In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. Despite substantial public health improvements in the last century, infectious diseases remain one of the leading causes of both morbidity and mortality 1,2 . When confronted with an infectious disease outbreak, public health officials typically work to control the outbreak by performing assessments, analyzing surveillance data, identifying resources and interacting with subject mater experts 2-4 . Control measures are then implemented based on the cumulative information collected. These approaches rely heavily on good surveillance systems, access to experts, and good intuition about which control measures to use. As such, they are largely subjective, time consuming, and the infrastructure required is often not present in high disease burden areas. Modeling is an attractive supplemental method because of the ability to estimate an outbreak's trajectory and the effects of possible control measures in a timely manner. Compartmental models are historically common; they divide individuals into categories based on their disease status. The most common variant is the SIR model, named after the categories used-"susceptible", "infectious" and "recovered". Models of this nature have small computational requirements, and are thus commonly used as first pass attempts to characterize outbreaks or infections quickly 5 . For example, after the sudden emergence of Severe Acute Respiratory Syndrome (SARS) in the early 2000s, researchers used modeling to characterize the virus' epidemiology. Several used compartmental models with control measures like quarantine or isolation in various settings (e.g., hospitals or cities) to describe effects of possible interventions 6-9 . Similar work exists for essentially all well known infectious diseases. For example, Mandal et al. provide a review of models used for malaria 10 and Bauch et al. explore model use with respect to SARS and other emerging infectious diseases 6 . Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. In contrast, agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks 11 . This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test 11 . These models further require computational resources inaccessible to an average health department. Both agent-based and compartmental models exhibit additional features that are problematic to wide-spread model adoption. The first is the focus on particular disease-location pairs. This emphasis precludes application to 
 Title: An approach to and web-based tool for infectious disease outbreak intervention analysis OPEN
False

Paragraph: For the purpose of this work, insect biotechnology, which is also known as yellow biotechnology, is the use of insects as well as insect-derived cells or molecules in medical (red biotechnology), agricultural (green biotechnology), and industrial (white) biotechnology. It is based on the application of biotechnological techniques on insects or their cells to develop products or services for human use. Such products are then applied in agriculture, medicine, and industrial biotechnology. Insect biotechnology has proven to be a useful resource in diverse industries, especially for the production of industrial enzymes including chitinases and cellulases, pharmaceuticals, microbial insecticides, insect genes, and many other substances. Insect cells (ICs), and particularly lepidopteran cells, constitute a competitive strategy to mammalian cells for the manufacturing of biotechnology products. Among the wide range of methods and expression hosts available for the production of biotech products, ICs are ideal for the production of complex proteins requiring extensive posttranslational modifi cation. The progress so far made in insect biotechnology essentially derives from scientifi c breakthroughs in molecular biology, especially with the advances in techniques that allow genetic manipulation of organisms and cells. Insect biotechnology has grown tremendously in the last 30 years. 
 Title: Insect Biotechnology
False

Paragraph: The current re-emergence of human monkeypox (HMPX) is a global concern for endemic and non-endemic countries, but healthcare workers in some regions, like Asia, have less experience with identifying and treating HMPX cases. This study aimed to assess the confidence and its predictors in HMPX case management among general practitioners (GPs), the frontline doctors in Indonesia, and to explore their perspectives on HMPX. Between May and July 2019, GPs in Indonesia completed an online-based survey. The questionnaire collected information on GPs' confidence, perspective, sociodemographic, workplace and professional characteristics, exposure to HMPX information and knowledge on HMPX. A logistic regression analysis was employed to explore the explanatory variables influencing the confidence and the perspective. We included 395 GPs in our analysis (77.4% out of 510 responses received) of which 10.1% and 34.9% were classified having good confidence using an 80% and 70% cut-off for confidence score, respectively. In the adjusted analysis, receiving information about HMPX during medical training was the only variable significantly associated with good confidence (adjusted odds ratio 2.74, 95% confidence interval 1.57 to 4.78 and p < 0.001). Approximately 73.6% and 77. 
 Title: Confidence in managing human monkeypox cases in Asia: A cross-sectional survey among general practitioners in Indonesia

True