**1. Introduction**

For an organization to become more resilient, top management needs to take heed of the fact that cyber attacks are likely to intensify in the years ahead and because of this, cyber security needs to be placed in a strategic cyber security management context [1]. The need for such an approach is clear, bearing in mind that: "Even with U.S. company losses due to cyberattacks nearing a reported \$1 trillion by late 2020, a survey of nearly 1000 organizations found that only 44% had cyber preparedness and incident response plans in place" [2] (p. 2). It seems logical, therefore, for managers to counteract cyber attacks by utilizing cyber security technology more fully, but also for them to discover new ways to engage in cyber security management. A key role of senior management is to help managers draw on operand and operant resources so that they can strengthen the organization's defenses against cyber attacks.

Advice relating to the appropriateness of cyber security technology comes in the form of government advice, highly specialized companies that operate cyber security technological solutions, consultants that have in-depth knowledge of cyber security problems and working practices, and university research teams that develop specific types of security software. There are, of course, other sources of intelligence that originate from government

**Citation:** Trim, P.R.J.; Lee, Y.-I. Combining Sociocultural Intelligence with Artificial Intelligence to Increase Organizational Cyber Security Provision through Enhanced Resilience. *Big Data Cogn. Comput.* **2022**, *6*, 110. https://doi.org/ 10.3390/bdcc6040110

Academic Editor: Fabrizio Baiardi

Received: 26 August 2022 Accepted: 1 October 2022 Published: 8 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

agencies and specialist consultancies, for example. Taking this into account, it can be suggested that managers need to adopt a pro-active approach to cyber security as resilience requires that intelligence-gathering involves the deployment of technology that has the power of human cognition and the ability to learn/reason and hear/see [3] (p. 109). An important point that surfaces, however, is that to be effective, organizational resilience needs to be placed within the context of how organizational staff coordinate investment in cyber security across the supply chain [4] (p. 169). Bearing this in mind, it is pertinent to suggest that cyber security management is to be viewed as a strategic-level capability [5], whereby security is linked with business continuity management and a set of procedures whereby security is placed within a crisis/disaster management setting. The case can be made, therefore, for a cyber security manager to be appointed to take charge of cyber security, which is at the heart of an organization's security [1].

 The cyber security manager is, therefore, required to have an appreciation of human psychology and possess adequate knowledge of how cyber security policy is formulated and implemented, if they are to provide guidance and advice to a range of functional heads. If a data breach does occur and results in reputational damage and an increase in adverse publicity resulting from a fine imposed by regulators, then cascading effects may have a debilitating effect on the organization and its trading partners. It is for this reason that the cyber security manager needs to have both technical and managerial knowledge relating to cyber security or have expertise available to them that can be drawn on when necessary.

The remit of the cyber security manager is to work with other senior managers and devise, manage, and implement cyber security policy decisions across the organization's networks. The focus of the research is, therefore, to explain how different approaches to intelligence (i.e., the intelligence cycle and the critical thinking process) can be combined and linked with cyber threat intelligence (CTI), which utilizes AI. To explain this, we explore how the cyber security manager can draw on social interaction and establish how it drives cognition [6] (p. 306). This can be viewed as logical in terms of establishing organizational resilience because cyber security management requires the cyber security manager to develop and share cyber security knowledge with individuals that are viewed as first responders. Social interaction is enhanced through trust-based relationships and open communication between staff and provides the basis for institutionalized learning. This gives rise to a defined risk mitigation policy and strategy within partner organizations and the utilization of cyber security models [7].

In terms of AI-based cyber attacks, it can be argued that cyber security experts will be required to intensify their effort to develop AI defense systems [8]. This will require that risk mitigation strategies are put in place to counteract cyber attacks; it also will focus attention on cyber defense from an intellectually driven and holistic perspective. It is with this in mind that the focus of the paper was to outline how sociocultural intelligence can be combined with AI to increase the organizational cyber security provision and enhance an organization's level of resilience. In doing so, we focused our efforts on providing answers to two questions: (1) How can a non-security specialist develop their appreciation and understanding of resilience through undertaking threat intelligence? (2) How can knowledge regarding different types of AI help managers better understand the complexities associated with different algorithms and their functionality vis-à-vis different types of defense system?

To assist us in our task, we drew on the knowledge derived from a small group interview that involved an academic researcher discussing various aspects of intelligence in relation to organizational security with five experts. Each participant had spent over twenty years in security and had worked in different industries and was known to be an expert in the field of organizational resilience. We contribute to the field of cyber security management by combining elements of the intelligence cycle (IC) with the critical thinking

process (CTP) [9] (p. 139) to produce a cyber threat intelligence cycle process (CTICP). This should enable staff to adopt a strategic cyber security intelligence perspective. We also highlight the importance of organizational learning and how it facilitates a higher level of intelligence that involves sociocultural interaction and thus makes the organization more resilient. The advantage of this approach is that we reflect on the interplay between centralized versus localized learning and how sociocultural intelligence is viewed as a necessary component of the strategic cyber security management process. Finally, through linking AI with sociocultural intelligence, we outline the steps in the cyber threat intelligence cycle process (CTICP) that enable managers across various industries to adopt a resilience centric approach that hardens the organization.

#### **2.  Hasan et al. [10] (p. 354) indicated that the advanced persistent threat (APT) is challenging organizational defenses because signaturebased defense mechanisms are unable to respond in real-time to new types of malicious code/intelligent mutant codes. It is worth noting that "Conventional cybersecurity tools look for historical matches to known malicious code, so hackers only have to modify small portions of that code to circumvent the defense. AI-enabled tools, on the other hand, can be trained to detect anomalies in broader patterns of network activity, thus presenting a more comprehensive and dynamic barrier to attack" [10] (p. 354).

Surya [11] (p. 991) has provided a useful definition of AI: "Artificial intelligence (AI) refers to the technology involved in the development of smart machines and software. This includes the developments of applications and systems that can reason, collect intelligence, prepare intelligently, learn, interact, interpret, and manipulate objects". Hence, AI allows users of big data to capture data from a variety of sources, store the data, and apply analytics so that decision-makers can use the outcome [11] (p. 992) in a variety of contexts (e.g., tactical and strategic).

AI can help managers to interpret patterns of cyber attack, and the outcome of a cyber threat intelligence (CTI) analysis can be placed in report form so that senior managers can offer advice based on the type of threat identified with a view to utilizing operand and operant resources. In addition, those charged with managing security can interact more fully with other functional managers and establish how cyber threat intelligence (CTI) can be strengthened using AI. However, it is worth noting that although it is recognized that AI can be used to defend an organization against cyber attacks [12] (p. 363), there are a number of challenges that senior management need to overcome vis-à-vis the use of AI. One such problem is the gap in knowledge relating to what AI/ML represents and how AI/ML can be used by managers operating at different levels of authority. It is possible to suggest that the complexities associated with AI/ML may well militate against individual managers understanding how AI can be used. To overcome the likely resistance of using AI, we propose that managers first develop an appreciation of AI/ML and think of how AI/ML can benefit them in terms of their decision-making so that the day-to-day operations are reinforced through contingency plans.

Managers need to be mindful of the fact that AI is refined through the application of ML but "humans are able to understand the behavior of others in terms of their mental states-intentions, beliefs and desires-by exploiting what is commonly designated as 'folk psychology'" [13] (p. 279). By acknowledging this, managers can avoid the various pitfalls associated with the use of AI, especially the contradiction whereby chatbots are used to help individuals (i.e., those using an organization's website) to gain certain information by responding/acting in known and logical ways. Gallese [13] (p. 285) suggests that although it is possible to make sense of how people respond to an event, with regard to human social cognition, "Language is the most specific hallmark of what it means to be human". It is with this in mind that we reflect on and pose the question: how can sociocultural intelligence be linked with AI to increase an organization's resilience?

Before progressing, we consider it necessary to reflect on the notion of what resilience is and to have a clear understanding of what it incorporates. HSSAI [14] (p. 9) provides a useful definition of resilience by indicating that it is "the ability of a system to attain the objectives of resisting, absorbing, and recovering from the impact of an adverse event, before, during, and after its occurrence. It is also a dynamic process that seeks to learn from incidents to strengthen capabilities of the system in meeting future challenges. The goals are to maintain continuity of function, degrading gracefully, and recover system functionality to a pre-designated level, as rapidly as desired and feasible".

The focus is clearly to learn from an event/incident and to make sure that those with operational responsibility can "learn from incidents", as this is what machine learning sets out to achieve. In the context of organizational learning, whereby the focus of attention is on how an individual's skill level is enhanced, Argyris [15] (p. 8) provides guidance by indicating that: "Learning is defined as occurring under two conditions. First, learning occurs when an organization achieves what it intended; that is, there is a match between its design for action and the actuality or outcome. Second, learning occurs when a mismatch between intentions and outcomes is identified and it is corrected; that is, a mismatch it turned into a match".

Whether data are collected, analyzed, and interpreted by humans or are left to a machine(s) is not what is under consideration. What is important to acknowledge is that adequate resilience requires managers to consider how best to utilize intelligence and to make use of limited intelligence. McCreight [16] (p. 5) has offered a comprehensive view as to what resilience encompasses by indicating that there are five main dimensions of resilience, which are: personal and familial socio-psychological well-being; organizational and institutional restoration; economic and commercial resumption of services and productivity; restoring infrastructural systems integrity; and operational regularity of public safety and government. The five dimensions highlighted prove useful with regard to a manager developing a comprehensive understanding of what resilience involves and how to place resilience within an organization–government–society context. Whether the relationships developed are transactional in nature or transformational in nature depends upon the organization's value system, and the leadership style/model in place.

In order to utilize big data to counteract sophisticated cyber attacks, managers are paying increased attention to the capability of AI and its deployment. Hence, it is useful to acknowledge two main but contradictory issues: the volume of data that needs to be processed versus the time available to carry out an analysis, which yields an outcome that has relevance and can be acted upon. Additionally, attention needs to be given to the cost of hiring experts for labeling the data, which relates to the issue of supervised learning, semisupervised learning, and unsupervised learning. In terms of cyber threat protection, deep learning (DL) is receiving renewed attention. For example, one area that needs immediate attention is ransomware attacks. Andrade and Yoo [17] (p. 2) noted that between 2014 and 2017, 327 families of ransomware were identified that accounted for 184 million attacks. Because cyber criminals are behind such attacks and do, of course, use technology to carry out their actions, it would be logical to suggest that advances in deep learning (DL) will help those involved in cyber security to protect computer systems and networks better. An interesting and relevant point raised by Andrade and Yoo [17] is how cyber security specialists can consider using psychology to enhance cyber security situation(al) awareness and they make clear that cognitive sciences can be utilized to enhance cyber security.

Bearing in mind that the focus of this paper was to deepen our understanding of cyber threat intelligence (CTI) and provide arguments as to how AI/ML can help senior managers to make an organization more resilient, we first need to take cognizance of what Dawson [18] (pp. 268–269) has said about an organization as it provides the basis for better understanding the relational processes that allow individual managers to utilize technology for the benefit of the organization and its partners, and at the same time, provide the basis for strategic cyber security management [1] that is aimed at safeguarding the organization against cyber attack. Dawson [18] (pp. 268–269) highlights seven points that epitomize an organization: (i) an interactive system (e.g., change in one aspect will have repercussions for another); (ii) high level of complexity (e.g., uncertainty is evident); (iii) there is no single way in which to manage a situation; (iv) resources are scare; (v) different interest groups prevail (e.g., conflict, consensus and indifference are evident); (vi) constraints exist that effect action; and (vii) the level of the individual/group needs to be known in order to identify and solve problems. It is with these seven points in mind that we embrace the view that organizational resilience is dependent upon managers having a clear appreciation of what sociocultural intelligence involves and how AI can be utilized to enable managers to make more informed cyber security-based decisions.

### **3. Placing Sociocultural Intelligence in Perspective**

The concept of sociocultural intelligence has been gaining momentum over a number of years and it is clear that the field of intelligence is expanding, and new perspectives are being offered that allow managers such as the cyber security manager to comprehend how intelligence is managed across organizational networks. To ensure that AI is not misused, we advocate a cautious and incremental approach to its use but also advise a wider understanding of AI's application in terms of intelligence provision. What can be deduced from the study of intelligence is that sociocultural intelligence (SOCINT) is purported to include "the process of directing, collecting data related to any of the social sciences, analyzing, producing, and then disseminating such data for situational awareness in any operational environment" [9] (p. 11). This is a well-known and accepted view. To better understand the antecedents of cyber threat intelligence CTI), we suggest that managers take cognizance of the intelligence cycle (IC) process and the critical thinking process (CTP), as outlined by Patton [9] (p. 139). The intelligence cycle (IC) is known to be composed of five separate but linked stages including (i) planning and direction; (ii) collection; (iii) processing; (iv) analysis and production; and (v) dissemination. The critical thinking process (CTP) is known to include eight separate but linked stages: (i) purpose; (ii) question at issue; (iii) information; (iv) interpretation and inference; (v) concepts; (vi) assumptions; (vii) implications and consequences; and (viii) point of view. By merging the intelligence cycle (IC) with the critical thinking process (CTP), it should be possible to establish how AI can be utilized by managers to better understand the role that cyber threat intelligence (CTI) plays and how it is to be managed across organizations. Before we explain this, we need to understand how the differences in learning capabilities associated with AI/ML can be drawn on to provide an intelligence focused appreciation, leading to an enhanced appreciation of resilience. To achieve this, we focused on AI/ML in relation to business so that managers in charge of various business functions can relate better to the learning capabilities afforded by AI/ML, and not worry too much about the technical aspects. Should managers need to, they can deepen their knowledge of AI/ML by consulting those with expert knowledge and/or attend specialist courses of study.

#### **4. Algorithms and Their Learning Capability**

Deep learning (DL) is a subset of AI, and it structures algorithms in layers to create an artificial neural network (e.g., a human brain) for filtering information and learning from it and making intelligent/informed decisions. DL applies ML to large datasets. ML uses algorithms to analyze, learn from the data, and make decisions based on the learning. Both DL and ML are subsets of AI. It is useful to note that different algorithms have their own unique functionality and capability for learning, some of which can be used for specific tasks. Table 1 shows different forms of learning in DL. AI systems can be divided into three types such as narrow AI (which is goal-oriented and programmed to perform a single task); general AI (representative of a machine that can learn, understand, and act in a way similar

to that of a human in a given situation); and super AI (a hypothetical AI where a machine exhibits intelligence that surpasses the brightest humans).

**Table 1.** The different types of learning associated with functionality/capability.

Source: The authors.

Managers in various industries such as banking, the motor industry, and health care have paid careful attention to AI implementation in relation to learning capabilities. Retailers utilize augmented reality for a better image (e.g., ASOS, visual search [22] and some retailers such as M&S and Kohl's have partnered with Snapchat and implemented a virtual fitting room [23]). The use of an avatar, a virtual character, with virtual reality and/or with a chatbot, is also gaining the attention of an increasing number of managers in business. It allows them to create virtual social touch points as well as create entertaining effects that result in a richer customer experience and higher customer engagement [24–26].

The application of methods and algorithms in AI/ML varies and produces a specific effect in the way in which the interaction process with end users is managed. Different algorithms also have implications for the types of data that are needed and how the data are captured and analyzed. AI is concerned with designing intelligent systems that exhibit characteristics associated with human intelligence and behavior and involves cognitive processes such as adapting to the latest information and problem-solving [27]. AI's capability varies, for example, Google Home and Alexa, integrate AI and advanced analytics (ML algorithms); chatbots sense the context of the conversation, but cannot perform a set of activities on their own; virtual assistants (e.g., Alexa, Apple Siri, Google assistant or Corona) provide daily activities such as emails or schedule meetings and can crawl through existing resources for a range of requests but with regard to customer service, however, they cannot resolve queries on their own [28] and friendly conversational chatbots such as Mitsuku and Replika, which are humanoid AI, are able to respond to emotional verbal reactions in a meaningful way [29,30]. What can be noted from this is that the communication process between a potential customer and the organization itself can be enriched by staff providing reassurance about the organization in terms of its resilience, which is mapped to an end user's understanding of security awareness. From this, we can identify the following question: how can an individual's learning capability be enhanced through using AI/ML? Finding an answer is important because managers need to link AI learning with the analysis of data and the interpretation of data so that the intelligence derived can be evidence based and used to underpin various plans/strategies. However, we stress that it is not just about AI enhancing what the organization is in terms of its commitment to dealing with customer requests or undertaking cyber threat intelligence (CTI) analysis. It is more about assuring external individuals that the staff are pro-active in terms of security awareness and can link the need for intelligence with the learning capability of those interested in buying the company's products and services, so they feel confident in buying from the company and avoid buying from rogue websites.

Learning can, according to Campbell et al. [31] and Ma and Sun [19] be divided into four types: supervised learning; semi-supervised learning; unsupervised learning; and reinforcement learning. In supervised learning, an expert trains the system by feeding labeled training data and defines variables to algorithms whereas in the case of unsupervised learning, the machine can learn inductively from unlabeled/unorganized data by analyzing the datasets to draw meaningful correlations or inferences by identifying hidden groups or grouping patterns. It can be noted that reinforcement learning (RL) is behavior-driven auto-learning where the algorithm/model (called agent) learns from interaction with its environment (by choosing from a set of possible actions) and their outcome. The sequential order and time plays an important role in reinforcement learning and is linked with a reward or penalty depending on the performance correctness and attempts to maximize the cumulative number of rewards.

The functions of AI/ML in an online business context can be grouped. For example, the basic mechanical function is an analytical tool, and the intuitive function includes humanoid AI [32]. Understanding different functions of AI/ML is useful as it helps managers to choose appropriate AI/ML tools in relation to the company's positioning strategy. We reiterate that the positioning strategy links learning with security awareness and is derived from the leadership style/model and organizational value system.

With regard to the basic mechanical function, it is based on rule-based learning at the minimum and relies on prior knowledge to perform repeated routines and/or transactional tasks (e.g., search engine used by Google or Bing). The analytical function relates to how information is processed for problem-solving in logical reasoning and how AI/ML tools learn from it. It is advanced, rule-based learning that carries out complex tasks and executes rational decisions (e.g., Deep Blue, IBM's chess player). The intuitive function incorporates digital technology that can mimic a human's learning intuitively. It is this, we feel, that can be used to ensure that sociocultural intelligence can be harnessed to get an individual to look more deeply at the issues relating to cyber threat intelligence (CTI) and map the outcomes to their own level of security awareness. Table 1 outlines the different types of AI/ML associated with learning and their use in business and is for illustrative purposes only. The differences in supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning are discussed next.

#### *4.1. Supervised Learning*

There are various algorithms for supervised learning such as a neural network (that has layers of nodes and trains data by mimicking the connectivity of the human brain, through each node being made up of inputs, weights, a threshold (bias)), and output; K-nearest neighbors (for prediction); naïve Bayes (is a classification method and wellused for text mining, spam filtering, and a recommend system); linear regression (used to identify relationships between a dependent and one or more independent variables); logistic regression (used to produce binary output by leveraging linear regression); support vector machine (SVM) (used for both data classification and regression, however, especially useful in the decision boundary to separate classes of data points); and decision tree (based on one input variable, each step split an existing subset into two, and has the capability of intuitive interpretations [19,33].

With respect to the analytical function of AI and ML, there are various levels of sophisticated applications [33,34]. For example, a convolutional neural network (CNN) is normally used for visual image analysis, classification, medial recreation, and is the recommended system, for example, whereas recurrent neural network (RNN) uses sequential data, which is distinguished by memory, and prior inputs influence the current input and output [19,35], but the outcome can vary depending on the type of RNN such as music generation, sentiment classification, and machine translation (e.g., IBM Watson Studio and Watson Machine Learning) [36]. The use of supervised learning in retail allows managers to use a shopper's basket datasheet to further define sub-segment groups by using the price of each product and the budget of an individual. It helps to uncover demand patterns for different products at different stores. For example, the combination of regression techniques may allow a retailer to predict the probability of a target variable (e.g., predict churn and switching behavior) that measures the satisfaction and engagement in the website characteristics and demographic information. This can be considered as confidence building from the perspective of the customer and provides them with a sense of well-being. However, supervised learning requires knowledge and the time to train the model, which can result in human error, which affects whether the algorithm performs as expected. Reflecting on this point, it can be suggested that should an error occur for whatever reason, it is likely that the end user will become less trusting of the technology and therefore seek to purchase another company's product/brand.


Author details

Bhaskar Kahali

12. Targeted therapy: a possibility for the future?

122 Hematology - Latest Research and Clinical Advances

essential to circumvent this problem.

13. Conclusion

bone marrow.

Next-generation sequencing (NGS) and mutational analysis have uncovered significant insights into the pathogenicity of leukemia. Consequently, the chances to develop targeted therapies for leukemia have become a distinct possibility. However, due to misdiagnosis and paucity of clinical samples, such comprehensive analysis for MS is still lacking. Nonetheless, studies conducted with small cohorts of patients did report mutations in genes such as FLT3 and NPM1. Li et al. performed an NGS analysis with six patients with a custom panel targeting 21 common genes associated with AML and myelodysplastic syndrome (MDS) [44]. In addition to FLT3 and NPM1, the authors were also able to identify mutations in several genes such as KIT, TET2, EZH2, SF3B1 and ASXL1 akin to AML [44]. This report does provide substantial evidence of an underlying similarity in the pathogenicity between MS and AML. The importance of targeted therapy is further accentuated by Piccaluga and colleagues [45]. In this study, the authors used an anti-CD33 monoclonal antibody to treat MS patients with concurrent CD33-positive AML. Two out five patients in the study elicited a complete remission of both MS and AML, while two patients showed reductions of extramedullary disease only [45]. In a different study, treatment of MS patients with BCR-ABL1, FLT3-ITD and FIP1L1-PDGFRA

mutations by tyrosine kinase inhibitors (TKIs) also showed encouraging outcome [46].

Taken together these observations does suggest that akin to AML a similar sequencing (whole genome, whole exome, and RNA seq.) base analysis should be employed in case of MS. However, the success of such an endeavor depends on the obtainability of large cohorts of samples, which unfortunately is a rarity in case of MS. Large multicenter collaboration is

Myeloid sarcoma is acknowledged as a separate disease entity for a significant period. It is an extremely rare hematological malignancy and is often associated with poor prognosis. Due to the scarcity of samples, there is no risk assessment study for MS. There are several unanswered questions for MS. Specifically, is there a bias for certain AML (such as CBF leukemia) to induce extramedullary infiltration. If yes, what is the primary mechanism(s) that drives the processes? Does MS represent alternate molecular landscapes, clonal evolution, from the original bone marrow disease? It can be argued that MS reflects a state of reduced immune surveillance in a patient at diagnosis or following hematopoietic stem cell transplantation. Consequently, this raises the possibility that MS may serve as a sanctuary site for leukemic relapse. The observation supports this implication that isolated MS ultimately gives rise to leukemia involving

MS is often challenging to identify and even more challenging to diagnose. Owing to its similarity with solid tumors, MS is often misdiagnosed, particularly as non-Hodgkin lymphoma. Accurate diagnosis of MS required an orchestrated approach involving whole body imaging (PET/CT, MRI), broad panels of immunohistochemical staining, and FISH assay for Address all correspondence to<EMAIL_ADDRESS>
Bone Marrow Trans and Cell Therapy, St Jude Children's Research Hospital, Memphis, United States


**Author details**

Harishan Tharmarajah Monash University, Victoria, Australia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*The Newborn Baby Check DOI: http://dx.doi.org/10.5772/intechopen.99524*


**3. DNA methyltransferases**

The methylation process is catalyzed by the DNA methyltransferases. There are currently four known DNMTs; DNMT1, 2, 3A and 3B (Okano et al., 1998). DNMT3A and DNMT3B are the de novo methyltransferases while DNMT1 maintains the methylation patterns during DNA replication (mitosis) (Bestor, 2000). The actual function of DNMT2 is not clear. It has been shown that DNMT2 possesses weak methyltransferase activity, and its deletion in the embryonic cells caused no detectible effect on global methylation (Okano et al., 1998). DNMT1 has a 5-30 fold preference for hemimethylated DNA (Goyal et al., 2006;Yoder et al., 1997). As well as to the epigenetic silencing of particular genes, DNMT1 supports the long term silencing of non-coding DNA, including most of the repetitive elements (Brannan & Bartolomei, 1999;Fuks, 2005;Jaenisch & Bird, 2003;Jones & Takai, 2001). DNMT1 exist as a component of the DNA replication complex, and thus methylates the newly synthesized DNA strand in correspondence to the template strand (Vertino et al., 2002). DNMT1 has different isoforms, the somatic tissue isoform DNMT1S, the oocyte specific isoform DNMT1o and the spermatocyte isoform DNMT1p. DNMT1o is responsible for maintaining maternal imprints during cleavage (Howell et al., 2001). In addition to that, over expression of DNMT1 has been reported in human tumours and many contribute to the global methylation abnormalities seen in cancer cells although increased expression of the DNMTs likely to be only partially responsible for the observed methylation abnormalities since not all tumours overexpress these enzymes (Robertson & Jones, 2000).

On the other hand, de novo DNA methylation is catalyzed by DNMT3a, DNMT3b and DNMT3L (Okano et al.,1999; Chedin et al., 2002). DNMT3L lacks the ability to bind to SAM, and is responsible for increasing the binding of DNMT3a to SAM (Chedin et al., 2002; Aapola et al., 2000). DNMT2, a small 391-amino-acid protein, is reported to possess weak DNA methyltransferase activity, but its biological function is not yet elucidated (Dong et al, 2001). Very recent studies have shown that Dicer-mediated microRNA biogenesis is involved in modulation of DNA methylation by indirectly regulating the expression of DNMT3 genes (Sinkkonen et al., 2008; Benetti et al., 2008). Dicer belongs to the RNase III family enzymes and is implicated in processing the biosynthesis of small interfering RNAs (siRNAs) and microRNAs (miRNAs) (Kim et al., 2005). In dicer−/− cells, the microRNAs of the miR-290 cluster are depleted and expression levels of their target Rbl2 protein (retinoblastoma-like protein) are increased, leading to downregulation of DNMT3 gene expression through Rbl2-

human cancers (Feinberg & Vogelstein, 1983; Narayan et al., 1998; Jones & Baylin, 2002). Although less well studied than DNA hypermethylation, several lines of investigation indicate that the global DNA hypomethylation identified in cancer cells might contribute to structural changes in chromosomes, loss of imprinting (LOI), micro satellite and chromosome instability through aberrant DNA recombination, aberrant activation of protooncogene expression and increased mutagenesis (Chen et al.,1998; Eden et al., 2003; Kaneda & Feinberg, 2005; Jones & Baylin, 2002). Global genomic hypomethylation in breast cancer has been known to correlate with some clinical features such as disease stage, tumor size and histological grade (Soares et al., 1999). Some proto-oncogenes implicated in proliferation and metastasis (e.g., synuclein γ and urokinase genes) or drug resistance to endocrine therapy (e.g., N-cadherin, ID4, annexin A4, β-catenin and WNT11 genes) have been found to be upregulated in breast cancer through the hypomethylation of their promoters (Fan et al.,

The methylation process is catalyzed by the DNA methyltransferases. There are currently four known DNMTs; DNMT1, 2, 3A and 3B (Okano et al., 1998). DNMT3A and DNMT3B are the de novo methyltransferases while DNMT1 maintains the methylation patterns during DNA replication (mitosis) (Bestor, 2000). The actual function of DNMT2 is not clear. It has been shown that DNMT2 possesses weak methyltransferase activity, and its deletion in the embryonic cells caused no detectible effect on global methylation (Okano et al., 1998). DNMT1 has a 5-30 fold preference for hemimethylated DNA (Goyal et al., 2006;Yoder et al., 1997). As well as to the epigenetic silencing of particular genes, DNMT1 supports the long term silencing of non-coding DNA, including most of the repetitive elements (Brannan & Bartolomei, 1999;Fuks, 2005;Jaenisch & Bird, 2003;Jones & Takai, 2001). DNMT1 exist as a component of the DNA replication complex, and thus methylates the newly synthesized DNA strand in correspondence to the template strand (Vertino et al., 2002). DNMT1 has different isoforms, the somatic tissue isoform DNMT1S, the oocyte specific isoform DNMT1o and the spermatocyte isoform DNMT1p. DNMT1o is responsible for maintaining maternal imprints during cleavage (Howell et al., 2001). In addition to that, over expression of DNMT1 has been reported in human tumours and many contribute to the global methylation abnormalities seen in cancer cells although increased expression of the DNMTs likely to be only partially responsible for the observed methylation abnormalities since not

On the other hand, de novo DNA methylation is catalyzed by DNMT3a, DNMT3b and DNMT3L (Okano et al.,1999; Chedin et al., 2002). DNMT3L lacks the ability to bind to SAM, and is responsible for increasing the binding of DNMT3a to SAM (Chedin et al., 2002; Aapola et al., 2000). DNMT2, a small 391-amino-acid protein, is reported to possess weak DNA methyltransferase activity, but its biological function is not yet elucidated (Dong et al, 2001). Very recent studies have shown that Dicer-mediated microRNA biogenesis is involved in modulation of DNA methylation by indirectly regulating the expression of DNMT3 genes (Sinkkonen et al., 2008; Benetti et al., 2008). Dicer belongs to the RNase III family enzymes and is implicated in processing the biosynthesis of small interfering RNAs (siRNAs) and microRNAs (miRNAs) (Kim et al., 2005). In dicer−/− cells, the microRNAs of the miR-290 cluster are depleted and expression levels of their target Rbl2 protein (retinoblastoma-like protein) are increased, leading to downregulation of DNMT3 gene expression through Rbl2-

2006; Gupta et al., 2003; Pakneshan et al., 2004).

all tumours overexpress these enzymes (Robertson & Jones, 2000).

**3. DNA methyltransferases**

mediated transcriptional repression, and in turn causing the DNA methylation defect (global hypomethylation) (Sinkkonen et al., 2008; Benetti et al., 2008). Regarding the role of DNMTs in breast tumorigenesis, it has been reported that DNMT3b mRNA is overexpressed in breast cancer, a finding that correlates well with the hypermethylator phenotype and poor prognosis in breast tumors (Girault et al., 2003; Roll et al.,2008).


**7. Risk management team elicitation**

The selection of members for the Risk Management Team (RMT), is not an easy task at all. A depth analysis of background and education was carried on for selecting the potential members of the team. The analysis, brainstorming, experience, background of the RMT members is critical not only for the risk management identification; it is for all the process including the monitoring and control. Eliciting the risk team members can be such a critical milestone for success or failure for the rest of the risk management process and the project itself. At the end, risk management is an input-output process that if wrong data or knowledge is feed into, then the results expected, most likely would be trustless or would included biases which at the end affect seriously the integrity and best practices in risk management.

**Figure 6.** Overview of the Expert Elicitation Process (USEPA, 2011)

**Figure 5.** Knowledge and Ignorance of Humans (Ayyub, 2000),

**7. Risk management team elicitation**

PM and the team.

Therefore, the intersection of the expert's knowledge with the ignorance space outside the knowledge base can be viewed as a measure of creativity and imagination. Another expert (i.e., Expert B) would have her/his own ellipses that might overlap with the ellipses of Expert A, and might overlap with other region by varying magnitudes (Ayyub, 2000).

SME`s are crucial for giving credible data for the risk model. If SMEs are not part of the risk management study, the results will not be trusted, causing this a failure of the process, the

The selection of members for the Risk Management Team (RMT), is not an easy task at all. A depth analysis of background and education was carried on for selecting the potential members of the team. The analysis, brainstorming, experience, background of the RMT members is critical not only for the risk management identification; it is for all the process including the monitoring and control. Eliciting the risk team members can be such a critical milestone for success or failure for the rest of the risk management process and the project itself. At the end, risk management is an input-output process that if wrong data or knowledge is feed into, then the results expected, most likely would be trustless or would included biases

which at the end affect seriously the integrity and best practices in risk management.

Expert Elicitation (EE) is a multidisciplinary process (Figure 6) that can inform decisions by characterizing uncertainty and filling data gaps where traditional scientific research is not feasible or data are not yet available. Although there are informal and nonprobabilistic EE methods for obtaining expert judgment. The goal of an EE is to characterize, to the degree possible, each expert's beliefs (typically expressed as probabilities) about relationships, quantities, events, or parameters of interest. The EE process uses expert knowledge, synthesized with experiences and judgments, to produce probabilities about their confidence in that knowledge. Experts derive judgments from the available body of evidence, including a wide range of data and information ranging from direct empirical evidence to theoretical insights. Even if direct empirical data were available on the item of interest, such measurements would not necessarily capture the full range of uncertainty. EE allows experts to use their scientific judgment transparently to interpret available empirical data and theory (Ayyub, 2011).

As mentioned by Vose (2008), it is usually impossible to obtain data from which to determine the uncertainty of all the variables within the model accurately, for a number of reasons:

The uncertainty in subjective estimates has two components: the inherent randomness of the variable itself and the uncertainty arising from the expert's lack of knowledge of the parameters that describe that variability.

Project and Enterprise Risk Management at the California Department of Transportation 421

the risks identified for each project; it can be observed the source of risk, which in Caltrans relates to the functional units (e.g. environmental, right of way, design, project management,

Project A was a Historical Bridge, Project B a Direct Access Ramp (DAR) with a Transit Station (TS) and Project C a standard bridge. The main goal of the risk analysis for each project was to determine the cost risk contingency associated to each project risk register. Figure 10 shows the critical risks obtained for each project once the qualitative analysis was performed. The qualitative assessment matrix (Figure 9) used for ranking the risk criticality,

The risk sources indicated in Figure 7 are: design, right of way (rw), environmental (env), program project management (ppm), construction (const), exterior (ext), engineering (eng)

The RM was responsible for educating the RMT along each phase of the risk management implementation. For the qualitative risk analysis in particular, it was useful to provide

As can be observed, the critical risks are only a few ones in compare with all the risks identified at the initiation phase. The purpose of the qualitative risk analysis was to select those risks that represent a major negative or positive impact into the project objectives. A critical part for performing the qualitative analysis was to define the probability and impact ranges. The impacts were defined in terms of cost only for these projects. In addition, construction, engineering and organizational risks were not part of the quantitative risk

analysis, since there were not scored as critical in the qualitative risk analysis.

forms part of Caltrans standard process for risk management.

construction etc.)

and organizational (org).

**Figure 8.** Risk matrix (Caltrans, 2007)

tangle examples to the team before and during the meeting.

In cases where there are no subject matter experts available, it is recommended to even hire external experts for obtaining the uncertainty ranges per variable or risk. Caltrans's practices towards project management and the project delivery process use a Project Development Team (PDT) which changes alongside the phases of the project. These teams are rather big in size and usually integrate stakeholders, subject matter experts and individuals coming from the same or different functional units (right of way, environmental, design, construction, etc.). Firstly, the downside of using such a team like the PDT is the size since it could be complicated for the RM to facilitate the meetings for obtaining better results towards assessing the uncertainty. In addition, the team members should feel free to talk about risk and in some cases having more than one member coming from the same division of functional unit, can cause some limitations for discussion and brainstorming.

In general, risk teams are rather small in compare with the traditional project teams. As a general rule, to invite a representative from each department or division is recommended. This approach definitely will help the whole process and will assist the RM to maintain the team focused in talking only about risk.


*Edited by Basel I. Ismail*

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ISBN 978-1-83969-233-8

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*Supporting open minds since 2005*

Lifestyle and Epidemiology - The Double Burden of Poverty and Cardiovascular Diseases in African Populations http://dx.doi.org/10.5772/intechopen.92932 Edited by Kotsedi Daniel Monyeki and Han C.G. Kemper

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Muzi Maseko, Bongubuhle Mlambo, Edgar Phukubje, Thamsanqa Nyundu, Joel Noutakdie Tochie, Frank Nzali, Mazou Ngou Temgoua, Simeon Pierre Choukem, Maseabata Ramathebane, Lineo Maja, Molungoa Sello, Emmanuel Limbole Bakilo, Dadi Serge Nkarnkwin Kasanza, Lucette Womba Tshomba, Gilot Ngoma, Venance Atheno Simele, Mireille Kika, Jean Booto, Reagan Wiyaka, Martine Ekeba, Janet Michel Michel, Marcel Tanner, Ahmed Anas Guerboub, Ghizlaine Belmejdoub, Tebogo Maria Maria Mothiba, Kudzaishe Mangombe, Ronald Musizvingoza, Naomi Wekwete, Amos Milanzi, Charles Lwanga, Shisana M. Baloyi, Kebogile Mokwena, Stella Chewe Sabi, Wilna Oldewage-Theron, Christa Grobler, Mamare Adelaide Bopape, Betty Sebati, Kotsedi Daniel Monyeki, James Hlengani Siweya, Mafoloa Suzan Monyeki, Julius Chacha Mwita, Brian Godman, Charity Ngoatle, Moloko Matshipi, Phuti Makgae, Abayomi Olusola Ayodapo, Ibisola Babalola, Rosina Nkwana, Gift Makaleng, Mabitsela Mphasha, Debbie Habedi, Julia Manyelo

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**2.2 Public infrastructure delivery challenges in South Africa**

