Women make essential contributions to agricultural sectors worldwide through livestock rearing, production of animal-based products, and promotion of animal health. Approximately 752 million of the world's poor have livestock for food, income, work, and/or societal status, and women comprise two-thirds of this population. The Food and Agriculture Organization for the United Nations, the World Bank, the United States Agency for International Development, and other international organizations widely recognize the value of women in agriculture.
Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may be infectious between 1-3 days prior to symptom onset. People may delay seeking medical care after symptom development due to multiple determinants of health seeking behavior like availability of testing, accessibility of providers, and ability to pay. Therefore, understanding symptoms in the general public is important to better predict and inform resource management plans and engage in reopening. As the influenza season looms, the ability to differentiate between clinical presentation of COVID-19 and seasonal influenza will also be important to health providers and public health response efforts. This project has developed an algorithm that when used with captured syndromic trends can help provide both differentiation to various influenza-like illnesses (ILI) as well as provide public health decision makers a better understanding regarding spatial and temporal trends. This effort has also developed a web-based tool to allow for the capturing of generalized syndromic trends and provide both spatial and temporal outputs on these trends. This page left blank
In this report, we evaluate a novel method for modeling the spread of COVID-19 pandemic. In this new approach we leverage methods and algorithms developed for fully-kinetic plasma physics simulations using Particle-In-Cell (PIC) Direct Simulation Monte-Carlo (DSMC) models. This approach then leverages Sandia-unique simulation capabilities, and High-Performance Computer (HPC) resources and expertise in particle-particle interactions using stochastic processes. Our hypothesis is that this approach would provide a more efficient platform with assumptions based on physical data that would then enable the user to assess the impact of mitigation strategies and forecast different phases of infection. This work addresses key scientific questions related to the assumptions this new approach must make to model the interactions of people using algorithms typically used for modeling particle interactions in physics codes (kinetic plasma, gas dynamics). The model developed uses rational/physical inputs while also providing critical insight; the results could serve as inputs to, or alternatives for, existing models. The model work presented was developed over a four-week time frame, thus far showing promising results and many ways in which this model/approach could be improved. This work is aimed at providing a proof-of-concept for this new pandemic modeling approach, which could have an immediate impact on the COVID-19 pandemic modeling, while laying a basis to model future pandemic scenarios in a manner that is timely and efficient. Additionally, this new approach provides new visualization tools to help epidemiologists comprehend and articulate the spread of this and other pandemics as well as a more general tool to determine key parameters needed in order to better predict pandemic modeling in the future. In the report we describe our model for pandemic modeling, apply this model to COVID-19 data for New York City (NYC), assess model sensitivities to different inputs and parameters and , finally, propagate the model forward under different conditions to assess the effects of mitigation and associated timing. Finally, our approach will help understand the role of asymptomatic cases, and could be extended to elucidate the role of recovered individuals in the second round of the infection, which is currently being ignored.
Here, with recent outbreaks of MERS-Cov, Anthrax, Nipah, and Highly Pathogenic Avian Influenza, much emphasis has been placed on rapid identification of infectious agents globally. As a result, laboratories are building capacity, conducting more advanced and sophisticated research, increasing laboratory staff, and establishing collections of dangerous pathogens in an attempt to reduce the impact of infectious disease outbreaks and characterize disease causing agents. With this expansion, the global laboratory community has started to focus on laboratory biosafety and biosecurity to prevent the accidental and/or intent ional release o f these agents. Laboratory biosafety and biosecurity systems are used around the world to help mit igate the risks posed by dangerous pathogens in the laboratory. Veterinary laboratories carry unique responsibilities to workers and communities to safely and securely handle disease causing microorganisms. Many microorganisms studied in veterinary laboratories not only infect animals, but also have the potential to infect humans. This paper will discuss the fundamentals of laboratory biosafety and biosecurity.
Despite significant progress in development of bioanalytical devices cost, complexity, access to reagents and lack of infrastructure have prevented use of these technologies in resource-limited regions. To provide a sustainable tool in the global effort to combat infectious diseases the diagnostic device must be low cost, simple to operate and read, robust, and have sensitivity and specificity comparable to laboratory analysis. In this mini-review we describe recent work using laser machined plastic laminates to produce diagnostic devices that are capable of a wide variety of bioanalytical measurements and show great promise towards future use in low-resource environments.