Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless: Trade-Offs Between Spatial Resolution and Temporal Scale

BACKGROUND: San Francisco has the highest rate of tuberculosis (TB) in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures. METHODS AND FINDINGS: We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model. CONCLUSION: Systematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction

Optimizing Use of Multistream Influenza Sentinel Surveillance Data

We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998–2007. Our study demonstrates that simultaneous monitoring of multiple streams of influenza surveillance data can improve the accuracy and timeliness of alerts compared with monitoring of aggregate data or of any single stream alone

Lo-Fi Matchmaking: A Study of Social Pairing for Backpackers

There is a new world emerging around mobile social networks and the technologies used to facilitate and mediate them. It is technically feasible for mobile social software such as pairing or matchmaking systems to introduce people to others and assist information exchange. However, little is known about the social structure of many mobile communities or why they would want pairing systems. When these systems are built, it is not clear what the social response by those communities will be or what the systems will be like to use in practice. While engaged in other work determining requirements for a mobile travel assistant we saw a potentially useful application for a pairing system to facilitate the exchange of travel information between backpackers. To explore this area, we designed two studies involving usage of a low-fidelity role prototype of a social pairing system for backpackers. Graphs of the resulting social pairings showed backpackers who were hubs in the network of travel information. It also demonstrated the effect of travel direction on information utility. Backpackers rated the utility of different pairing types, and provided feedback on the social implications of being paired based on travel histories. Practical usage of the social network pairing activity and the implications of broader societal usage are discussed

VX Hydrolysis by Human Serum Paraoxonase 1: A Comparison of Experimental and Computational Results

Human Serum paraoxonase 1 (HuPON1) is an enzyme that has been shown to hydrolyze a variety of chemicals including the nerve agent VX. While wildtype HuPON1 does not exhibit sufficient activity against VX to be used as an in vivo countermeasure, it has been suggested that increasing HuPON1's organophosphorous hydrolase activity by one or two orders of magnitude would make the enzyme suitable for this purpose. The binding interaction between HuPON1 and VX has recently been modeled, but the mechanism for VX hydrolysis is still unknown. In this study, we created a transition state model for VX hydrolysis (VXts) in water using quantum mechanical/molecular mechanical simulations, and docked the transition state model to 22 experimentally characterized HuPON1 variants using AutoDock Vina. The HuPON1-VXts complexes were grouped by reaction mechanism using a novel clustering procedure. The average Vina interaction energies for different clusters were compared to the experimentally determined activities of HuPON1 variants to determine which computational procedures best predict how well HuPON1 variants will hydrolyze VX. The analysis showed that only conformations which have the attacking hydroxyl group of VXts coordinated by the sidechain oxygen of D269 have a significant correlation with experimental results. The results from this study can be used for further characterization of how HuPON1 hydrolyzes VX and design of HuPON1 variants with increased activity against VX.United States. Defense Threat Reduction Agenc

Indirect Reciprocity under Incomplete Observation

Indirect reciprocity, in which individuals help others with a good reputation but not those with a bad reputation, is a mechanism for cooperation in social dilemma situations when individuals do not repeatedly interact with the same partners. In a relatively large society where indirect reciprocity is relevant, individuals may not know each other's reputation even indirectly. Previous studies investigated the situations where individuals playing the game have to determine the action possibly without knowing others' reputations. Nevertheless, the possibility that observers of the game, who generate the reputation of the interacting players, assign reputations without complete information about them has been neglected. Because an individual acts as an interacting player and as an observer on different occasions if indirect reciprocity is endogenously sustained in a society, the incompleteness of information may affect either role. We examine the game of indirect reciprocity when the reputations of players are not necessarily known to observers and to interacting players. We find that the trustful discriminator, which cooperates with good and unknown players and defects against bad players, realizes cooperative societies under seven social norms. Among the seven social norms, three of the four suspicious norms under which cooperation (defection) to unknown players leads to a good (bad) reputation enable cooperation down to a relatively small observation probability. In contrast, the three trustful norms under which both cooperation and defection to unknown players lead to a good reputation are relatively efficient

Social Closure and the Evolution of Cooperation via Indirect Reciprocity

Direct and indirect reciprocity are good candidates to explain the fundamental problem of evolution of cooperation. We explore the conditions under which different types of reciprocity gain dominance and their performances in sustaining cooperation in the PD played on simple networks. We confirm that direct reciprocity gains dominance over indirect reciprocity strategies also in larger populations, as long as it has no memory constraints. In the absence of direct reciprocity, or when its memory is flawed, different forms of indirect reciprocity strategies are able to dominate and to support cooperation. We show that indirect reciprocity relying on social capital inherent in closed triads is the best competitor among them, outperforming indirect reciprocity that uses information from any source. Results hold in a wide range of conditions with different evolutionary update rules, extent of evolutionary pressure, initial conditions, population size, and density

LM. Early detection of tuberculosis outbreaks among the San Francisco homeless: trade-offs between spatial resolution and temporal scale. PLOS One

Background San Francisco has the highest rate of tuberculosis (TB) in the US. Exact locations of patients' primary residences at the time of diagnoses are routinely collected as part of the TB surveillance program. It has been shown for syndromic surveillance data that when exact geographic coordinates of individual patients are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. Here, we examine the effect of varying the spatial resolution in the TB data on the San Francisco homeless population, on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures. Methods and Findings We apply a variation of space-time permutation scan statistic to the TB data in which a patient location is either represented by its exact longitude and latitude or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify both simulated and real TB outbreaks, however, better performance measures were attained under simulated cases as compared to actual outbreaks. Finally, we compare the dependency of the method on the extent of data needed for parameter estimations under different geospatial constraints, and show that smaller amount of data is required when exact locations are used to achieve similar performance measures. Conclusion We investigate the relationship between using exact locations of TB patients, the timeliness of identifying real TB outbreaks, and the amount of legacy data required for early detection. We demonstrate that using higher spatial resolution results in higher detection rate, but more importantly in timely detection of TB outbreaks even when the amount of available data is relatively small. For higher spatial resolution, we also show generally better sensitivity for simulated outbreaks as compared to actual outbreaks, though this difference can be explained by the variations in dispersal structure of cases between the two. Trading higher spatial resolution for better performance, however, is ultimately a tradeoff between maintaining patient confidentiality and improving public health. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction