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Could murder be modeled as an infectious disease?

Could murder be modeled as an infectious disease?


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Background

"When swine flu hit the population it spiked in certain areas and tapered off in neighboring regions, it hits hardest where people have least protection and this pattern is more pronounced (here)"

Apparently incidences of murder spreads in a similar pattern to infectious diseases such as swine flu according to a 26-year study of the city of Newark, New Jersey (summary).

Questions

It would be interesting to hear your opinions, but some interesting questions I would like to ask are:

  1. To what extent could murder and the epidemiology infectious disease patterns resemble each other?

  2. What factors could you suggest may lead to their apparent similarities?

  3. Given that murder could be modeled as an infectious disease could we apply epidemiological interventions to murder?


Disclaimer: I'm an infectious disease modeler, and generally pretty skeptical of "We modeled X like an outbreak!" claims, because many are just an exercise in curve fitting.

Given that, the answer is both "Yes" and "No".

"No": Murder as an act really isn't transmissible, and if its not transmissible, it can't be modeled as an infectious disease.

"Yes": It is probably possible to model some things in the same way we model infectious diseases, because the underlying causes of murder may be somewhat 'transmissible'. Certain behaviors, cascading effects (if one family member is incarcerated it increases the likelihood that other members of that family will be incarcerated) etc. might give crime "disease-like" properties.

That's probably largely driven by underlying social networks and the like, which are also important to infectious diseases. But then you have a problem in reasoning. For example, did murder "spread" along a social network, or did we merely sample a group of people with a shared underlying cause (for example, poverty, membership in gangs, etc.)

It's probably useful to model some of these things, but it's more useful, in my mind, to model them as their own process, rather than trying to shoehorn them into being like a disease.


Model organism

A model organism (often shortened to model) is a non-human species that is extensively studied to understand particular biological phenomena, with the expectation that discoveries made in the model organism will provide insight into the workings of other organisms. [1] Model organisms are widely used to research human disease when human experimentation would be unfeasible or unethical. [2] This strategy is made possible by the common descent of all living organisms, and the conservation of metabolic and developmental pathways and genetic material over the course of evolution. [3] [ page needed ]

Studying model organisms can be informative, but care must be taken when generalizing from one organism to another. [4] [ page needed ]

In researching human disease, model organisms allow for better understanding the disease process without the added risk of harming an actual human. The species chosen will usually meet a determined taxonomic equivalency [ clarification needed ] to humans, so as to react to disease or its treatment in a way that resembles human physiology as needed. Although biological activity in a model organism does not ensure an effect in humans, many drugs, treatments and cures for human diseases are developed in part with the guidance of animal models. [5] [6] There are three main types of disease models: homologous, isomorphic and predictive. Homologous animals have the same causes, symptoms and treatment options as would humans who have the same disease. Isomorphic animals share the same symptoms and treatments. Predictive models are similar to a particular human disease in only a couple of aspects, but are useful in isolating and making predictions about mechanisms of a set of disease features. [7]


The biomedical model

In the biomedical health-care system, the care provider does not have enough time to listen to all of the patient’s concerns and thus offers ineffective care. The system does not address the many psychological risk factors for both morbidity and mortality, and ignores the psychosocial aspects. This leads to unnecessary utilization of medical and surgical services. Further, the current model does not fully tackle issues like “treatment adherence and lifestyle improvement or psychological interventions for acute illness, and management of stressful medical procedures.” (Levant R, 2005)

Success and strength of the biomedical model

The biomedical model has proved to be effective at diagnosing and treating most diseases and has “been associated with huge improvement in medical care.” (Wade D, Halligan P, 2004.) Throughout the course of history, the model has established the reasons for the occurrence of diseases, and has come up with very effective treatment strategies. “The biomedical model is clearly relevant for many disease based illnesses, has intuitive appeal, and is supported by a wealth of supporting biological findings.”(Wade D, Halligan P, 2004.)

