Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017–2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space–time clustering of isolates at the zip code level using scan statistic models. Space–time clusters were detected for each major serotype during 2017–2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.

Zika virus (ZIKV) is an arbovirus transmitted mainly by Aedes aegypti mosquitoes. Recent scientific evidence on Culex quinquefasciatus has suggested its potential as a vector for ZIKV, which may change the current risk zones. We aimed to quantify the world population potentially exposed to ZIKV in a spatially explicit way, considering the primary vector (A. aegypti) and the potential vector (C. quinquefasciatus). Our model combined species distribution modelling of mosquito species with spatially explicit human population data to estimate ZIKV exposure risk. We estimated the potential global distribution of C. quinquefasciatus and estimated its potential interaction zones with A. aegypti. Then we evaluated the risk zones for ZIKV considering both vectors. Finally, we quantified and compared the people under risk associated with each vector by risk level, country and continent. We found that C. quinquefasciatus had a more temperate distribution until 42° in both hemispheres, while the risk involving A. aegypti is concentrated mainly in tropical latitudes until 35° in both hemispheres. Globally, 4.2 billion people are under risk associated with ZIKV. Around 2.6 billon people are under very high risk associated with C. quinquefasciatus and 1 billion people associated with A. aegypti. Several countries could be exposed to ZIKV, which emphasises the need to clarify the competence of C. quinquefasciatus as a potential vector as soon as possible. The models presented here represent a tool for risk management, public health planning, mosquito control and preventive actions, especially to focus efforts on the most affected areas.

While parasites serve as prey, it is unclear how the spatial distribution of parasite predators provides transmission control and influences patterns of parasitism. Because many of its organisms are sessile, the rocky intertidal zone is a valuable but little used system to understand spatial patterns of parasitism and elucidate the underlying mechanisms driving these patterns. Sea anemones and barnacles are important space competitors in the rocky intertidal zone along the Pacific coast of North America. Anemones are voracious, indiscriminate predators; thus, they may intercept infectious stages of parasites before they reach a host. We investigate whether a sea anemone protects an associated barnacle from parasitism by Hemioniscus balani, an isopod parasitic castrator. At Coal Oil Point, Santa Barbara, California USA, 29% of barnacles were within 1 cm from an anemone at the surveyed tidal height. Barnacles associated with anemones had reduced parasite prevalence and higher reproductive productivity than those remote from sea anemones. In the laboratory, anemones readily consumed the transmission stage of the parasite. Hence, anemone consumption of parasite transmission stages may provide a mechanism by which community context regulates parasite prevalence at a local scale. Our results suggest predation may be an important process providing parasite transmission control.

Disaster medicine is characterized by shortages of everything but patients. There are never enough beds, equipment, personnel, or supplies. In the 2014 Ebola epidemic, another scarcity was maps. The need for maps of the affected areas, and the ways the maps were used, serve to emphasize the way maps have always served in both disaster medicine and public health preparedness. Those lessons are reviewed here in the context of the Ebola epidemic. (Disaster Med Public Health Preparedness. 2015;0:1-8)

Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region of China and compared it with the fixed KDE-based sRRF. We found that the adaptive KDE-based sRRF had a better ability to depict the heterogeneity of risk regions, but was more sensitive to altering the user-defined smoothing parameters. Specifically, the impact of bandwidths on the estimated risk value and risk significance (P value) was higher for the adaptive KDE-based sRRF, but lower on the estimated risk variation standard error (s.e.) compared with the fixed KDE-based sRRF. Based on this application the adaptive and fixed KDE-based sRRF have their respective advantages and disadvantages and the joint application of the two approaches can warrant the best possible identification of high-risk subregions of diseases.

The distributions of parasitic diseases are determined by complex factors, including many that are distributed in space. A variety of statistical methods are now readily accessible to researchers providing opportunities for describing and ultimately understanding and predicting spatial distributions. This review provides an overview of the spatial statistical methods available to parasitologists, ecologists and epidemiologists and discusses how such methods have yielded new insights into the ecology and epidemiology of infection and disease. The review is structured according to the three major branches of spatial statistics: continuous spatial variation; discrete spatial variation; and spatial point processes.

This study was performed to investigate space–time patterns of Campylobacter spp. colonization in broiler flocks in Norway. Data on the Campylobacter spp. status at the time of slaughter of 16 054 broiler flocks from 580 farms between 2002 and 2006 was included in the study. Spatial relative risk maps together with maps of space–time clustering were generated, the latter by using spatial scan statistics. These maps identified the same areas almost every year where there was a higher risk for a broiler flock to test positive for Campylobacter spp. during the summer months. A modified K-function analysis showed significant clustering at distances between 2·5 and 4 km within different years. The identification of geographical areas with higher risk for Campylobacter spp. colonization in broilers indicates that there are risk factors associated with Campylobacter spp. colonization in broiler flocks varying with region and time, e.g. climate, landscape or geography. These need to be further explored. The results also showed clustering at shorter distances indicating that there are risk factors for Campylobacter spp. acting in a more narrow scale as well.

Schistosomiasis control in China has, in general, been very successful during the past several decades. However, the rebounding of the epidemic situation in some areas in recent years raises concerns about a sustainable control strategy of which locating active transmission sites (ATS) is a necessary first step. This study presents a systematic approach for locating schistosomiasis ATS by combining the approaches of identifying high risk regions for schisotosmiasis and extracting snail habitats. Environmental, topographical, and human behavioural factors were included in the model. Four significant high-risk regions were detected and 6 ATS were located. We used the normalized difference water index (NDWI) combined with the normalized difference vegetation index (NDVI) to extract snail habitats, and the pointwise ‘P-value surface’ approach to test statistical significance of predicted disease risk. We found complicated non-linear relationships between predictors and schistosomiasis risk, which might result in serious biases if data were not properly treated. We also found that the associations were related to spatial scales, indicating that a well-designed series of studies were needed to relate the disease risk with predictors across various study scales. Our approach provides a useful tool, especially in the field of vector-borne or environment-related diseases.