Spatial targeting of infectious disease control: identifying multiple, unknown sources

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Using geographic profiling to locate elusive nocturnal animals: A case study with spectral tarsiers

© 2015 The Zoological Society of London. Estimates of biodiversity, population size, population density and habitat use have important implications for management of both species and habitats, yet are based on census data that can be extremely difficult to collect. Traditional assessment techniques are often limited by time and money and by the difficulties of working in certain habitats, and species become more difficult to find as population size decreases. Particular difficulties arise when studying elusive species with cryptic behaviours. Here, we show how geographic profiling (GP) - a statistical tool originally developed in criminology to prioritize large lists of suspects in cases of serial crime - can be used to address these problems. We ask whether GP can be used to locate sleeping sites of spectral tarsiers Tarsius tarsier in Sulawesi, Southeast Asia, using as input the positions at which tarsier vocalizations were recorded in the field. This novel application of GP is potentially of value as tarsiers are cryptic and nocturnal and can easily be overlooked in habitat assessments (e.g. in dense rainforest). Our results show that GP provides a useful tool for locating sleeping sites of this species, and indeed analysis of a preliminary dataset during field work strongly suggested the presence of a sleeping tree at a previously unknown location; two sleeping trees were subsequently found within 5m of the predicted site. We believe that GP can be successfully applied to locating the nests, dens or roosts of elusive animals such as tarsiers, potentially improving estimates of population size with important implications for management of both species and habitats.We thank Operation Wallacea for supporting S.C.F. in thisproject and for providing logistical support for the fieldwork,and Aidan Kelsey for invaluable assistance in the field. Wethank the Indonesian Institute of Sciences (LIPI) andKementerian Riset dan Teknologi Republik Indonesia(RISTEK) for providing permission to undertake the work(RISTEK permit no. 211/SIP/FRP/SM/VI/2013, and BalaiKonservasi Sumber Daya Alam (BKSDA) for theirassistance

Spatially clustered count data provide more efficient search strategies in invasion biology and disease control.

Geographic profiling, a mathematical model originally developed in criminology, is increasingly being used in ecology and epidemiology. Geographic profiling boasts a wide range of applications, such as finding source populations of invasive species or breeding sites of vectors of infectious disease. The model provides a cost-effective approach for prioritising search strategies for source locations and does so via simple data in the form of the positions of each observation, such as individual sightings of invasive species or cases of a disease. In doing so, however, classic geographic profiling approaches fail to make the distinction between those areas containing observed absences and those areas where no data were recorded. Absence data are generated via spatial sampling protocols but are often discarded during the inference process. Here we construct a geographic profiling model that resolves these issues by making inferences via count data - analysing a set of discrete sentinel locations at which the number of encounters has been recorded. Crucially, in our model this number can be zero. We verify the ability of this new model to estimate source locations and other parameters of practical interest via a Bayesian power analysis. We also measure model performance via real-world data in which the model infers breeding locations of mosquitoes in bromeliads in Miami-Dade County, Florida. In both cases, our novel model produces more efficient search strategies by shifting focus from those areas containing observed absences to those with no data, an improvement over existing models that treat these areas equally. Our model makes important improvements upon classic geographic profiling methods, which will significantly enhance real-world efforts to develop conservation management plans and targeted interventions

Extending Geographic Profiling Likelihoods to Include a of Data Range Types Generated in Ecology and Epidemiology.

PhD Theses.This thesis revolves around the development of geographic pro ling, a spatial model originally developed in criminology to identify areas that likely contain a suspect's home or workplace, based upon where they committed their crimes. Geographic pro ling is still used to this day in investigations of serial crime but has recently found a cornucopia of applications in ecology and epidemiology. For example, the sightings of an invading species, responsible for the decline of local wildlife, can be used to target their nesting sites for e cient removal from an environment. Similarly, households testing positive for an infectious disease can be used to target breeding sites of vectors responsible for the disease's transmission. Despite countless applications, geographic pro ling models are limited to considering only a single type of spatial data; a set of points on a map. The work I have conducted addresses this issue by specifying a set of geographic pro ling models that can deal with multiple kinds of spatial data. In ecology, eld experiments surveying alien species often record the location of an encounter and count the number of individuals present. I make the rst development accordingly, by describing a model that produces interventions based on spatial count data. I then describe the rst instance that geographic pro ling is applied to simulated and real-world count data, comparing the conclusions of this new model to that of an existing model that only considers the location recorded but not the number of individuals counted. The next development focusses on applications in epidemiology. When testing members of a household for an infectious disease, the test location and prevalence rate; the proportion of individuals testing positive, are recorded. Hence, I build and test a geographic pro ling model that draws conclusions via spatial prevalence data. In the nal part of the thesis, I return to analysing the type of data common to geographic pro ling, a set of points on a map. I equip users with the exibility to specify varying assumptions about the process generating these spatial points, ultimately leading to a model for better describing real data. To conclude, I summarise the impact of each new development in this thesis and take a step back, establishing where geographic pro ling ts within a universe of spatial models

