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- Creators: Department of Chemistry and Biochemistry
- Member of: ASU Regents' Professors Open Access Works

The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.


Background: Staphylococcus aureus and S. epidermidis biofilms differ in structure, growth and regulation, and thus the high-throughput method of evaluating biofilm susceptibility that has been published for S. epidermidis cannot be applied to S. aureus without first evaluating the assay's reproducibility and reliability with S. aureus biofilms.
Methods: Staphylococcus aureus biofilms were treated with eleven approved antibiotics, lysostaphin, or Conflikt®, exposed to the oxidation reduction indicator Alamar blue, and reduction relative to untreated controls was determined visually and spectrophotometrically. The minimum biofilm inhibitory concentration (MBIC) was defined as ≤ 50% Alamar blue reduction and a purple/blue well 60 min after the addition of Alamar blue. Because all of the approved antibiotics had MBICs >128 μg/ml (most >2048 μg/ml), lysostaphin and Conflikt®, with relatively low MBICs, were used to correlate Alamar blue reduction with 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) reduction and viable counts (CFU/ml) for S. aureus ATCC 29213 and three clinical isolates. Alamar blue's stability and lack of toxicity allowed CFU/ml to be determined from the same wells as Alamar blue absorbances.
Results: Overall, Alamar blue reduction had excellent correlation with XTT reduction and with CFU/ml. For ATCC 29213 and two clinical isolates treated with lysostaphin or Conflikt®, Alamar blue reduction had excellent correlation with XTT reduction (r = 0.93-0.99) and with CFU/ml (r = 0.92-0.98). For one of the clinical isolates, the results were moderately correlated for Conflikt® (r = 0.76, Alamar blue vs. XTT; r = 0.81, Alamar blue vs. CFU/ml) and had excellent correlation for lysostaphin (r = 0.95, Alamar blue vs. XTT; r = 0.97, Alamar blue vs. CFU/ml).
Conclusion: A reliable, reproducible method for evaluating biofilm susceptibility was successfully applied to S. aureus biofilms. The described method provides researchers with a simple, nontoxic, relatively inexpensive, high throughput measure of viability after drug treatment. A standardized biofilm Alamar blue assay should greatly increase the rate of discovery of S. aureus biofilm specific agents.






In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
