Evaluating Stochastic Seeding Strategies in Networks

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated “off-policy” using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can substantially increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an antibullying intervention. This paper was accepted by Gui Liberali, Special Issue on Data-Driven Prescriptive Analytics.

Workplace Violence Faced by Nurses in a Rural Tertiary Hospital of Central India: Pattern & Intervention

It is often said about the nurses that they are strong enough to tolerate everything and soft enough to understand everyone. It would not be an overstatement to say that, in the hierarchy of the healthcare system, they are the most taken for granted population and thus one of the most vulnerable. In most countries, the nursing staffs have inflexible working hours; have maximum periods of interaction with the consumers of healthcare and are given a status of secondary citizens in comparison with their doctor counterparts. The power to exercise freedom of expression and peaceful assembly without apprehensions of violence or intimidation is regarded as a basic human right, by United Nations’ universal declaration on human rights [1]. Workplace violence can be regarded as any act of aggression manifesting into a physical or emotional assault, towards a person on duty [2]. A work place can be regarded as any setting where a person renders his professional duties, which in the cases of nurses is mostly the hospital settings. It is inclusive of the entire work environment, such as the parking spaces or premises or even a temporary place of deputation for work purposes. The perpetrator may be any person who is the recipient of medical help or a senior or junior fellow worker, a member of the organization of work or even a random individual with no legitimate workplace relationship to the victim but merely a visitor in the hospital

Modeling Genetic Complexity in the Classroom

This classroom exercise aims to help students understand the three Ps of genetic complexity: polymorphic, polygenic, and pleiotropic. Using coin flips and dice rolls, students are able to generate the genotype and phenotype of a random individual. From there, students find a mate for this individual and determine the phenotype of their offspring. The randomness generated by the coin and dice mechanics illustrates the principles of independent assortment and segregation, variable gene expression, and environmental effects.

Selection and use of microsatellite markers for individual identification and meat traceability of six swine breeds in the Chinese market

Meat traceability based on molecular markers is exerting a great influence on food safety and will enhance its key role in the future. This study aimed to investigate and verify the polymorphism of 23 microsatellite markers and select the most suitable markers for individual identification and meat traceability of six swine breeds in the Chinese market. The mean polymorphism information content value of these 23 loci was 0.7851, and each locus exhibited high polymorphism in the pooled population. There were 10 loci showing good polymorphism in each breed, namely, Sw632, S0155, Sw2406, Sw830, Sw2525, Sw72, Sw2448, Sw911, Sw122 and CGA. When six highly polymorphic loci were combined, the match probability value for two random individual genotypes among the pig breeds (Beijing Black, Sanyuan and Taihu) was lower than 1.151 E-06. An increasing number of loci indicated a gradually decreasing match probability value and therefore enhanced traceability accuracy. The validation results of tracing 18 blood and corresponding meat samples based on five highly polymorphic loci (Sw2525, S0005, Sw0107, Sw911 and Sw857) were successful, with 100% conformation probability, which provided a foundation for establishing a traceability system for pork in the Chinese market.