Drug-based treatment and surgical procedures have become far more effective as well as safer for the individual.

Numerous lives have been saved through interventions such as treatment of trauma, cancer, and the reduction of mortality from cardiovascular disease. These are definitely significant contributions to society (Thiele D, 2004.)

Weakness and drawbacks of the biomedical model

The biomedical model does not take into account the role of a person’s psychology or society in the cause of disease and its treatment. By ignoring the patient’s psychology, the care provider might see only a ‘patient’ or a ‘case’ and not the real person behind it.

The biomedical model of illness may not be able to fully explain many forms of illness.

This is because of the assumption that all illness has a single underlying cause, which is disease (pathology) and that removal or attenuation of the disease will result in a return to health. However, evidence exists that this assumption is wrong. (Wade D, Halligan P, 2004).

This assumption “has led to medicalisation of commonly experienced anomalous sensations and often disbelief of patients who present with illness without any demonstrable disease process.” (Wade D, Halligan P, 2004). In other words, it can be said that the biomedical model does not explain functional somatic syndromes and illness without evident disease.

Although the biomedical model is effective in the diagnosis of a disease, and in developing treatment strategies and surgical procedures, the limitations of the medical model cannot be denied.

The model does not effectively incorporate psychological, psychosocial, or spiritual factors. In order to be a truly useful model it should be radically changed so that it incorporates the above factors. There is a need to transform our biomedical health-care system to one based on the biopsychosocial model, which recognises psychological and social factors.

REFERENCES

Levant R. (2005). Health care for the whole person . Retrieved November 3, from http://www.apa.org/monitor/may05/pc.html

Thiele D. (2004). Impact of the biomedical model of disease for aboriginal people. Retrieved November 4, from http://www.latrobe.edu.au/aipc/HTML%20abstracts/Thiele,%20D-270.html

Wade D, Halligan P. (2004). Do biomedical models of illness make for good healthcare systems? BMJ 329:1398-1401.


Statistical Model Could Predict Future Disease Outbreaks

John Drake in the Ecology Auditorium at UGA CREDIT: UGA

Several University of Georgia researchers teamed up to create a statistical method that may allow public health and infectious disease forecasters to better predict disease reemergence, especially for preventable childhood infections such as measles and pertussis.

As described in the journal PLOS Computational Biology, their five-year project resulted in a model that shows how subtle changes in the stream of reported cases of a disease may be predictive of both an approaching epidemic and of the final success of a disease eradication campaign.

"We hope that in the near future, we will be available to monitor and track warning signals for emerging diseases identified by this model," said John Drake, Distinguished Research Professor of Ecology and director for the Center for the Ecology of Infectious Diseases who researches the dynamics of biological epidemics. His current projects include studies of Ebola virus in West Africa and Middle East respiratory syndrome-related coronavirus in the horn of Africa.

In recent years, the re-emergence of measles, mumps, polio, whooping cough, and other vaccine-preventable diseases has sparked a refocus on emergency preparedness.

"Research has been done in ecology and climate science about tipping points in climate change," he said. "We realized this is mathematically similar to disease dynamics."

Drake and colleagues focused on "critical slowing down," or the loss of stability that occurs in a system as a tipping point is reached. This slowing down can result from pathogen evolution, changes in contact rates of infected individuals, and declines in vaccination. All these changes may affect the spread of a disease, but they often take place gradually and without much consequence until a tipping point is crossed.

Most data analysis methods are designed to characterize disease spread after the tipping point has already been crossed.

"We saw a need to improve the ways of measuring how well-controlled a disease is, which can be difficult to do in a very complex system, especially when we observe a small fraction of the true number of cases that occur," said Eamon O'Dea, a postdoctoral researcher in Drake's laboratory who focuses on disease ecology.

