Draft Genome Sequences of Yersinia pestis Strains from the 1994 Plague Epidemic of Surat and 2002 Shimla Outbreak in India

We report the first draft genome sequences of the strains of plague-causing bacteria, Yersinia pestis, from India. These include two strains from the Surat epidemic (1994), one strain from the Shimla outbreak (2002) and one strain from the plague surveillance activity in the Deccan plateau region (1998). Genome size for all four strains is ~4.49 million bp with 139–147 contigs. Average sequencing depth for all four genomes was 21x.     

Intracellular erythrocyte platelet-activating factor acetylhydrolase I inactivates aspirin in blood

Aspirin (acetylsalicylic acid) prophylaxis suppresses major adverse cardiovascular events, but its rapid turnover limits inhibition of platelet cyclooxygenase activity and thrombosis. Despite its importance, the identity of the enzyme(s) that hydrolyzes the acetyl residue of circulating aspirin, which must be an existing enzyme, remains unknown. We find that circulating aspirin was extensively hydrolyzed within erythrocytes, and chromatography indicated these cells contained a single hydrolytic activity. Purification by over 1400-fold and sequencing identified the PAFAH1B2 and PAFAH1B3 subunits of type I platelet-activating factor (PAF) acetylhydrolase, a phospholipase A(2) with selectivity for acetyl residues of PAF, as a candidate for aspirin acetylhydrolase. Western blotting showed that catalytic PAFAH1B2 and PAFAH1B3 subunits of the type I enzyme co-migrated with purified erythrocyte aspirin hydrolytic activity. Recombinant PAFAH1B2, but not its family member plasma PAF acetylhydrolase, hydrolyzed aspirin, and PAF competitively inhibited aspirin hydrolysis by purified or recombinant erythrocyte enzymes. Aspirin was hydrolyzed by HEK cells transfected with PAFAH1B2 or PAFAH1B3, and the competitive type I PAF acetylhydrolase inhibitor NaF reduced erythrocyte hydrolysis of aspirin. Exposing aspirin to erythrocytes blocked its ability to inhibit thromboxane A(2) synthesis and platelet aggregation. Not all individuals or populations are equally protected by aspirin prophylaxis, the phenomenon of aspirin resistance, and erythrocyte hydrolysis of aspirin varied 3-fold among individuals, which correlated with PAFAH1B2 and not PAFAH1B3. We conclude that intracellular type I PAF acetylhydrolase is the major aspirin hydrolase of human blood.     

Modeling to Predict Cases of Hantavirus Pulmonary Syndrome in Chile

Background

Hantavirus pulmonary syndrome (HPS) is a life threatening disease transmitted by the rodent Oligoryzomys longicaudatus in Chile. Hantavirus outbreaks are typically small and geographically confined. Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes.

Methodology

Monthly counts of confirmed HPS cases were obtained from the Chilean Ministry of Health for 2001–2012. There were an estimated 667 confirmed HPS cases. The data suggested a seasonal trend, which appeared to correlate with changes in climatological variables such as temperature, precipitation, and humidity. We considered several Auto Regressive Integrated Moving Average (ARIMA) time-series models and regression models with ARIMA errors with one or a combination of these climate variables as covariates. We adopted an information-theoretic approach to model ranking and selection. Data from 2001–2009 were used in fitting and data from January 2010 to December 2012 were used for one-step-ahead predictions.

Results

We focused on six models. In a baseline model, future HPS cases were forecasted from previous incidence; the other models included climate variables as covariates. The baseline model had a Corrected Akaike Information Criterion (AICc) of 444.98, and the top ranked model, which included precipitation, had an AICc of 437.62. Although the AICc of the top ranked model only provided a 1.65% improvement to the baseline AICc, the empirical support was 39 times stronger relative to the baseline model.

Conclusions

Instead of choosing a single model, we present a set of candidate models that can be used in modeling and forecasting confirmed HPS cases in Chile. The models can be improved by using data at the regional level and easily extended to other countries with seasonal incidence of HPS.     

Human Initiated Cascading Failures in Societal Infrastructures

In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.     

Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics

Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.