RESUMO
Is it possible to reduce crime without exacerbating adversarial relationships between police and citizens? Community policing is a celebrated reform with that aim, which is now adopted on six continents. However, the evidence base is limited, studying reform components in isolation in a limited set of countries, and remaining largely silent on citizen-police trust. We designed six field experiments with Global South police agencies to study locally designed models of community policing using coordinated measures of crime and the attitudes and behaviors of citizens and police. In a preregistered meta-analysis, we found that these interventions led to mixed implementation, largely failed to improve citizen-police relations, and did not reduce crime. Societies may need to implement structural changes first for incremental police reforms such as community policing to succeed.
RESUMO
Seasonal influenza infects approximately 5-20% of the U.S. population every year, resulting in over 200,000 hospitalizations. The ability to more accurately assess infection levels and predict which regions have higher infection risk in future time periods can instruct targeted prevention and treatment efforts, especially during epidemics. Google Flu Trends (GFT) has generated significant hope that "big data" can be an effective tool for estimating disease burden and spread. The estimates generated by GFT come in real-time--two weeks earlier than traditional surveillance data collected by the U.S. Centers for Disease Control and Prevention (CDC). However, GFT had some infamous errors and is significantly less accurate at tracking laboratory-confirmed cases than syndromic influenza-like illness (ILI) cases. We construct an empirical network using CDC data and combine this with GFT to substantially improve its performance. This improved model predicts infections one week into the future as well as GFT predicts the present and does particularly well in regions that are most likely to facilitate influenza spread and during epidemics.
