This chapter explains the distinctions between zoonotic and vector-borne illnesses and other infectious diseases, as well as how a disease is transmitted. It describes how zoonotic and vector-borne disease control strategies are comparable to those used for other types of disease and justifies the reasons for and ways in which zoonotic and vector-borne diseases depend on eco-epidemiologic techniques. The chapter also discusses how to prevent the spread of vector-borne and zoonotic diseases. Understanding and preventing zoonotic and vector-borne disease requires a holistic view that improves health by considering the interactions of humans, animals, and environment through a collaborative and multidisciplinary approach. Science and epidemiology work to estimate where those interactions are likely to occur and to quickly identify them once they do. The One Health approach leads to a more robust understanding of the interactions that may lead to disease transmission and opens the ingenuity of potential solutions.
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Respiratory diseases can take on multiple modes of transmission, including contact, droplet, and airborne. Key methods for preventing respiratory transmission in community and clinical settings may include hand hygiene, prompt isolation precautions, and vaccination. Importantly, in hospital settings, administrative and engineering controls, in addition to individual measures such as use of personal protective equipment, can minimize transmission. Contact patterns allow epidemiologists to investigate transmission of respiratory diseases through implementation of contact investigations. The randomized controlled trial is a powerful study design that can be used to conduct experiments in respiratory and other diseases and to understand whether new interventions or approaches are more effective than others. This chapter discusses the different types of respiratory transmission. It provides a list of preventive measures to stop respiratory transmission, as well as information on how randomized controlled trials can be used to study diseases.
Globally, food-borne disease is the cause of about 600 million cases, resulting in 420,000 deaths, approximately 30% of which occur among children under 5 years old. It is preventable by simple things like handwashing, improved sanitation, access to clean water, and, when available, vaccination. The challenge is that those important public health measures have been known since the 1850s, yet 3.6 billion (nearly 50%) global citizens still do not have access to basic sanitation and two billion do not have safe water in their homes. However, increased testing and data sharing will help to quickly and efficiently find areas for improvement to aid in controlling and preventing food-borne disease. This chapter describes the effect of food-borne disease on health. It discusses the unique epidemiologic approaches to assess a food-borne exposure, describes the proven preventive measures to prevent food-borne disease, and explains the effect of climate change on food security.
Infectious diseases have their own unique modes of transmission and natural histories. These disease progressions also mark important points for potential public health intervention. The spectrum of disease allows us to quantify several outcomes as a disease progresses, as well as how many individuals in a population may have these outcomes. Infectious diseases are heterogeneous, with recent evidence pointing to a major role in the etiology of chronic diseases. This chapter aids readers in making the distinction between predominant modes of transmission. It helps them understand the chain of infection, the implications for public health control strategies, and the natural history of disease. Additionally, it explains how infectious causes of cancer and other diseases blur the chronic/infectious divide and how to determine whether an exposure is linked to a particular outcome.
Environmental factors influence the incidence, seasonality, and distribution of infectious diseases. While the association may be stronger for some diseases compared with others, because of this environmental influence infectious diseases are particularly vulnerable to the effects of a changing climate. Frameworks like the Centers for Disease Control and Prevention’s Building Resilience Against Climate Effects framework guide the development of adaptation strategies which will hopefully help to ameliorate the health effects of climate change. This chapter lists how environmental factors predict continuing disease incidence and transmission. It describes how infectious diseases have changed in a changing climate. The chapter explains the concept of One Health and how it interfaces with infectious disease epidemiology. One Health’s transdisciplinary approach supports the understanding of the complex interactions between the environment, humans, and animals, and facilitates innovative and sustainable solutions to improving health.
Epidemiology has many tools to analyze infectious disease transmission dynamics. More sophisticated modeling efforts like estimating the basic reproduction number (R0) and using compartmental susceptible-infected-recovered models further our understanding and allow for the comparison of interventions and diseases across time and place. An immense amount of work goes into finding, cleaning, and compiling the data necessary to graph or parameterize these models. This is achieved by hard working, interdisciplinary teams often involving epidemiologists working with mathematical modelers and ecologists on the estimation side, virologists, immunologists and entomologists for laboratory experiments, and health care workers and public health for the disease data and interventions. Together, these are powerful tools that help us to better understand disease transmission dynamics and, thereby, open avenues for disease control. This chapter provides information about reproductive number and different types of epidemics, and describes how diseases are transmitted and how transmission mechanisms can be quantified.
Persistent infectious disease public health threats mandate clear and standardized outbreak response and surveillance systems. Epidemiologic expertise is needed from the design of data systems, to using a variety of types of health information and data sources, to analyzing, interpreting, and communicating complex data. Responses also require coordinated multidisciplinary efforts with all levels of public health working together to protect the population’s health. The single word ‘surveillance’ encompasses a variety of ways in which surveillance data are collected and collated. Evaluating surveillance systems requires both qualitative and quantitative approaches. This chapter describes the steps responsible for an outbreak, practices that have contributed to the outbreak, and how to monitor and track disease outbreaks over time. It explains how disease surveillance and reporting are conducted. The chapter also describes the mechanisms to monitor and evaluate surveillance systems and discusses the differences between sensitivity, specificity, and positive predictive value.
Infectious disease epidemiologists face numerous challenges in their work. Beyond the heterogeneity of organisms and conditions, identification of appropriate frameworks and measures for studying diseases is very important. How does one study and make predictions regarding an asymptomatic infection? How does one decide on the target population of interest for a study? How does one handle new infectious disease outbreaks and agents and communicate findings with policymakers and the public? To successfully do this, epidemiologists engage with multiple disciplines from colleagues in basic science laboratories to government officials working to implement public health policy. This chapter examines the work of epidemiologists, assesses how to distinguish between the disease caused by an infectious agent and that not affected by it, and considers infectious disease trends and reasons for re-emergence. It illustrates major infectious disease classification systems, and examines the characteristics of the host, agent, and environment that might enable disease transmission.
Immunization and sanitation are the greatest, most efficient, and cost-effective public health achievements. Each generation brings new susceptible individuals who need to be protected and, even among those fully vaccinated, immunity wanes with age and previously protected individuals may become susceptible as the pathogen evolves or as our immune system changes. Vaccination is an active prevention strategy at the community or population level, it is ongoing and must be maintained. The goals of vaccination are twofold: to protect the individual from the disease and its devastating side effects, and to reduce the spread of disease, thereby protecting the community. The benefits of vaccination go beyond illnesses or deaths averted to include education and economic attainment through wellness. This chapter discusses how vaccines work, including how they stimulate the immune system. It also discusses the epidemiology of vaccination including the effect of the effective reproductive number and herd immunity.
In the age of technology, numerous data sources beyond traditional medical/health data have been harnessed for disease control. The data sources available for epidemiologic surveillance range from survey apps and news websites, to search data, to wearable sensors and social media platforms. One of the potential advantages of digital data sources is early disease detection. There is growing potential to use machine learning and big data to forecast disease spread, and personalized medical and public health approaches to customize care and disease response. Applications of these technologies include improved data visualization and communication but these must be weighed with the protection of individual rights and challenges of combining data sets created for different purposes. This chapter enumerates the 21st century disease control advances and explains how social media, big data, and search tools are used to improve disease control. It demonstrates the role of the P-value and confidence intervals in data analysis.