Hot Spot or Not: A Comparison of Spatial Statistical Methods to Predict Prospective Malaria Infections.

Within affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year. Two full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic. Guided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes. Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection

Continuous testing for Poisson process intensities: A new perspective on scanning statistics

We propose a novel continuous testing framework to test the intensities of Poisson Processes. This framework allows a rigorous definition of the complete testing procedure, from an infinite number of hypothesis to joint error rates. Our work extends traditional procedures based on scanning windows, by controlling the family-wise error rate and the false discovery rate in a non-asymptotic manner and in a continuous way. The decision rule is based on a \pvalue process that can be estimated by a Monte-Carlo procedure. We also propose new test statistics based on kernels. Our method is applied in Neurosciences and Genomics through the standard test of homogeneity, and the two-sample test

Spatial analysis and identification of environmental risk factors affecting the distribution of Indoplanorbis and Lymnaea species in semi-arid and irrigated areas of Haryana, India

Fasciolosis, amphistomosis and schistosomosis, transmitted by the freshwater snail species Indoplanorbis and Lymnaea, are important snail-borne diseases in India as they affect the entire spectrum of domestic animals causing substantial mortality and economic loss. Identifying any heterogeneity in the spatial distribution of these snail-borne diseases will allow for targeted disease control and efficient use of resources. The objectives of this study were threefold: (i) to describe and explore the spatial distribution of Indoplanorbis and Lymnaea in Rohtak and Jhajjar districts of Haryana, India (ii) to identify factors associated with occurrence of these freshwater snail species and (iii) to produce a map showing the predicted risk of occurrence of Lymnaea and Indoplanorbis spp. in the study area. Snails were collected from water bodies of 99 settlements out of a total of 453 in the study area. Kernel smoothing was used to generate a kernel ratio map while Kulldorff's spatial scan statistic was used to detect clusters of settlements with a high/low risk. Multivariable logistic regression showed that snails were almost ten times more likely to be present in rice-growing areas than in those not growing rice (OR 9.24) and that snails were less likely to be present with each 1 km increase in distance from a canal (OR 0.86). The regression model was used to produce a map illustrating the predicted risk of snail occurrence. Since the distribution of vector snails mirrors the distribution of snail-borne parasitic diseases, such spatial analysis helps to determine the relative risk of snail-infestation as well as snail-borne diseases' distribution and planning of control activities

Exploring spatiotemporal dynamics of urban fires: A case of Nanjing, China

Urban fire occurs within the built environment, usually involving casualties and economic losses, and affects individuals and socioeconomic activities in the surrounding neighborhoods. A good understanding of the spatiotemporal dynamics of fire incidents can offer insights into potential determinants of various fire events, therefore enabling better fire risk estimation which can assist with future allocation of prevention resources and strategic planning of mitigation programs. Using a twelve-year (2002–2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatiotemporal dynamics of urban fires using a range of exploratory spatial data analysis (ESDA) approaches. Of particular interest here are the fire incidents involving residential properties and local facilities due to their relatively higher occurrence frequencies. The results indicate that the overall amount of urban fires has greatly increased in the last decade and the spatiotemporal distribution of fire events varies among different incident types. The identified spatiotemporal patterns of urban fires in Nanjing can be linked to the urban development strategies and how they have been reflected in reality in recent years

Methods for the Investigation of Spatial Clustering, With Epidemiological Applications