The research team found that their predictions were consistent with well-known findings of British epidemiologists Roy Anderson and Robert May, who compared the duration of epidemic cycles in measles, rubella, mumps, smallpox, chickenpox, scarlet fever, diphtheria and pertussis from the 1880s to 1980s. For instance, Anderson and May found that measles in England and Wales slowed down after extensive immunization in 1968. Similarly, the model shows that infectious diseases slow as an immunization threshold is approached. Slight variations in infection levels could be useful early warning signals for disease reemergence that results from a decline in vaccine uptake, they wrote.

"Our goal is to validate this on smaller scales so states and cities can potentially predict disease, which is practical in terms of how to make decisions about vaccines," O'Dea said. "This could be particularly useful in countries where measles is still a high cause of mortality."

To illustrate how the infectious disease model behaves, the team created a visualization that looks like a series of bowls with balls rolling in them. In the model, vaccine coverage affects the shallowness of the bowl and the speed of the ball rolling in it.

"Very often, the conceptual side of science is not emphasized as much as it should be, and we were pleased to find the right visuals to help others understand the science," said Eric Marty, an ecology researcher who specializes in data visualization.

As part of Project AERO, which stands for Anticipating Emerging and Re-emerging Outbreaks, Drake and colleagues are creating interactive tools based on critical slowing down for researchers and policymakers to use in the field and guide decisions. For instance, the team is developing an interactive dashboard that will help non-scientists plot and analyze data to understand the current trends for a certain infectious disease. They're presenting a prototype to fellow researchers now and anticipating a public release within the next year.

"If a computer model of a particular disease was sufficiently detailed and accurate, it would be possible to predict the course of an outbreak using simulation," Marty said. "But if you don't have a good model, as is often the case, then the statistics of critical slowing down might still give us early warning of an outbreak."


Future Research Directions

Research using agent-based modeling to study chronic diseases is still in its infancy. We provided 3 possible reasons for a low adoption rate of agent-based modeling in the study of chronic health conditions and their consequences. First, chronic diseases are not characterized by clear &ldquotransmission&rdquo mechanisms thus, many researchers are reluctant to use agent-based modeling to study chronic diseases because of the general perception that agent-based modeling is only suitable to model health conditions that can be transmitted from person to person. Second, it is generally more difficult to implement agent-based modeling than more widely used simulation approaches such as Markov-based state-transition models. In most cases, developing an agent-based model requires some training in computer programming, whereas constructing Markov-based models can be done using spreadsheet software (eg, Microsoft Excel) or specialized, easy-to-use software such as TreeAge Pro (TreeAge Software, Inc). Finally, the development of agent-based models generally requires a large amount of individual-level data for parameterization, calibration, and validation such data are not always available to researchers. Despite these barriers, we believe that policymakers and health care providers would benefit from having access to high-quality, well-designed agent-based models that can help them better understand the development and consequences of chronic diseases and thereby improve their decision-making with regard to intervention design and resource allocation.

Disease-specific future research directions

Diabetes. To the best of our knowledge, agent-based modeling has only been applied to the study of diabetic retinopathy (22,23). However, we believe that it can also be useful to study the progression of other diabetic complications &mdash nephropathy, neuropathy, myocardial infarction, and stroke. In addition, future agent-based models should incorporate health behaviors, such as diet, physical activity, and smoking, and examine the impact of modifying behaviors on the prevention and management of diabetes. Finally, agent-based modeling should take into account the impact of comorbidities (eg, obesity, hypertension, hyperlipidemia) and pharmacologic interventions on the health outcomes of a person with diabetes.

Cardiovascular disease. Although the model in the study by Li et al demonstrated the possibility of using an agent-based model to study cardiovascular disease, the model has some limitations related to its design and structure (27). For example, a person is either of normal weight or overweight in the model, and detailed changes in BMI are not modeled. We believe that an agent-based model of cardiovascular disease with more detailed disease progression and validated model prediction will provide potential users with more precise insights and more confidence in using the results to inform decision-making. In addition, we suggest incorporating social influence in future modeling when studying the impact of lifestyle interventions on cardiovascular disease. Finally, future agent-based models of cardiovascular disease could take into account the effects of different treatment strategies, drug therapies, and procedures (eg, revascularization, pacemaker implantation) to improve their clinical relevance.

