offers a homogeneous unit, well thought out and balanced on the material plane. It will appeal to selfies lovers with its front sensor 5 megapixel still quite rare.
Similarly, the smartphone has a SD slot for expanding the internal capacity here 8GB. Its 5-inch screen is also HD with a beautiful definition. For a 5-inch, the phone also has a rather nice size with a weight of only 133 grams. The pleasant surprise is confirmed especially since its price is often much below the 200 Euros in the usual retailers. A consultant.


your stock wheel weight is that the stock 16in or the 17in?
Good point, it is for the US stock wheels - so 17"
I don't even wanna know!!
The stock wheels aren't actually that light. Those AVS model 5s are like 17 pounds each or something. And the volks are quite a bit lighter than stock too.
Well, in 17x9 mine weight in at 19.8lbs... So at 18x10 and 18x12 *ahem* oh dear.
Front with new tyre was 21Kg each. Rears with bald tyres are 22.2Kg each - without centre caps.
So I guess the bottom line is if you move from 17" wheels to 18"
they are pretty much going to weigh more...???
So its looks -v- pace..!!! ?
My cars gonna be a proper fatty.
Yep - it may get closer to a 'UK fatty' - but think of the improvement in QUALITY!
P.S. Soonto now runs for nuclear bunker type cover!


Home Regional 5 Attributes That Prove Rakul Is The Rockstar For This Generation!!
A girl just learnt that movies was the well she wanted to jump into, picked up modelling career early at the age of eighteen. Back in 2009, in order to earn some pocket money and being unaware of the impact of South Indian cinema, Rakul Preet Singh made her debut down south with the Kannada movie Gilli. A remake of 7/G Brindavan Colony.
She then took a break of two years to complete her graduation and went ahead to compete in the Miss India beauty pageant contest. Ever since she emerged winning four titles in 2011, Rakul only turned to be more successful and a go-getter.
Just to begin with, after her fist film, she went to compete with in the Miss India Beauty pageant contest and won four titles which include Miss Fresh Face, Miss Talented, Miss Beautiful Smile, Miss Beautiful Eyes.
In an interview when asked what she would do when she takes off from shoot, she said that she wouldn't be very active and would never want a break from shoots. She only wants to work tirelessly as movies was always she aspired to be in. Her steady march toward the top league of heroines where she has already worked with the top stars (Telugu to start with) in the beginning of her career only shows how committed she is to her goals. Interesting personality isn't it?
For many who don't know much about Rakul, she is a national level golf player and also an excellent student in mathematics. She is one of the heroines who has set example to many of her contemporaries by dubbing for herself in a language she hardly learnt since 2-3 years. Now, that comes only if you are truly talented.
She is not the sorts who has come to the industry that offers big bucks and take her share and spend the money recklessly. She is a beauty with brains. She is now an entrepreneur who has opened a gym of her own, f45 at Gachibowli. Definitely a smart move!
She is good friends with many of the happening celebs in the industry and has gotten well with many of them. She has even extended her hand to a social cause program that airs on one of the Television channels.
Aren't these qualities enough to show that she is the current show stopper and has already grabbed many eye balls towards her.
Host Screws up Miss Universe 2015 finals – wrong contestant crowned!


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Firenze Capitale (en español: Florencia Capital), designa el periodo, de 1865 a 1871, en el que la ciudad toscana fue capital de la Italia unificada, sucediendo a la primera capital, Turín, y antecediendo a la definitiva y actual, Roma.

Convención de Septiembre
El traslado de la capital, tiene su origen en la llamada Convención de septiembre. Dicho acuerdo, en principio secreto, fue firmado el 15 de septiembre de 1864 entre el Reino de Italia y el Segundo Imperio Francés en Fontainebleau. Sus firmantes fueron, Constantino Nigra, embajador italiano en París, el marqués de Pepoli, embajador italiano en San Petersburgo y Édouard Drouyn de Lhuys, ministro de exteriores francés.

El acuerdo buscaba solucionar la problemática de la capital italiana o la llamada "cuestión romana". Cuando el reino unificado de Italia se había proclamado en 1861, la capital proclamada había sido Roma. Sin embargo, al ya ser ésta, capital de los Estados Pontificios (el actual Lazio), la capital "provisional" se estableció en Turín, ciudad que ya era la sede de la Casa de Saboya y de las instituciones del Reino de Cerdeña, estado que había liderado la unificación.