Expanded and mega-plex STR panels as a tool for presumptive population assignment

Historically, forensic STR panels have been unsuccessful for population assignment due to the limited ancestry information that can be derived from the non-coding STR loci and the low number of loci included in the panel. However, given the recent adoption of expanded (16+ loci) and ‘mega-plex’ (23+ loci) STR panels, the ability to identify source population groups may be improved. This study assessed the impact of increasing locus number on population assignment under different analysis conditions using a published US population dataset comprised of individuals from the African American, Caucasian, Hispanic and Asian populations. The Bayesian clustering programme STRUCTURE was used to assess first, whether increasing the number of loci and the inclusion of known sample population data enabled greater resolution between the four populations in the dataset, and second, the utility for population assignment using criteria based on inferred ancestry scores. Results suggest that increasing the number of loci and including population of origin data allowed the identification of more distinct populations, with three primary populations being observed; African American, Asian, and Caucasian/Hispanic. The close grouping of the Caucasian and Hispanic populations is supported by their recently common ancestry from Western Europe. The ability of the programme to support population assignment to each of the four existing populations was assessed through the application of population and panel specific assignment thresholds based on the inferred ancestry scores obtained from the analysis programme. Predictive accuracy based on a training dataset of 984 individuals suggest that assignment accuracy is > 96% across the four populations and can reach 100% under some test conditions. The accuracy was > 90% when blind testing was performed on 40 ‘unknown’ individuals. As such, the approach described is considered within the acceptable range for a presumptive test and can be performed using data already collected as part of routine forensic investigations

Police Militarization And Overuse of Force: An Analysis of the Efficacy of Paramilitary Units And Society

The role of the police is to maintain social order and safety through the enforcement of law. They investigate, prevent, and detect criminal activity. However, the problem with police protection today is overuse of force which often comes through the term militarism regarding agency tenets and dogma. Also, social media has focused on police overuse of force, exacerbating race riots and retaliatory police killings. Because of this, police reform has become a significant concern, not only in the United States but also abroad. The methodological approach for this dissertation is a quantitative analysis, data used is secondary, statistical procedure is chi square (cross tabulation) and multiple linear regression. This dissertation’s expected findings are to answer whether there is a statistically significant difference in police overuse of force by race and geographic areas; is there a relationship between DHS 1033 Program and violent crimes and property crimes; how accurately can a DHS 1033 Program index be predicted from a linear combination of crime rates

Bayesian Geographical Profiling in Terrorism Revealing

A significant part of research in terrorism studies focuses on the analysis of terrorist groups. An important issue for this type of research is that a large number of attacks are not attributed to a specific group. As an appropriate approach to solve the problem of attributing group responsibility we applied the geographic profiling theory. We analyzed several terrorist organizations which typically commit attacks far away from their headquarters. We proposed an innovative method based on Bayesian approach to find the organization’s base and to attribute responsibility to perpetrators of terrorist attacks. We compared the results with classical techniques used in criminology. The real data analysis shows rationale for the proposed approach. Analyzed data comes from the Global Terrorism Database which is currently the most extensive database on terrorism ever collected

A new Geographic Profiling Suspect Mapping And Ranking Technique for crime investigations: GP-SMART

This study developed and tested a new geographic profiling method for automating suspect prioritisation in crime investigations. The Geographic Profiling Suspect Mapping And Ranking Technique (GP-SMART) maps suspects' activity locations available in police records—such as home addresses, family members' home addresses, prior offence locations, locations of non-crime incidents, and other contacts with police—and ranks suspects based on both the proximity and nature of these locations, relative to an input crime. In accuracy tests using solved burglary, robbery and extra-familial sex offence cases in New Zealand (n = 4511), GP-SMART ranked the offender at or near the top of the suspect list at rates greatly exceeding chance. Highlighting the benefit of its novel inclusion and differentiation of many different types of activity location, GP-SMART also outperformed baseline methods—approximating existing algorithms—that ranked suspects using only the proximity of their activity locations, or home addresses, to the input crime