Obesity. Most agent-based models of obesity focused on the impact of social influences (peer effects) on the dynamics of obesity (33,35). However, social influences may not be the only factors or the most important factors associated with obesity. We suggest incorporating health behaviors, such as physical activity and diet, in future agent-based models of obesity. Moreover, agent-based models of obesity could be more useful if they took into account evidence from biology, behavioral science, and psychology to better understand the development and progression of obesity.

Multimorbidity. Although multimorbidity has become the most common chronic condition among the elderly population (age 65 or older) in the United States (37), credible agent-based models studying the development and consequences of multimorbidity are lacking. Thus, modelers and interested public health and medical researchers should strive to develop comprehensive agent-based models of multimorbidity in which both the characteristics of individual chronic conditions as well as the possible interactions across these health conditions are explicitly captured.

Purpose-specific future research directions

Risk assessment. Risk assessment for chronic disease is an essential component of population health management. Current risk assessment tools rely on standard statistical models (eg, regression) to identify correlations in somewhat limited administrative data sets. Even more advanced statistical methods, such as structural equation modeling and latent class analysis, are unable to capture the common nonlinearity, interdependency, and dynamics of risk factors and disease outcomes among the individuals that make up a population. Thus, a promising future research direction is to use agent-based models to assess the risk of chronic disease and disease-specific mortality. Agent-based models capture the development of chronic disease as an emergent outcome of a set of factors, including health beliefs, social norms, lifestyle behaviors, medication compliance, and biomarkers, that often change stochastically, dynamically, and interactively. As demonstrated in Li et al, an agent-based model of cardiovascular disease can be used to assess the risk of a population of interest and, potentially, can become an essential part of population health management (28).

Cost-effectiveness analysis. Most model-based cost-effectiveness analyses are based on Markov models. However, Markov models have been criticized for having many limitations and inherent assumptions that may render the results misleading (38). Examples of limitations for Markov models are its inability to model heterogeneous populations (ie, with a set of population characteristics) or to account for dependence on prior states of the system. A few studies have demonstrated that agent-based modeling can overcome some limitations of Markov models and provide decision-makers with more flexibility in studying the cost-effectiveness of a certain intervention to prevent chronic diseases (39,40). However, researchers have not fully taken advantage of the modeling power of agent-based models &mdash such as capturing population interactions and integrating individual-level data &mdash to improve the accuracy and credibility of cost-effectiveness analysis.

Although agent-based modeling is a powerful approach to studying chronic health conditions, it remains an underused tool among researchers in medicine and public health who are interested in chronic disease prevention and management. We provide examples of agent-based modeling applications in the areas of diabetes, cardiovascular disease, and obesity. The broader use of agent-based modeling has the potential to provide new insights in the areas of population health management, medical decision-making, and health care policy formulation and assessment.