La convención estipulaba:
 que Francia retiraría, en dos años, sus tropas de Roma, que protegían el poder del Papa.
 que Italia no atacaría ni Roma ni los Estados Pontificios, garantizando su integridad si fuera necesario hasta con la fuerza.
 Italia no renunciaba formalmente a Roma pero, a partir de la retirada de las tropas francesas, intentaría llegar a un acuerdo con el Papado.
Para garantizar dicho cumplimento por parte de Italia, el emperador Napoléon III añadió un protocolo secreto al acuerdo: la capital italiana sería trasladada a otra ciudad, en preferencia Florencia, Milán, Bolonia, Nápoles o Palermo.

Por su parte, los italianos consideraban que el acuerdo era cuestión de realpolitik y esperaban que este fuera transitorio, hasta que el cambio de la postura de Pío IX o de Francia permitiera integrar Roma, y los Estados Pontificios, en la Italia unificada.

Milán fue descartada por estar demasiado cerca de la frontera austríaca, Bolonia por su anterior vinculación con el Papado, Nápoles por ser fácilmente atacable por mar y Palermo por estar "aislada" en una isla, finalmente, Florencia fue escogida como futura capital.

Preparativos

Leyes y disturbios

La filtración de los términos de la Convención y la decisión del traslado de la capital causó un profundo malestar en la sociedad turinesa. El rey Vittorio Emanuele II se sintió disgustado al saber que tendría que abandonar la ciudad que había sido la sede de la dinastía desde 1563, la aristocracia temió perder su influencia y proximidad a la Corte, los funcionarios del gobierno consideraron que el coste sería excesivo, la burguesía vio amenazadas las inversiones que había hecho en la ciudad desde 1861 y al pueblo llano le disgustó saber que después de tantos sacrificios la ciudad perdería su estatus de capital.

Ante el disgusto del traslado, se convocaron varias manifestaciones en el centro de Turín los días 21 y 22 de septiembre. Las concentraciones fueron reprimidas de forma brutal por el Regio Esercito en la Piazza Castello y la Piazza San Carlo, respectivamente. El resultado de la llamada Strage di Torino (Matanza de Turín) fueron 55 muertos y al menos 133 heridos. Como consecuencia de los hechos, el Rey solicitó la dimisión del Presidente del Consejo, Marco Minghetti, y todo su gobierno.

A la cabeza del nuevo gobierno fue nombrado el laureado general Alfonso La Marmora, piamontés de origen y contrario al traslado de la capital. Sin embargo, la Convención ya había sido aprobada por el Rey y su suspensión habría significado la pérdida del apoyo francés, el más poderoso aliado del joven Reino de Italia. A pesar de las reticencias personales, La Marmora consiguió que el Senado y la Cámara de Diputados aprobaran la ley n.2032. La nueva norma establecía que la capital se trasladaría a Florencia en un plazo de seis meses y que se destinaría una partida extraordinaria de 7 millones de liras de los presupuestos del estado a tal efecto. El 11 de diciembre, Vittorio Emanuele II promulgó la ley.

La partida del Rey

Los disturbios se reanudaron en Turín en enero de 1865, a partir del día 23, cuando la Cámara de Diputados votó que "en interés de la nación" se daba por concluida la investigación de la matanzas del 21 y 22 de setiembre sin que nadie fuera considerado culpable. Los días 25, 26, 27 y 28 hubo manifestaciones en la ciudad, sin embargo, la culminación llegó el lunes 30 de enero durante el baile de inicio de temporada celebrado en el Palacio Real. Los manifestantes bloquearon la entrada de los invitados al palacio, insultándolos y lanzando pescado y piedras a los carruajes, mientras portaban el eslogan "Reggia da vendere – padrone da appendere" (Casa a la venta - dueño a la horca). Muchos invitados rehusaron intentar acceder al palacio, cuyo salones permanecieron medio vacíos durante el baile, además, el propio monarca pudo observar el suceso desde las ventanas de sus aposentos.

Sintiéndose profundamente ofendido, el día siguiente, Vittorio Emanuele II se trasladó al Castillo de Moncalieri, su residencia favorita en las afueras de la ciudad. Allí esperó, durante dos días, una disculpa pública por parte del ayuntamiento, disculpa que nunca llegó.

