Project title: Using Non-Local Connectivity Information to Identify Nascent Disease Outbreaks

Transcription

1 MIDAS Pilot Study Final Report Project title: Using Non-Local Connectivity Information to Identify Nascent Disease Outbreaks Tongbo Huang, James Sharpnack and Aarti Singh Carnegie Mellon University 1 Specific Aims A recent innovation in the modeling of the spread of epidemics is the use of crude and unreliable indicators such as web search queries, school absenteeism, or thermometer sales that are easily monitored in human populations. However, most state-of-the-art disease outbreak detection methods use the gravity model to fuse data from local nearby regions [8, 15, 13, 17, 11, 14] to boost the signal-to-noise ratio of such indicators. This pilot study was aimed at investigating the use of non-local connectivity information, such as air-traffic data, to detect epidemics like influenza in their nascent stages. Specifically, we targeted the following two goals: 1) Identify the occurrence of an influenza outbreak in its nascent stages by exploiting the structure of the non-local connectivity graph between geographically disparate regions. 2) Learn the non-local connectivity graph which influences spread of the influenza virus between different locations. In this project, we focused on detecting influenza outbreaks using ILI (Influenza like illness) rates based on Google search queries. Our results indicate that air traffic networks can be exploited to perform detection of influenza outbreaks earlier than gravity based models. Furthermore, we attempted to learn the network of dependency between ILI counts at different cities from data and it was found to be very similar to the air traffic graph, again emphasizing the role that non-local connectivity plays in spread of a disease. As part of this study, we also developed two tools that effectively visualize and summarize data about disease intensity at US cities in general, and more specifically Google flu trend data and live Twitter feeds. 2 Studies and Results 2.1 Introduction Disease outbreaks such as the seasonal influenza epidemics, have a signicant impact on public health. Seasonal influenza accounted for an average of 41,400 death per year within the US from 1979 to 2001 with high variability (a 95% confidence interval of 27,100 to 55,700) [9]. Influenza has a mild effect on most individuals, but for high-risk individuals, influenza may cause complications such as pneumonia, bronchitis, sinus infections and ear infections. The center for disease control s (CDC) most recommended preventative measure for influenza is a yearly flu-vaccine. It would clearly be advantageous to preempt a severe influenza outbreak by timely detection in order to 1

2 strategically take preventative measures and to prepare for influenza complications. We concern ourselves with all illnesses that have influenza symptoms, ILIs. Thus, our analysis includes not only seasonal influenza but also pandemic influenza such as H1N1. It is very difficult to obtain directly ILI rate measurements in real time. As an alternative, new strategies have been developed that use measurements of related quantities, such as search queries on Google, school absenteeism, or thermometer sales. Google search queries have been monitored for health-seeking behavior, which has been proven to be associated with published ILI rates, as measured by the CDC as the percentage of patient visits with ILI symptoms [10]. In our study we will be studying the early detection of ILI rates for each city in the United States using the Google Flu Trends dataset [2]. State-of-the-art disease outbreak detection method use the gravity, or radiation, models to fuse data from local nearby regions, that relies on geographic distances [8, 15, 13, 17, 11, 14, 16, 19]. However, the propensity of an infection spreading from one city to another may not be purely a function of geographic distance for a highly mobile population. With this in mind, we propose to use flight records from the Bureau of Transportation Statistics to provide a transportation network between cities [1]. This dataset contains passenger counts for flights between airports, which is then matched with the nearest cities. The air traffic network is depicted in Figure 1 which is constructed by connecting each city to the five cities from which the traffic coming into that city is highest. Similarly, the five nearest city neighbor graph based on geographic distance is shown in Figure 2. Clearly, gravity models based on the geographic distance graph fail to capture the non-local disease spreading pathways as offered by air traffic connectivity. Figure 1: Air traffic network based on BTS data. [via Google Maps] 2.2 Methodology In all of the analyses we will consider a network to be represented by a weighted graph. A graph is a set of vertices, in this case cities, and edges that represent connections between the cities. Furthermore for each edge we associate a weight based on the influence that two adjacent cities have on each other. In the case of the air-traffic network, we will consider edge weights given by the total number of passengers traveling by air between cities. 2