Concluding remarks

The study of intracellular pathogens in zebrafish macrophages has led to new mechanistic insights that are inspiring novel host-directed therapeutic strategies (Table 1). The real-time imaging possibilities in zebrafish will also be very useful for elucidating the mechanisms underlying macrophage migration processes, as has already been demonstrated by the study of neutrophils in the larval system (Sarris et al., 2012 Henry et al., 2013 Shelef et al., 2013). A question that is very relevant both for infectious diseases and for cancer biology concerns the presence of different pro- and anti-inflammatory macrophage subtypes in zebrafish. Classically activated (M1) and alternatively activated (M2) macrophages, resembling the phenotypes of mammalian macrophages, have been identified in different fish species (Forlenza et al., 2011). That different macrophage subtypes might already be present in early zebrafish larvae has been suggested, but this remains to be further investigated (Feng et al., 2010). The early larval stages, which are optimally suited for imaging and for genetic and pharmacological interventions, can give much information on the intracellular survival mechanisms of pathogens, as demonstrated by the studies discussed herein. The early larval stages are also very useful for studying the response of microglia to brain injuries or infection, contributing to a deeper understanding of the role of these specialized macrophages in neurodegenerative diseases (Sieger et al., 2012 Sieger and Peri, 2013). Studying the antigen-presentation function of macrophages and DCs at later developmental stages is becoming increasingly feasible owing to advances in technologies for generating stable mutant lines (Clark et al., 2011 Blackburn et al., 2013 Kettleborough et al., 2013). Dynamic interactions between macrophages and neutrophils that are emerging from recent studies in zebrafish are of considerable interest for further study (Ellett et al., 2011 Yang et al., 2012 Elks et al., 2013). The use of the zebrafish model has already provided insights into the in vivo relevance of intracellular defense mechanisms such as ROS and RNS production and autophagy. We expect that further use of this powerful model will continue to make important contributions towards the understanding of innate immunity and of the virulence strategies that pathogens use to subvert innate host defenses.


Functions of a Hormone

Hormones in Animals

The neuroendocrine system is a complex arrangement of cells in animals which can pass messages via hormones. From the time of conception to the time of death, different hormones will affect the body and alter its development and course. These chemical signals operate on a variety of levels in animals.

A hormonal response in animals starts with input to the sensory systems. Light, touch, smell, taste and other physical inputs are processed by the central nervous system. The brain decides what to make from these inputs based on genetics and past experiences. In response to the inputs, the brain sends a signal to the hypothalamus, the central processing center for hormonal instructions. For instructions which need to be delivered quickly, the hypothalamus contacts the posterior pituitary gland through nerve connections. These impulses signal the posterior pituitary to quickly release a hormone. Arteries within the gland carry the released hormones directly through the blood to the tissues they are meant for.

While there are hundreds of reactions caused by a hormonal cascade, there are only a few different actions caused by hormones at a biochemical level. Many hormones bind to a surface proteins, which extends through the cell membrane. The protein then changes shape, causing a conformational change on the inside of the cell as well. This change can activate a second messenger, which carries the message to another point within the cell. Other hormones pass through the cell membrane and activate a process in the cytosol or travel all the way to the nuclear envelope to deliver a message about the rate of transcription.

There are hundreds of different signals that can be enacted by the hormone system. Different hormones activate different systems. Steroid hormones, for instance, activate gene transcription and regulate the enzymes created from genes. Vitamin D, a hormone and vitamin, regulates calcium in the blood and bones. Other signals can activate enzymes already present within the target cell, quickly turning on a metabolic process. Neurotransmitters are a special form of hormones, which travel only short distances between neurons. Animals also have special hormones called pheromones which they release into the environment to stimulate behaviors in other animals. These hormones can be sexual, territorial, or instructional.

Hormones in Plants

Much like in animals, plants have many different hormones which control their life cycles and development. There several groups of plant hormone, including the auxins, gibberellins and ethylene, among others. Plant hormones have been studied for a long time, as a means of modifying and manipulating plant growth. Some plant hormones have been developed artificially, for use on commercial crops. For instance, tomatoes are often ripened through the release of the plant hormone ethylene. This insures that all the tomatoes are ready for picking at the same time. This allows commercial machines to quickly and easily process entire fields at the same time. Auxin and related hormones are used to promote rooting and develop tissue cultures. This hormone also inhibits the growth of many adult plants and can act as a weed killer.

Plants also have hormones which come from a variety of starting molecules. In fact, a hormone from the brassinolides family resembles animal steroid hormones such as testosterone. Others, like hormones from the gibberellin family, have hundreds of different forms and don’t clearly resemble a known animal hormone. Synthetic versions of many of these hormones have been created in the lab, so their effects and composition could be more easily studied.