According to academics, several difficulties stem from inadequate infrastructure development and delivery caused by various causes. Experts say South Africa's infrastructure project delivery system needs several problems. Olatunji et al. [21] state that some difficulties may arise due to wrong infrastructure delivery, including cost overruns, project delays, poor quality, inefficient fund management, and poor social benefits in service delivery. Inadequate human resources procedures, insufficient procurement methods, and a lack of leadership impede service delivery [22]. To provide public services, the government relies on various state departments [23]. According to Koops [24], effective project delivery might include additional dependent elements, such as observable performance, to meet customer expectations. According to the World Bank [25], increasing public sector performance necessitates eliminating infrastructural gaps. A rise in domestic and foreign direct investment (FDI), as well as private sector engagement via the adoption of public-private partnerships (PPPs) in public infrastructure delivery, appears to be a critical effort made by the governments [26]. According to Kudumela [27], a deficit in skills capability at the local level, lack of finance, political instability, and corruption are among the key reasons that cause problems for infrastructure delivery in underdeveloped nations. There were several obstacles to public infrastructure project delivery in South Africa. While these problems and variables apply to all types of infrastructure, public construction infrastructure needs to be immune to these issues. The Presidency [28] lists the following as enablers where communities may profit from better infrastructure project performance and where problems can be resolved via action. They listed project challenges as follows;

To overcome these challenges to ensure communities benefits from projects, they proposed many actions to improve the performance of infrastructure, including:

The Infrastructure Development Bill passed Act No. 23 of 2014 on June 2, 2014, aiming to increase the government's ability to deploy infrastructure. Infrastructure development is one of the essential strategies for resolving the legacies of privilege and underdevelopment that are a part of our terrible history [29].


**2. Adult autism**

All diagnoses of autism have to take a developmental history from childhood, which will include persistent deficits in social communication and social interaction from the early developmental period, as well as restricted, repetitive patterns of behaviour causing clinically significant impairment in functioning (American Psychiatric Association [2]). The problem with adult autism diagnosis will include getting a relatively early history from an informant which may be a parent or other, the problem of camouflaging because of treatment or just life experience which makes it more difficult to diagnose the adult with autism. They may have learned about eye contact, etc. School reports or home videos sometimes help. They will often present with comorbidities, for example, depression, (70%), anxiety (40%), attention deficit disorder or psychosis. Mazefsky and White [3] "caution against excessive reliance on ADOS (Autism Diagnostic Observation Scale), Lord et al. [4] for diagnosis".


**12. Evolution of mathematical modeling**

Epidemiological modeling has undergone numerous revisions and improvements to cope up with emerging new infections and discovery of new concepts and basis of existing infections. It is well-known that increase in model complexity by including more relevant biological details improves the accuracy. But it is practically not feasible to construct a fully accurate model. There will always be some factors related to host, pathogen, environment or population which cannot be estimated or predicted. Processes of random occurrence affect the degree of accuracy of model. Predictability of the model depends highly on a strong interplay between statistics and models for estimation of parameters from epidemiological data.

The key assumption of deterministic mathematical model is the existence of homogeneous and constant population, where it is assumed that, the death of a susceptible, infectious or recovered individual is immediately compensated by the birth of a new susceptible. In actual practice, heterogeneity is observed where the population can be divided into several homo‐ geneous subpopulations or groups on the basis of mode of transmission, contact patterns, latent period, infectious period, genetic susceptibility or resistance as well as socio-economic, cultural, demographic and geographic factors. All these complexities can be suitably incorpo‐ rated in a multi-group model and can prove effective in explaining the dynamics of sexually transmitted diseases such as gonorrhea or AIDS [29]. Population-level heterogeneities that, if included can improve the model's accuracy and predictive ability, include age, gender, behavior, genetic susceptibility [7]. Influenza infection is well-studied and well-modeled. But, there are still several aspects of the infection which have not been included in model construc‐ tion like, the contribution of strain-specific cell tropism, pre-existing immunity, effect of host genetic factors on virulence and transmissibility of a particular strain. The model may be modified to determine the severity, duration and outcome of infection progression within an individual [30]. Moreover, drug resistance may develop. Therefore, a model with an immune response can be generated for better insight into the disease dynamics and the predictions from the model are different from those of a model formulated without an immune response. Twophase solution can be used to study different viral infections. Study of models, specific for influenza or H1N1 require careful parameterization to match available data and it should reflect both statistical uncertainty and uncertainty in data itself [13].

does not affect severely the transmission of the disease [11], [27]. For better understanding of

Unearthing the Complexities of Mathematical Modeling of Infectious Disease Transmission Dynamics

http://dx.doi.org/10.5772/57387

199

Assumptions that have been discussed in a previous section hold true for developing countries where all individuals are exposed to similar death pressure, irrespective of age, due to the environmental conditions and lack of medical facilities. But the situation differs in developed countries where square shape age distribution is manifested. This can be attributed to availability of proper medical care. Modeling becomes somewhat complicated in analysis of macroparasitic infections owing to their inherent differences from the infections caused due to microparasites. In case of microparasitic infections, the internal dynamics of the pathogen within the host is not as crucial as the host's infection status. But, the complex life cycle of the macroparasite within the host necessitates inclusion of this parameter in modeling of macro‐ parasitic infections. Moreover, disease transmission and pathogenicity are highly related to

Furthermore, complex dynamics may be followed by the infections, where the causative agent can undergo mutation and can create a class of population, susceptible to the new strains. Previous infections in those cases confer only partial cross-immunity and thus, history of infection is important. Number of parameters in the model therefore, increases exponentially with the number of strains. In these circumstances, either reduced transmission approach or polarized immunity concept is applied [11]. Emergence of drug resistant strains is an issue of great concern which should be included in modeling disease dynamics. Sexually transmitted diseases unfold a different picture because of high risk of exposure of sexually active individ‐

Complexities may also arise in modeling of diseases in presence of an optimal control measure where there is a delay element between implementation of isolation and quaran‐ tining. Inclusion of delay increases the proportion of transmission occurring with an asymptomatic or pre-symptomatic infector and hence, necessitating adoption of stringent measure including contact tracing. Oversimplification may fail to estimate the efficacy of contact tracing in reducing the transmission. However, in this delay period, the patient may practice self-isolation depending on the nature and severity of the symptoms and the time

In a modified SIR model with "skipping" dynamics for diseases like influenza, it has been shown that the immunity of hosts depends on previous exposure to the disease and immune memory. Due to mutational changes in influenza virus or antigenic evolution, hosts may be re-infected with the disease every few years, with years of 'skips' or gaps in between [31].