3 Figure 2: Geographic graph based on nearest neighbors. In order to detect preemptively an outbreak in each city we will use kernel methods to smooth the raw ILI rates using the graph. Consider a graph with edge weight matrix W such that Wi,j is the edge weight from city i to j. W itself forms a valid kernel, which will be appropriate for the air-traffic data. We will let Yt denote the vector of observed ILI rates over the cities for date t. Then given a kernel matrix K, the smoothed counts are P j Ki,j Yt,j Ybt,i = P (1) l Ki,l With a smoothed infection count for each time period and city, we are then able to monitor the smoothed version which incorporates the neighbors of a city in the graph in proportion to their edge weights. We will compare this air-traffic based smoothed infection count with the gravity model based smoothed infection counts (where the graph weights are given by the traffic between cities based on gravity model). 2.3 Experiments The Google Flu Trends data [2] we used consists of ILI rates for 398 weeks spanning from 9/28/2003 to 5/8/2011 for 117 cities in the US. ILI rates are given as counts per 100,000 individuals, and range from 161 to 20, 836. As a baseline to compare our methods against, we use the US average ILI rates to do early detection. The raw ILI rates obtained from Google flu data are denoted by Yt,a and the smoothed counts are denoted by Y t,a. We would like to detect a spike in the Y t,a before it occurs in the raw values Yt,a. We define spikes by thresholding the (raw or smoothed) ILI rates at a certain level (we use levels of 1000, 1250 and 1500). We announce the detection of an outbreak for a city when the smoothed ILI rates (Y t,a ) exceeds that threshold. If this occurred before the corresponding spike in Yt,a this is a positive detection. Generally, we will concern ourselves with two quantities to validate a smoothing procedure: the ratio of positive detections to observed spikes in the raw data and the mean number of weeks in advance that the detection occured. In addition to the baseline country-wide average ILI counts, we perform the kernel smoothing procedure using air-traffic and gravity methods, and attempt early detection of outbreaks per city. air to be precisely the number of passengers We allow the weights for the air-traffic based smoother Ki,j 3

4 that traveled by air from city i to city j. Then we calculate the air-traffic based smoothed ILI rates by equation (1). We compare the performance of the air-traffic kernel to that of a geographic distance based kernel. We utilize the gravity model to estimate ground traffic between populations and determine a kernel matrix K grav for geographic distances. We allow the elements of K grav to be proportionate the estimated amount of ground traffic by, K grav i,j = C N α i N γ j f(d i,j ) where N i, N j is the populations of cities i and j and d i,j is the geographic distance between the cities. The exact specifications of α, γ, f and C are given in [7]. Then equation (1) is used to obtain gravity smoothed ILI rates. Figure 3 show the raw time series observations ( City ), air-traffic smoothed ILI rates ( Airline ), and geographically smoothed ILI rates ( Geo ) for two representative cities - Jackson, MS and State College, PA. Also, shown are the global ILI rates ( Total ). Notice that every tick mark on the x-axis represents a 5-week period. We observe that for many cities (such as Jackson, MS shown here) the Airline smoothed counts lead the raw city counts, geographically averaged counts as well as total counts by 2-3 weeks. For some other cities (such as State College, PA shown here) the airline smoothed counts may not lead significantly but are usually not worse than the other counts. This suggests that monitoring air-traffic smoothed counts can often lead to early detection of flu outbreak. We study this phenomena quantitatively in more detail. Table 1 gives the percentages of infection spikes that are detected by different smoothing techniques by the weeks prior to raw ILI counts exceeding the same threshold, averaged across all cities. The US column corresponds to country-wide average, the air column corresponds to air traffic smoothed counts, and the gravity column corresponds to smoothing using gravity-model based weights. We also consider a smoother AG that uses combination of traffic information from air-traffic and gravity-model based traffic counts i.e. simply the average of air and ground traffic counts. For every smoothing technique, the table also lists the mean number of weeks in advance that it can predict if the raw counts are expected to exceed the threshold, as well as the accuracy (the percentage of times the technique is able to predict earlier). We see that at low ILI rate epidemic threshold, the air-traffic smoother performs better than both the US average and the gravity model. Since low ILI rates correspond to the outbreak being in its nascent stages, this is the most useful regime in pre-empting epidemics. Thus, air traffic based smoothing can help preempt outbreaks by more than 3 weeks on average for low ILI rates. As the ILI threshold increases, the advantage due to air-traffic smoother decreases. When the ILI rates become high enough, the flu activity can now be detected in global US average, and thus it starts performing better than air traffic smoothing (as expected). This indicates that air-traffic is able to detect an outbreak in its nascent stages, but is not effective as an indicator when the epidemic has already exceeded large ILI rates. The ability of the gravity model to detect an epidemic increases with ILI threshold, however it never performs better than the air-traffic based smoother or even the global smoother. The estimator AG which uses both air traffic and gravity model together doesn t yield any performance gains over air-traffic alone. We believe this might be because the gravity based traffic estimate between two cities is very high compared to the air traffic between two cities, and thus biases the combined estimate to the gravity model leading to worse performance. 4

5 (a) Jackson, MS (b) State College, PA Figure 3: The kernel smoothed ILI rates for two cities, selected to show the ability of smoothing flu counts based on Air traffic connectivity network (Airline) to detect preemptively an outbreak. The raw ILI counts are shown in black (City), counts smoothed according to the gravity model or geographic distances are shown in purple (Geo), and the country-wide average of flu counts is shown in red (Total). 5