Through this process, it was found that plants have developed many pathways that use a particular hormone as a signal between plants. Plants being attacked by grasshoppers might release a hormone which signals neighboring plants to ready their defenses. This is very similar to how animals communicate with pheromones.


Intended audience and goal of the package

The audience for the DSAIDE package are individuals interested in understanding infectious disease spread and control on the population level from a dynamical systems and modeling perspective. The package was originally built to complement a course on infectious disease epidemiology from a dynamical systems perspective. However, the documentation contained within DSAIDE strives to be detailed and self-contained enough to allow a motivated student to use DSAIDE and learn the topics covered by the package on their own. Any knowledge gaps can be filled by reading the provided references. For more advanced students who are comfortable with some level of coding, the package can be used as described in “Level 2” and “Level 3” below, either on its own or as a complement to a course on infectious disease modeling.


3. Evolution of new intervention products and sequence of study phases

Many intervention products, and especially drugs and vaccines, are likely to originate from basic research in laboratories. Such products must go through a long series of tests, before they can be considered for use in the kinds of field trials that are the focus of this book. Before any human use, a new product will be tested in the laboratory for its activity and toxicity in various in vitro and animal test systems. If it successfully passes through these stages, studies of safety, toxicity, and activity may be conducted in a small number of human volunteers, with careful clinical monitoring. A series of further studies, each including increasing numbers of subjects, must be carried out before a new product can be introduced for widespread use. Trials in humans usually go through a series of sequential ‘phases’ of progressively increasing size to establish first the safety and mode of action and then, in later phases, the efficacy against the target disease(s) and safety in a larger number of subjects.

3.1. Clinical studies: Phases I to IV

Phase I studies are exploratory first-in-human trials and may involve the administration of small, then larger, doses of the study product to a small number of healthy human subjects (ten to 50) to gather preliminary data on the product’s pharmacokinetics (where the product and its metabolites go within the body and in what concentrations) and pharmacodynamics (what the drug does in the body). These studies can help to establish the dosage and frequency that are safe and necessary to have an effect. These trials are designed to make an initial assessment of the safety and tolerability of the drug or vaccine in a small number of, usually healthy, volunteers.

Phase II trials are conducted for products that have shown no significant safety problems in Phase I trials. They involve progressively larger numbers of participants (for example, initially tens of subjects, but later studies may involve 100s) and are designed to assess how well the intervention works (therapeutic drugs would involve studies in patients, whereas vaccines would be assessed for immunogenicity in healthy volunteers), as well as to check for safety in a larger number of healthy volunteers (vaccines) or in patients (therapeutic drugs). Phase II trials may also be designed to evaluate what doses and the number of doses of the intervention should be given, and what the intervals should be between doses. Usually, a product will be evaluated in a number of different Phase II trials, evaluating its performance under different circumstances, for example, a malaria vaccine might be initially trialled in adults but then tested in progressively younger groups until tested in the final target population of infants.

Phase III trials aim to provide a definitive assessment of the efficacy of the intervention against the primary outcome(s) of interest. They also provide safety data in a larger group of subjects. These trials usually involve large numbers of individuals (e.g. 1000� or more) and are studies that are conducted to produce the evidence of efficacy and safety required to submit a product to a licensing authority. For this reason, they are sometimes called ‘pivotal’ trials.

Phase IV studies are conducted after the intervention has been shown to be efficacious in Phase III trials and are conducted to assess the safety and effectiveness of an intervention when used under routine health service conditions, or close to these conditions (rather than in the special circumstances of a controlled trial). Where they involve a regulated product, such as a drug or vaccine, they are usually post-registration or post-licensure studies. Safety issues that are important, but which arise in a relatively small proportion of individuals, may only become apparent through Phase IV studies, once there is widespread use of an intervention. Phase IV studies sometimes take the form of randomized trials where the safety and effectiveness are assessed by comparing the results of administering the product to some individuals or communities, but not to others (allocated at random). However, such trials may be difficult to conduct, once a product has been licensed by the national regulatory authority, and then non-randomized assessments must be made, such as through �ore versus after studies’ or case-control investigations. Many trials of strategies of how best to use drugs or vaccines can also be considered as Phase IV studies, such as a comparison of intermittent preventive therapy (IPT) using anti-malarial drugs given to all young children, compared to teaching their mothers to recognize and treat their children if they have possible falciparum malaria.