El viernes 3 de febrero, sin previo aviso, el soberano y su corte partieron en tren hacia Florencia. La Gazzetta Ufficiale publicó un escueto comunicado:"Esta mañana a las 8, Su Majestad el Rey a partido de Turín hacia Florencia, acompañado de Su Excelencia el Presidente del Consejo de Ministros, el general La Marmora."Pasando por Piacenza y Bolonia, donde fue aclamado por la multitud, el tren real llegó a Florencia a las 10 y media de la noche. La pequeña estación florentina había sido apresuradamente decorada e iluminada, entre vítores, el carruaje del Rey llegó hasta el Palazzo Pitti a través de calles angostas pero iluminadas y rodeado de lo mejor de la aristocracia y burguesía de la ciudad portando antorchas. Hacia la medianoche, el soberano tuvo que salir en varias ocasiones al balcón a saludar a la multitud.

Después de 302 años, Turín había dejado de ser, entre disturbios y broncas, la sede de la dinastía de los Saboya.  Según el Conde Giuseppe Pasolini: "ésta es la consecuencia de haber dejado que cada uno hiciera lo que quisiera durante cinco o seis días".

La nueva capital

Instalación en Florencia

Apenas llegado a la ciudad toscana, Vittorio Emanuele II, su familia y su corte se instalaron en el Palazzo Pitti. La antigua residencia de los Médici y los Lorena que ya había sido atribuida al soberano después de que Florencia se integrara al reino de Italia en 1860. Sin embargo, el Rey rehusó alojarse en los aposentos del piano nobile y en su lugar escogió el pabellón anexo conocido como la Palazzina della Meridiana que resultaba más íntimo y adecuado para su estilo de vida "burgués". Además, la Meridiana, con su acceso directo al jardín, permitía entradas y salidas discretas. Cruzando la Porta Annalena, el Rey podía fácilmente desplazarse a las villas de La Petraia, donde se alojaba su amante, la Condesa de Mirafiori, y de Poggio a Caiano, que servía de pabellón de caza. Asimismo, el soberano también disponía del amplio coto de caza de San Rossore, situado en el litoral toscano.

Más complicado fue instalar las distintas instituciones gubernamentales. En setiembre de 1864, un nuevo prefecto, el Conde Girolamo Cantelli había sido enviado a la ciudad para sondear la situación y en octubre, una comisión parlamentaria había sido establecida a tal efecto. El traslado empezó en diciembre del mismo año y no terminó hasta junio de siguiente.

La Cámara de Diputados tuvo su sede en el famoso Salone dei Cinquecento en el Palazzo Vecchio, donde un nuevo hemiciclo fue construido. Otras partes del palacio fueron ocupadas por el Ministerio de Exteriores. El Senado se instaló en el vecino Teatro Mediceo de los Uffizi, el Ministerio de la Guerra (y el Presidente del Consejo La Marmora) en un palacete de la Via Gino Capponi, el Ministerio del Interior en el famoso Palazzo Médici-Riccardi, el Ministerio de la Marina en el Convento de los Padres Misioneros, la Corte de Casación en el Convento de Santa Maria Novella...

Los florentinos, acostumbrados a que desde tiempos de los Médici, la ciudad y la Toscana entera hubieran sido gobernadas desde el Palazzo Vecchio, pronto quedaron sorprendidos de la llegada de centenares de funcionarios de todas partes del país y de la literal ocupación de tantos edificios a lo largo y ancho de la ciudad.

Mientras corte y gobierno se instalaban, la distensión con Turín no tardó en llegar. Pocos días después de la inesperada partida del soberano, una delegación del ayuntamiento turinés, con el alcalde a la cabeza, se desplazó a Florencia ante la presión de varios ciudadanos notables. La delegación y las disculpas fueron calurosamente recibidas por el Rey en San Rossore. El 23 de febrero, Vittorio Emanuele II se desplazó a Turín e hizo una aparición por sorpresa en su palco del Teatro Regio, dónde fue aclamado.

Vida política

Con las elecciones a ambas cámaras del Parlamento Italiano, el 22 de octubre, y la apertura formal de la IX legislatura por parte del Rey el 18 de noviembre, dio inicio la vida política en Florencia. Durante cinco años, la pequeña ciudad en rápida transformación, fue escenario de importantes eventos históricos, como la alianza con Prusia y la declaración de la guerra a Austria en junio de 1866. Pese a la poca fortuna del conflicto, también en Florencia se celebró la anexión del Véneto en octubre del mismo año.