6 ILI thresh weeks US air gravity AG US air gravity AG US air gravity AG (%) mean accuracy 61(%) Table 1: ILI spikes are defined as increase of ILI rates above the outbreak threshold (1000, 1250, 1500). We report the percentages of epidemic detections by weeks prior to infection, the mean weeks in advance, and the percent of epidemics detected in advance (accuracy). The results are given for prediction with the US average (US), the air traffic smoothing (air), gravity model smoothing (gravity), and the combined estimates (AG). In the analysis presented above, the smoothed ILI counts at each city are obtained by a weighted average of all other cities. This might make the smoothed ILI rates more similar to the US average. As a possible remedy, we also considered truncating the kernel or weights such that only the k- nearest neighbors (knn) to a city are non-zero. Namely, if the kernel matrix is K and the k-th largest observation in row i is K i,(k) then we set { Ki,j knn Ki,j, if K = i,j K i,(k) 0, otherwise The results of air-traffic and gravity based smoothing using this nearest-neighbor procedure are listed in tables 2 and 3 for two different values of k = 5, 10. However, the performance with k = 5 is slightly worse. Also, notice that one cannot distinguish between the air-traffic results for the 10- nearest neighbors graph and the original air-traffic kernel smoother in Table 1. The mean weeks in advance for both these methods using an ILI threshold of 1000 for the air-traffic network smoothers is 3.47 weeks. 2.4 Learning the dependency graph To verify the role of air traffic connectivity on disease spread further, we attempted to learn the graph of dependencies between the ILI counts at different cities. Towards this end, we used a 6

7 ILI thresh weeks US air gravity AG US air gravity AG US air gravity AG (%) mean accuracy 61(%) Table 2: ILI spike detections with knn truncation for k = 5. recently proposed procedure [12, 18] that regresses the ILI counts at a city on the ILI counts of all other cities using the following optimization: arg min β i x i j i β i (j)x j 2 + λ j β i (j) Here x i, x j denote the vectors of ILI counts over time at city i and j, respectively, β i is a vector of regression coefficients for city i and β i (j) indicates whether the ILI counts at city i depend on the ILI counts at city j. The second term above denotes an l 1 penalty on the coefficient vector and encourages the entries of β i to be sparse (zero), as the ILI counts at one city are expected to depend on the ILI counts of a few other cities. The non-zero entries in the coefficient vector β i indicate an edge (if β i (j) is non-zero, then the ILI counts at city i depend on the ILI counts at city j). The effect of the penalty term can be controlled by a parameter λ > 0. The above optimization is performed for each city i. The resulting graph of learnt dependencies between city flu counts is show in Figure 4. Comparing it to Figures 1 and 2, we see that the learnt network graph contains several non-local edges (disease spreading pathways) akin to the air traffic network, but that are not captured in the geographic distance network based on the gravity model. Next, we used this learnt network graph to detect ILI spikes by smoothing the counts using weights K LN obtained by regressing the ILI counts of a city only on the ILI counts of cities that it i,j is connected to as follows: arg min K LN i,j x i (i,j) E K LN i,j x j 2 We used the ILI counts at half the time points to learn the network and evaluated the detection performance on the remaining half of the time points. Table 4 compares the performance of learnt dependency graph smoother (LN) with the country-wide average (US), air traffic smoothed ILI 7

8 ILI thresh weeks US air gravity AG US air gravity AG US air gravity AG (%) mean accuracy 61(%) Table 3: ILI spike detections but with knn truncation for k = 10. counts (air), geographic distance smoothed ILI counts (gravity) and mixed air traffic and geographic models (AG). The results indicate that smoothing based on the graph of learnt dependencies between ILI counts at cities performs the best in these preliminary experiments. 2.5 Conclusion This study was a preliminary investigation of how the air traffic network might inform the prediction of influenza epidemic events. We found that by exploiting the relationships between cities in air traffic data we are able to better detect a nascent influenza outbreak. This is in contrast to the network formed by the gravity model based on geographic distances. These results indicate the usefulness of non-local connectivity for early flu outbreak detection, that may provide residents and health workers of a city with advanced warning of heightened flu levels. A variety of prediction techniques were used to detect prematurely an epidemic event, but all had the same basic structure of using a kernel to smooth the ILI rates. The kernels that were most successful were based on the air-traffic rates. 3 Visualization Tools for Health Care Practitioners In addition to the specific aims described in the pilot study proposal, we also devoted some time to develop two visualization tools for health care practitioners (this was a future work component in the proposal). The first tool we developed is the ability to plot the disease intensity (e.g. ILI counts) at each city over time and generate a movie of the evolution of the disease spread, available at [6] for the Google flu data. A screenshot of the movie is shown in Figure 5 and denotes the intensity 8