When analysing spatial data, it is often of interest to investigate whether or not the events under consideration show any tendency to form small aggregations, or clusters, that are unlikely to be the result of random variation. For example, the events might be the coordinates of the address at diagnosis of cases of a malignant disease, such as acute lymphoblastic leukaemia or non-Hodgkin's lymphoma. This thesis considers the usefulness of methods employing nonparametric kernel density estimation for the detection of clustering, as defined above, so that specific, and sometimes limiting, alternative hypotheses are not required, and the continuous spatial context of the problem is maintained. Two approaches, in particular, are considered; first, a generalisation of the Scan Statistic to two dimensions, with a correction for spatial heterogeneity under the null hypothesis, and secondly, a statistic measuring the squared difference between kernel estimates of the probability density functions of the principal events and a sample of controls. Chapter 1 establishes the background for this work, and identifies four different families of techniques that have been proposed, previously, for the study of clustering. Problems inherent in typical applications are discussed, and then used to motivate the approach taken subsequently. Chapter 2 describes the Scan Statistic for a one-dimensional problem, assuming that the distribution of events under the null hypothesis is uniform. A number of approximations to the statistic's distribution and methods of calculating critical values are compared, to enable significance testing to be carried out with minimum effort. A statistic based on the supremum of a kernel density estimate is also suggested, but an empirical study demonstrates that this has lower power than the Scan Statistic. Chapter 3 generalises the Scan Statistic to two dimensions and demonstrates empirically that existing bounds for the upper tail probability are not sufficiently sharp for significance testing purposes. As an aside, the chapter also describes a problem that can occur when a single pseudo-random number generator is used to produce parallel streams of uniform deviates. Chapter 4 investigates a method, suggested by Weinstock (1981), of correcting for a known, non-uniform null distribution when using the Scan Statistic in one dimension, and proposes that a kernel estimator replace the exact density, the estimate being calculated from a second set of (control) observations. The approach is generalised to two dimensions, and approximations are developed to simplify the computation required. However, simulation results indicate that the accuracy of these approximations is often poor, so an alternative implementation is suggested. For the case where two samples of observations are available, the events of interest and a group of control locations. Chapter 5 suggests the use of the integrated squared difference between the corresponding kernel density estimates as a measure of the departure of the events from null expectation. By exploiting its similarity to the integrated square error of a k.d.e., the statistic is shown to be asymptotically normal; the proof generalises a central limit theorem of Hall (1984) to the two-sample case. However, simulation results suggest that significance testing should use the bootstrap, since the exact distribution of the statistic appears to be noticeably skewed. A modified statistic, with the smoothing parameters of the two k.d.e.'s constrained to be equal and non-random, is also discussed, and shown, both asymptotically and empirically, to have greater power than the original. In Chapter 6, the two techniques are applied to the geographical distribution of cases of laryngeal cancer in South Lancashire for the period 1974 to 1983. The results are similar, for the most part, to a previous analysis of the data, described by Diggle (1990) and Diggle et al (1990). The differences in the two analyses appear to be attributable to the bias or variability of the k.d.e.'s required to calculate the integrated squared difference statistic, and the inaccuracy of the approximations used by the corrected Scan Statistic. Chapter 7 summarises the results obtained in the preceding sections, and considers the implications for further research of the observations made in Chapter 6 regarding the weaknesses of the two statistics. It also suggests extensions to the basic methodology presented here that would increase the range of problems to which the two methods could be applied

Foot-and-mouth disease in Tanzania from 2001 to 2006.

Foot-and-mouth disease (FMD) is endemic in Tanzania, with outbreaks occurring almost each year in different parts of the country. There is now a strong political desire to control animal diseases as part of national poverty alleviation strategies. However, FMD control requires improving the current knowledge on the disease dynamics and factors related to FMD occurrence so control measures can be implemented more efficiently. The objectives of this study were to describe the FMD dynamics in Tanzania from 2001 to 2006 and investigate the spatiotemporal patterns of transmission. Extraction maps, the space-time K-function and space-time permutation models based on scan statistics were calculated for each year to evaluate the spatial distribution, the spatiotemporal interaction and the spatiotemporal clustering of FMD-affected villages. From 2001 to 2006, 878 FMD outbreaks were reported in 605 different villages of 5815 populated places included in the database. The spatial distribution of FMD outbreaks was concentrated along the Tanzania-Kenya, Tanzania-Zambia borders, and the Kagera basin bordering Uganda, Rwanda and Tanzania. The spatiotemporal interaction among FMD-affected villages was statistically significant (P≤0.01) and 12 local spatiotemporal clusters were detected; however, the extent and intensity varied across the study period. Dividing the country in zones according to their epidemiological status will allow improving the control of FMD and delimiting potential FMD-free areas