Transmission is the driving force in the dynamics of any infectious disease. A crucial element in understanding disease dynamics, therefore, is the 'transmission term' describing the rate at which susceptible hosts are 'converted' into infected hosts by their contact with infectious material. Recently, the conventional form of this term has been increasingly questioned, and new terminologies and conventions have been proposed. Here, therefore, we review the derivation of transmission terms, explain the basis of confusion, and provide clarification. The root of the problem has been a failure to include explicit consideration of the area occupied by a host population, alongside both the number of infectious hosts and their density within the population. We argue that the terms 'density-dependent transmission' and 'frequency-dependent transmission' remain valid and useful (though a 'fuller' transmission term for the former is identified), but that the terms 'mass action', 'true mass action' and 'pseudo mass action' are all unhelpful and should be dropped. Also, contrary to what has often been assumed, the distinction between homogeneous and heterogeneous mixing in a host population is orthogonal to the distinction between density- and frequency-dependent transmission modes.

Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. "Centrality" describes an individual's position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies.

Zika, a mosquito-borne viral disease that emerged in South America in 2015, was declared a Public Health Emergency of International Concern by the WHO in February of 2016. We developed a climate-driven R0 mathematical model for the transmission risk of Zika virus (ZIKV) that explicitly includes two key mosquito vector species: Aedes aegypti and Aedes albopictus. The model was parameterized and calibrated using the most up to date information from the available literature. It was then driven by observed gridded temperature and rainfall datasets for the period 1950–2015. We find that the transmission risk in South America in 2015 was the highest since 1950. This maximum is related to favoring temperature conditions that caused the simulated biting rates to be largest and mosquito mortality rates and extrinsic incubation periods to be smallest in 2015. This event followed the suspected introduction of ZIKV in Brazil in 2013. The ZIKV outbreak in Latin America has very likely been fueled by the 2015–2016 El Niño climate phenomenon affecting the region. The highest transmission risk globally is in South America and tropical countries where Ae. aegypti is abundant. Transmission risk is strongly seasonal in temperate regions where Ae. albopictus is present, with significant risk of ZIKV transmission in the southeastern states of the United States, in southern China, and to a lesser extent, over southern Europe during the boreal summer season.

Humans are exposed to Campylobacter spp. in a range of sources via both food and environmental pathways. For this study, we explored the frequency and distribution of thermophilic Campylobacter spp. in a 10-by 10-km square rural area of Cheshire, United Kingdom. The area contains approximately 70, mainly dairy, farms and is used extensively for outdoor recreational activities. Campylobacter spp. were isolated from a range of environmental samples by use of a systematic sampling grid. Livestock (mainly cattle) and wildlife feces and environmental water and soil samples were cultured, and isolates were presumptively identified by standard techniques. These isolates were further characterized by PCR. Campylobacter jejuni was the most prevalent species in all animal samples, ranging from 11% in samples from nonavian wildlife to 36% in cattle feces, and was isolated from 15% of water samples. Campylobacter coli was commonly found in water (17%) and sheep (21%) samples, but rarely in other samples. Campylobacter lari was recovered from all sample types, with the exception of sheep feces, and was found in moderate numbers in birds (7%) and water (5%). Campylobacter hyointestinalis was only recovered from cattle (7%) and birds (1%). The spatial distribution and determinants of C. jejuni in cattle feces were examined by the use of model-based spatial statistics. The distribution was consistent with very localized within-farm or within-field transmission and showed little evidence of any larger-scale spatial dependence. We concluded that there is a potentially high risk of human exposure to Campylobacter spp., particularly C. jejuni, in the environment of our study area. The prevalence and likely risk posed by C. jejuni-positive cattle feces in the environment diminished as the fecal material aged. After we took into account the age of the fecal material, the absence or presence of rain, and the presence of bird feces, there was evidence of significant variation in the prevalence of C. jejuni-positive cattle feces between grazing fields but no evidence of spatial clustering beyond this resolution. The spatial pattern of C. jejuni is therefore consistent with that for an organism that is ubiquitous in areas contaminated with cattle feces, with a short-scale variation in infection intensity that cannot be explained solely by variations in the age of the fecal material. The observed pattern is not consistent with large-scale transmission attributable to watercourses, wildlife territories, or other geographical features that transcend field and farm boundaries.

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