The main focus of the book will be on large-scale Phase III trials conducted ‘in the field’ (i.e. outside clinical facilities), but there is also a specific chapter on Phase IV studies (see Chapter 22).

Although similar terms are often used for the ‘phase’ of trials conducted to test the effectiveness or efficacy of interventions that do not use an investigational product, such as behaviour change interventions or incentives, these have much less well-defined, or universally agreed, phases, and it is not uncommon for the first RCT of such an intervention to be the equivalent of a Phase III trial of a drug or vaccine.

3.2. Registration of new interventions

Legal registration procedures are mandated in most countries before a drug or vaccine can be put into general use, and these procedures normally require documentation of the safety and efficacy of the intervention, based on RCTs involving many hundreds of subjects. Further guidance on the rules and regulations for assessing the safety and efficacy of products for use in human beings can be found at the website of the US Food and Drug Administration (<http://www.fda.gov>).

3.3. ‘Proof of principle’ trials

The purposes of field trials may change as experience with an intervention accumulates. Sometimes, particularly in early trials of a new intervention, the purpose of the study is analytic to demonstrate an effect or to establish a principle, with little consideration as to whether the intervention is practicable at the population level for disease control. An example might be the use of a malaria vaccine that must be administered monthly to be effective. Such studies are sometimes called 𠆎xplanatory’ or ‘proof of principle’ trials (Schwartz and Lellouch, 1967). Once an effect against the disease under study has been demonstrated, there might then be greater impetus to develop new formulations of the intervention or different schedules that would be more practicable for application in a disease control programme. Subsequent, and generally larger, trials are conducted, in which the purpose is to establish the benefit of an intervention applied under the circumstances of general use. These studies are often called ‘pragmatic’ trials (Schwartz and Lellouch, 1967).

3.4. Trials of intervention delivery strategies

Although new products developed through basic science research may serve as the impetus for field trials, some interventions or intervention strategies are developed directly as a result of field studies and experience such as a vaccine strategy for smallpox eradication and the use of tsetse fly traps for the control of trypanosomiasis transmission. Thus, trials may be needed not only of the product itself, but also of the way that product is used or delivered. Trials like these would involve intervention ‘packages’ which might include, for example, the same drug or vaccine, but provided with different educational approaches or delivery methods. Sometimes, an intervention that has been shown to be effective must be added into an ongoing disease control programme that involves other kinds of interventions. For example, it is expected that, when effective malaria vaccines become available, they will be added to other malaria control methods, based on a combination of vector control, case finding, and treatment strategies. Further studies of how best to integrate these interventions into an overall strategy will have to be worked out. In addition, policy and planning decisions about disease control will have to be guided by appropriate cost-effectiveness analyses.


Could murder be modeled as an infectious disease? - Biology

NIAID conducts and supports clinical trials evaluating therapies and vaccine candidates against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19.

The NIAID Strategic Plan for COVID-19 Research details the institute’s priorities for controlling and ultimately ending the spread of the novel coronavirus (SARS-CoV-2) and the disease it causes (COVID-19).

NIAID's cloud-based, secure data platform enables sharing of anonymous patient-level clinical data to help generate new knowledge to treat and prevent infectious diseases such as COVID-19.

NIAID’s research program to develop safe and effective antivirals to combat SARS-CoV-2 will also build sustainable platforms for targeted drug discovery and development of antivirals against viruses with pandemic potential.


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