Deterministic and Stochastic Optimal Control

A stochastic SIRS epidemic model with infectious force under intervention strategies

We present a mathematical model for the transmission of COVID-19 by the Caputo fractional-order derivative. We calculate the equilibrium points and the reproduction number for the model and obtain the region of the feasibility of system. By fixed point theory, we prove the existence of a unique solution. Using the generalized Adams-Bashforth-Moulton method, we solve the system and obtain the approximate solutions. We present a numerical simulation for the transmission of COVID-19 in the world, and in this simulation, the reproduction number is obtained as R 0 = 1 : 610007996 , which shows that the epidemic continues.

A mathematical model for COVID-19 transmission by using the Caputo fractional derivative

Coevolution spreading in complex networks.

1. References to solidarity are currently on the increase in public discourse. In the UK and beyond, during economic crises and in a political climate where many feel that mutual assistance has lost currency, calls for a new and forceful emphasis on the meaning of solidarity are increasingly heard. Such appeals to solidarity are inevitably linked to ideas about how societies function, and about how and where the boundary between individual, familial, communal and societal spheres of responsibilities should be drawn.

Solidarity: Reflections on an emerging concept in bioethics

There 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. We 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. A 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. The 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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The impact of media coverage on the transmission dynamics of human influenza.

In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R2 ~ 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R2 of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management.

Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data

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.

Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework

https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.20412

Nothabo Dube

Papers

The impact of media coverage on the transmission dynamics of human influenza.

Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data

Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework

Cotton boll distribution and yield estimation using three-dimensional point cloud data

In Situ Cotton Leaf Area Index by Height Using Three-Dimensional Point Clouds