En lo que respecta a la política interior, caben destacar como fundamentales las leyes sobre la separación de la Iglesia y el estado y de la desamortización eclesiástica bajo los gobiernos de Ricasoli y Rattazzi, todo ello por si " en cumplimiento de su destino, Italia tenía que hacer frente a nuevas pruebas". Ello no contribuyó, evidentemente, a mejorar las relaciones con el Papado de Roma. Otras tareas que se propuso llevar a cabo el gobierno, en este caso en el X legislatura, fue la cohesión del país a través de la reorganización administrativa y local, la simplificación del sistema impositivo y el control del gasto público.

La otra gran cuestión a tratar, fue la resolución de la "cuestión romana", hecho que precisamente había motivado el traslado de la capital de Turín a Florencia. Cuando los zuavos de Napoléon III abandonaron Roma en 1867, Garibaldi organizó una expedición de voluntarios para conquistar la ciudad. El proyecto, denunciado pero tampoco impedido por el gobierno italiano, que veía peligrar su alianza con Francia, fue, sin embargo, secretamente apoyado por Vittorio Emanuele II. La Intentona de Mentana acabó con un sonoro fracaso de los garibaldinos, pero obligó al emperador francés a mantener, a perpetuidad, a la fuerza expedicionaria en Roma.

Vida cultural

No fue casualidad que, apenas unos meses después de la partida del Rey, se inaugurara, el 14 de mayo, una estatua dedicada a Dante en la Piazza de la Santa Croce. Con la celebración de los quinientos años del nacimiento de tan emblemático poeta, Florencia se erigía como centro, no solo político, sino también cultural de la Italia unificada. El cincuentenario de Dante, también contó con una exposición retrospectiva en el Palazzo del Podestà con diversos manuscritos traídos desde Milán.

Con la llegada de centenares de funcionarios desde todas las partes de Italia, la pequeña Florencia se "desprovincializó". Las antiguas botteghe (talleres de los artesanos) se transformaron en elegantes negocios para suplir la demanda de bienes de lujo como instrumentos musicales, porcelanas, sombreros o guantes. Por otro lado, los tradicionales bottegone (tabernas) ampliaron sus horarios hasta el atardecer, equiparándose a los estándares de otras capitales europeas.

Los teatros también se multiplicaron, llegándose a contabilizar once teatros fijos, más otros cuatro que abrían por temporada. La Pergola siguió siendo el preferido de las clases altas y el cuerpo diplomático, mientras que las clases populares prefirieron La Querconia. El mismo camino siguieron los periódicos, en 1870, de los 723 periódicos publicados en Italia, 101 se publicaban en Florencia o en la provincia.

Asimismo, florecieron los salones privados, donde se hablaba de política y cultura más que en los pasillos del Palazzo Vecchio. Los literatos y los músicos se reunían cada domingo en la palacio del príncipe polaco Carlo Poniatowski. Los lunes, la nobleza piamontesa y toscana se citaba en el Palazzo Corsini del barrio del Prato. El salón del senador Cesare Alfieri era el favorito de los eruditos, el de Madame Ratazzi (esposa de Urbano Rattazzi) en el Palazzo Guadagni era conocido por sus funciones teatrales privadas y el de Madame Peruzzi (esposa del exministro Ubaldino Peruzzi) reunía a jóvenes políticos y escritores independientemente de su rango y posición.

El Risanamento

La transformación más importante de la ciudad durante sus años como capital, no fue social sino urbanística. Florencia, a pesar de los gobiernos reformistas de la Casa de Lorena en los siglos XVIII y XIX, apenas había variado desde que los Médici llegaron al poder en el siglo XVI. El núcleo histórico medieval, con estrechas calles y plazuelas, apenas había mutado, tampoco la extensión de la ciudad, delimitada por las murallas y, más recientemente, por los muros aduaneros.

Como símbolo del naciente Reino de Italia, muchas ciudades a lo largo y ancho de la península, iniciaron importantes transformaciones para equipararse a las grandes urbes europeas. Con el traslado de la capital de Turín, ciudad moderna y de trazado reticular, a Florencia, las necesidades de modernización se hicieron cada vez más acuciantes. No en vano, la ciudad toscana había pasado de 115.000 habitantes en 1861, año de la fundación del reino, a 150.000 en 1865, antes del traslado de la capital, para alcanzar los 194.000 en 1870.

