BackgroundThere is an urgent need to understand how the provision of information influences individual risk perception and how this in turn shapes the evolution of epidemics. Individuals are influenced by information in complex and unpredictable ways. Emerging infectious diseases, such as the recent swine flu epidemic, may be particular hotspots for a media-fueled rush to vaccination; conversely, seasonal diseases may receive little media attention, despite their high mortality rate, due to their perceived lack of newness.MethodsWe formulate a deterministic transmission and vaccination model to investigate the effects of media coverage on the transmission dynamics of influenza. The population is subdivided into different classes according to their disease status. The compartmental model includes the effect of media coverage on reporting the number of infections as well as the number of individuals successfully vaccinated.ResultsA threshold parameter (the basic reproductive ratio) is analytically derived and used to discuss the local stability of the disease-free steady state. The impact of costs that can be incurred, which include vaccination, education, implementation and campaigns on media coverage, are also investigated using optimal control theory. A simplified version of the model with pulse vaccination shows that the media can trigger a vaccinating panic if the vaccine is imperfect and simplified messages result in the vaccinated mixing with the infectives without regard to disease risk.ConclusionsThe effects of media on an outbreak are complex. Simplified understandings of disease epidemiology, propogated through media soundbites, may make the disease significantly worse.

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Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R2 values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0–14 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.

Three-dimensional (3-D) high-throughput crop phenotyping may benefit plant research and breeding programs by providing a rapid, nondestructive method of determining in-season crop growth and development. In this study, a set of three inexpensive, structured, near-infrared laser projectors mounted on a robotic platform were used to generate high-density color point clouds (PCs) in cotton (Gossypium hirsutum L.) in 2016 and 2017. The PCs were calibrated based on destructive leaf area measurements, and the transformed PCs were analyzed to quantify leaf area by canopy height in 1-cm intervals. We conducted weekly scans of 8-m plots throughout the growing season for two cotton cultivars and two irrigation treatments during the 2 yr. The calibrated PCs were used to accurately measure leaf area index and leaf area index by canopy height. The analysis of leaf area distribution throughout the growing season indicated significant cultivar and irrigation effects by canopy height as well as significant interactions between cultivar and irrigation. Based on the results, it is possible to use a 3-D sensor system to distinguish growth habits that are not easily measured using traditional growth measurements. The information obtained from 3-D measurements may provide additional information about several canopy characteristics that are currently difficult to measure, including radiation capture parameters and biomass partitioning.