9 Figure 4: ILI count dependency graph (dark edges) between cities learnt from Google flu trend data. of flu both by the color and size of circles centered at each city. One can get detailed values by hovering mouse over the circle as shown in the Figure. This helps us visualize how geographically disparate cities such as Chicago, New York, Houston and Los Angeles tend to experience disease outbreaks at the same time as they are connected by heavy air traffic. The second tool we developed relies on twitter data instead of the Google flu trend data. Inspired by the Now Trending Health Challenge [5] announced by US Office of ASPR (Assistant Secretary for Preparedness and Response), we investigated the use of twitter data for identifying which illnesses are trending, i.e. are either starting to spread or becoming of concern to people, at any point in time. We developed a preliminary web-based live application tool [3] that demonstrates the results of this investigation and is available to health-care practitioners online. As shown in Figure 6, the tool currently depicts the percentage of tweets related to a particular illness prior to the query time in various formats. The user can specify which illnesses are of interest. We hope to augment this tool to also declare early detection of an outbreak by using the detection methods we investigated in this study that exploit connectivity between regions. As a first step towards this, we are making the analysis be region and duration specific using geo-tags and time stamps in the twitter data. For example, a snapshot of tweets using 20 miles around Pittsburgh as region of interest and querying on west nile virus results in the tweets shown in Figure 6. 4 Summary of Contributions The contributions of this pilot study can be summarized as follows. In this pilot study we investigated whether non-local connectivity information can play a role in identifying nascent disease outbreaks. Specifically, in the context of influenza like illnesses, our results indicate that using air traffic connectivity information performs better than simply using local traffic information as predicted by the commonly used gravity model, and can help preempt flu outbreaks by more than 3 weeks on average. 9

10 ILI th weeks US air grav. AG LN US air grav. AG LN US air grav. AG LN mean acc Table 4: ILI spike detection rates as in Table 1 but including the results of smoothing using the learnt dependency network (LN). The numbers for previous estimators (US, air, gravity and AG) look different than Table 1 because the evaluation is performed on only half the data. The remaining half of data was used to learn the dependency graph. We also learnt the graph of dependency between ILI counts at different cities from data and it was found to be very similar to the air traffic graph, again emphasizing the role that non-local connectivity plays in spread of a disease. As part of this study, two visualization tools were developed that can be used by healthcare practitioners. One provides the ability to visualize disease intensity over time at each city in the United States. The other is an online tool that provides a summary of diseases gaining interest in the community based on live Twitter feeds. From an educational perspective, the project trained an undergraduate student on applying machine learning methods for a healthcare application, and developing user-friendly tools that can be used by practitioners. The student decided to pursue graduate studies and is now enrolled as a Masters student in Computer Science at Carnegie Mellon. 5 Future Plans In the future, we plan to extend this study in the following directions. This study does not explicitly incorporate the known models of infection spread when performing detection. In particular, there is no temporal modeling of the epidemic, but the results focus on processing time cross-sections of the data individually. Modelling epidemics as stochastic processes has a rich history, while the theory of epidemics over networks is a relatively new direction of research. We intend to extend our methods to some reasonable infection models over graphs while maintaining computational 10

11 Figure 5: A screenshot of the visualization tool developed for depicting disease intensity (in this case, ILI counts) at each city over time by the size and color of the circles centered at the respective city. feasibility. After we have incorporated the temporal dimension, we hope to submit a conference paper on our initial findings. Additionally, this work can also be extended to other diseases. An example is using patient sharing between hospitals to construct an inter-hospital network that may be able to better monitor the spread of staph bacterias such as MRSA. We will be coordinating with the affiliated MIDAS investigator Dr. Bruce Lee on this task. The control of such virulent infections is highly important to the health community. It is evident that an appropriate understanding of the networks that provide a conduit for infection is an essential step in disease control. We also plan to extend the web-based application we have developed for health care practitioners to be more user-friendly and analytical. On the user-friendly aspect, we are developing the ability to allow users to specify a region and time of interest, and the analysis will be restricted to those specifications. On the analytical front, instead of merely depicting the percentage of queries related to a particular illness, the application will generate a pre-emptive alert of the possibility of an outbreak by using the detection methods we have investigated in this pilot study that exploit connectivity between regions to boost the detection power of weak indicators such as tweets. Also, the application will be augmented to identify new diseases or health concerns not in the database. 6 Acknowledgements This study was funded by NIH s MIDAS National Center of Excellence at the University of Pittsburgh [4] under grant 5U54GM

12 Figure 6: A snapshot of the web-based application tool that depicts trending illnesses based on live twitter feeds. The first two figure show types of data summaries returned and the last one shows sample of tweets pulled up when querying for west nile virus with a 20 mile radius around 12 Pittsburgh as region of interest.

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