Fue al arquitecto Giuseppe Poggi el que recibió el prestigioso encargo de liderar el risanamento ("saneamiento") de Florencia. El 18 de febrero de 1865, es decir, pocos días después de la llegada del Rey, el ayuntamiento aprobó, con cierta urgencia, la realización del llamado "Piano Poggi" ("Plan Poggi"). Requisitos indispensables para realización fueron: proporcionar alojamiento a los funcionarios a llegar, conjugar intereses públicos y privados limitando la especulación, defender la ciudad de las crecidas del Arno y dar a la ciudad un aspecto moderno y burgués como el de otras grandes urbes europeas.

La realizaciones del Piano Poggi se concretaron en:
 los Viali de Circonvallazione (Avenidas de Circunvalación) en la mitad norte: al modo de los boulevards franceses o los rings alemanes eran amplias avenidas arboladas que separaban el casco antiguo de los nuevos barrios regulares y burgueses en las afueras.
 el Viale dei Colli (Avenida de las Colinas) en la mitad sur: con una función parecida, la serpenteante avenida permitía pasear disfrutando de amplias panorámicas de la ciudad.
 los Nouvi Quartieri (Nuevos Distritos): trazados de forma regular y gozando de las últimas comodidades y avances técnicos, servirían para alojar a los funcionarios y a la naciente burguesía florentina.
 edificación de nuevos espacios públicos como el Mercato Centrale o la Piazza Vittorio Emanuele II.
 regularización de cierta áreas del casco antiguo, como la Piazza del Duomo o la Via de'Tornabuoni.
 realización de la fachada inacabada del Duomo.
Si bien la fiebre constructiva caracterizó el periodo de Firenze Capitale, el Risanamento se alargó más allá de 1870. Algunas intervenciones, de hecho, tuvieron lugar ya a finales de siglo, como la fachada del Duomo o la Piazza Vittorio Emanuele II.

Aunque calificado por algunos de "Sventramento" ("Destripamiento"), a causa de la cantidad de viejos barrios y edificios derribados, el Risanamento contribuyó a modernizar Florencia, equiparándola al resto de grandes ciudades europeas.

El fin del Firenze Capitale

La toma de Roma

Con el estallido de la Guerra franco-prusiana en el verano 1870, Napoléon III se vio obligado a retirar sus zuavos de Roma. El gobierno italiano esperó, sin embargo, hasta la derrota del Segundo Imperio francés el 2 de septiembre, para lanzar la ofensiva sobre la capital papal. Después de varios intentos fracasados de facilitar una entrega negociada de la ciudad, el ejército italiano asedió Roma. En 19 de septiembre, tras un breve bombardeo, se abrió una brecha en los muros aurelianos y la ciudad fue tomada sin que la población o las tropas papales apenas ofrecieran resistencia. La llamada "cuestión romana" parecía resuelta, sin embargo, el Pio IX se declaró "prisionero en el Vaticano" y se negó a reconocer al Reino de Italia y su control sobre Roma. Dicha situación no se solucionó hasta la firma de los Pactos de Letrán en 1929.

Leyes y plebiscitos

La noticia de la toma de Roma fue celebrada en Florencia con júbilo y manifestaciones populares, a pesar de que los florentinos sabían que eso significaba perder su estatus de capital. El 2 de octubre se celebró un plebiscito en el que Roma y el Lazio votaron incorporarse al Reino de Italia, el día 8 una delegación de notables romanos, presidida por el Duque de Sermoneta, presidente del gobierno provisional de la ciudad, llegaron a Florencia para entregar al Rey los resultados del plebiscito. El día 20 de noviembre se celebraron elecciones generales para elegir la composición de la XI legislatura, la última que tendría su sede en Florencia.

El mes de diciembre estuvo casi enteramente dominado por las negociaciones y la aceptación, por parte del príncipe Amadeo, hijo del Rey, del trono español. El día 30 del mismo mes, a raíz de las importantes inundaciones que habían tenido lugar en Roma, el Rey decidió visitar por primera vez la ciudad, manteniendo un perfil bajo, sin pompa ni grandes recibimientos, visitando a los damnificados y reuniéndose con el consejo municipal. El día 1 de enero estaba de vuelta a Florencia.

El día 1 de febrero, la Cámara de Diputados aprobó la ley n.33, sobre el traslado de la capital a Roma, Vittorio Emanuele II la promulgó el día 3. En marzo, se aprobó la famosa Ley de Garantías para normalizar las relaciones con el Papado. No obstante, Pío IX, que se consideraba un "prisionero político encerrado en el Vaticano", la rechazó por unilateral.

Adiós a Florencia
El ministro de infraestructuras, Giuseppe Gadda, fue enviado a Roma en abril para sondear el traslado de los ministerios, que debía empezar en junio. A principios de dicho mes se celebró la Fiesta del Estatuto albertino y poco después el solemne funeral del poeta y patriota italiano Ugo Foscolo, enterrado en la Santa Croce. Fueron los dos últimos grandes festejos del Firenze Capitale.

El 24 de junio, en una emotiva sesión, la Cámara de Diputados tuvo su última reunión en Florencia. Su presidente, Giuseppe Biancheri, agradeció:"a la Guardia Nacional, al Ayuntamiento y a la ciudad de Florencia sus servicios. La noble Florencia ha bien merecido su patria. Ahora, honorables colegas, nos vemos en Roma. En Roma continuaremos trabajando por nuestra patria y por el Rey."No en vano, meses antes, el 20 de diciembre, Florencia había sido declarada "ciudad benemérita de la nación", por haber sido "sede temporal" del gobierno italiano.

La despedida definitiva llegó el 28 de junio a las cuatro de la tarde, cuando el Rey tomó el tren hacia Nápoles para inaugurar la "Esposizione delle Industrie marittime". Ante la presencia del gobierno, del consejo municipal y aclamado por la muchedumbre, el soberano se despidió afectuosamente del alcalde Ubaldino Peruzzi. El cronista Giuseppe Massari comentó que "el Rey abandona Florencia con pesar".

El 2 de julio, llegando desde Nápoles, el Rey entró triunfalmente en Roma, un día antes habían llegado los miembros del Senado y la Cámara, así como el cuerpo diplomático. Entre vítores, el soberano alcanzó el Palazzo del Quirinale, antigua residencia papal y ahora nueva sede de la corte.

La Cámara de Diputados se instaló en el Palazzo Montecitorio y la vida política pudo re-emprenderse el 27 de noviembre, con una inaugural "seduta reale" ("sesión real").

Mientras tanto, en Florencia comenzó un lento éxodo, casi 25.000 personas abandonaron la ciudad, que poco a poco fue recuperando su tranquilidad de ciudad de provincia. Más allá de las vivencias históricas, el periodo Firenze Capitale dejó una profunda huella en el urbanismo de la ciudad, con la completa modernización de barrios y distritos. Florencia se había convertido en una digna vitrina del naciente y triunfante Reino de Italia.

Referencias

Florencia
Historia contemporánea de Italia
Historia de Florencia
Reino de Italia
Unificación de Italia
Años 1860 en Italia

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I think my husband would agree with all of those statements. We hired a doula for our second birth and we were so happy we did!
We had a doula for both of my boys. Frankly, I felt like the doula was more helpful with our second. This may sound bad but my husband had just gotten a huge promotion (in terms of responsibility) the week before our second was born. He was home but able to work during the first several hours of labor because I had a doula. He felt like he could take care of other stuff instead of just holding my hand the whole time and then, when it was time, he was right there in the midst of it.
We also decided to hire a doula for my second birth. My husband wasn't sure but wanted me to feel supported. Afterwards, he was glad that she was there to reassure me through a natural water birth and take some of the pressure of my squeezing their hands during transition time!
I absolutely loved having a doula! My husband felt lost and often anxious with our first birth, even with a midwife. We both loved having a doula for our 2nd and 3rd births. I recommend having a doula to ALL my friends!
I don't have children but the idea of doulas has always seemed like a very good one to me. It's great to see the husband's side as well.


text:
Q: Giving an html template text using jquery I am currently loading an html template into my index.html using:
$("#x").load("template.html");

and my template file is something like:
<html>
  <div id="textBox">
  </div>
</html>

Is it possible for me to give the template text from the index.html?
Thanks for your time,
LL

A: Add the text from a callback in load()'s complete argument:
$("#x").load("template.html", function() {
    $("#textBox").html("Added text");
});