The simple SIR model has been extended to include the effects of saturation where the incidence rate is not bilinear in S and I but a general function *f*(S,I). It is assumed that in presence of large number of infectives in the population, the number of contacts per time diminishes [3]. The simplistic SIR model has evolved into biphasic SIR model (B-SIR) to explain the key features of multi-ennial epidemic cycle. In this model, the SIR dynamics alternates between a relatively fast epidemic phase in which there is a significant increase in the number of infected as well as a rapid decline in the count of the susceptible and a slow build-up phase

the model with periodic perturbations, bifurcation diagram proves beneficial [7].

the load of the causative organisms within the host system [6], [7].

uals, belonging to a particular age group.

scales involved [22].

A new rule, known as 20-80 rule has been proposed by researchers for certain infectious diseases which show unique features. According to the rule, 20% of the individuals are responsible for 80% of onward transmission and they are known as super-spreaders. This gives rise to variation in the number of secondary infections per infected individual which is denoted by Z. It is described by two parameters-mean R0 among infections and dispersion parameter, K.

In multiple-host diseases, different hosts or reservoirs are involved. Dynamics of the disease itself may vary in the different hosts and the mode of transmission from one host to another is quite complex. All these factors should be borne in mind during study of these diseases [6].

In viral disease like Hepatitis B, there is a carrier state where the individual is not fully recovered and is able to transmit low level of infection throughout his life. Some diseases like chlamydial infection is characterized by infected individual who is asymptomatic but is able to transmit disease. Again, meningitis may remain benign for long within population but may exhibit sporadic symptomatic outbreaks [7]. Infectious diseases which are characterized by multi-factorial pathologies or where concurrent infections prevail in immune-compromised hosts usher in complexities in mathematical modeling [1]. A common feature of certain childhood infections is the periodical occurrence of high levels of infection of school-going children which necessitates the inclusion of a time-varying contact rate between susceptible and infected classes of individuals in the model, which is then known as seasonally-forced model and is recognized by a sinusoidal function. The concept of seasonality in the coefficient of transmission and temporal heterogeneity was introduced by Soper to account for the high amplitude outbreaks of measles in Glasgow. These types of diseases are characterized by strong annual, biennial and sometimes irregular oscillations. In India, polio has been found to occur annually. Measles is known to exhibit biennial occurrence for extended periods in London. It has been noticed that the same disease can have different temporal patterns during different epochs which may be attributed to changes in epidemiological factors, e.g. population birth rate, magnitude of disease transmission, and strength of seasonality [14]. Modeling of measles dynamics is best achieved by incorporation of an epidemic oscillator, which takes into account the birth rate variations and can analyze chaotic behavior of epidemic outbreaks. It has been observed that high birth rates drive measles dynamics to annual cycles and dimin‐ ishing birth rates result in biennial patterns. Vaccination causes irregular cycles. Complex oscillations arise due to interactions between the externally imposed annual seasonality and intrinsic oscillatory dynamics of the infection itself [26]. The approach of "term-time forcing" should also be employed for temporally forced models of another childhood bacterial infection, pertussis or whooping cough. Though both measles and pertussis possess identical values for basic reproduction ratio, yet they exhibit different dynamics because the infectious period in whooping cough is longer than that of measles. School holidays during Christmas does not affect severely the transmission of the disease [11], [27]. For better understanding of the model with periodic perturbations, bifurcation diagram proves beneficial [7].

influenza or H1N1 require careful parameterization to match available data and it should

A new rule, known as 20-80 rule has been proposed by researchers for certain infectious diseases which show unique features. According to the rule, 20% of the individuals are responsible for 80% of onward transmission and they are known as super-spreaders. This gives rise to variation in the number of secondary infections per infected individual which is denoted by Z. It is described by two parameters-mean R0 among infections and dispersion parameter, K.  Dynamics of the disease itself may vary in the different hosts and the mode of transmission from one host to another is quite complex. All these factors should be borne in mind during study of these diseases [6].

In viral disease like Hepatitis B, there is a carrier state where the individual is not fully recovered and is able to transmit low level of infection throughout his life. Some diseases like chlamydial infection is characterized by infected individual who is asymptomatic but is able to transmit disease. Again, meningitis may remain benign for long within population but may exhibit sporadic symptomatic outbreaks [7]. Infectious diseases which are characterized by multi-factorial pathologies or where concurrent infections prevail in immune-compromised hosts usher in complexities in mathematical modeling [1]. A common feature of certain childhood infections is the periodical occurrence of high levels of infection of school-going children which necessitates the inclusion of a time-varying contact rate between susceptible and infected classes of individuals in the model, which is then known as seasonally-forced model and is recognized by a sinusoidal function. The concept of seasonality in the coefficient of transmission and temporal heterogeneity was introduced by Soper to account for the high amplitude outbreaks of measles in Glasgow. These types of diseases are characterized by strong annual, biennial and sometimes irregular oscillations. In India, polio has been found to occur annually. Measles is known to exhibit biennial occurrence for extended periods in London. It has been noticed that the same disease can have different temporal patterns during different epochs which may be attributed to changes in epidemiological factors, e.g. population birth rate, magnitude of disease transmission, and strength of seasonality [14]. Modeling of measles dynamics is best achieved by incorporation of an epidemic oscillator, which takes into account the birth rate variations and can analyze chaotic behavior of epidemic outbreaks. It has been observed that high birth rates drive measles dynamics to annual cycles and dimin‐ ishing birth rates result in biennial patterns. Vaccination causes irregular cycles. Complex oscillations arise due to interactions between the externally imposed annual seasonality and intrinsic oscillatory dynamics of the infection itself [26]. The approach of "term-time forcing" should also be employed for temporally forced models of another childhood bacterial infection, pertussis or whooping cough. Though both measles and pertussis possess identical values for basic reproduction ratio, yet they exhibit different dynamics because the infectious period in whooping cough is longer than that of measles. School holidays during Christmas

reflect both statistical uncertainty and uncertainty in data itself [13].

responsible for onward transmission [19].

198 Trends in Infectious Diseases

Assumptions that have been discussed in a previous section hold true for developing countries where all individuals are exposed to similar death pressure, irrespective of age, due to the environmental conditions and lack of medical facilities. But the situation differs in developed countries where square shape age distribution is manifested. This can be attributed to availability of proper medical care. Modeling becomes somewhat complicated in analysis of macroparasitic infections owing to their inherent differences from the infections caused due to microparasites. In case of microparasitic infections, the internal dynamics of the pathogen within the host is not as crucial as the host's infection status. But, the complex life cycle of the macroparasite within the host necessitates inclusion of this parameter in modeling of macro‐ parasitic infections. Moreover, disease transmission and pathogenicity are highly related to the load of the causative organisms within the host system [6], [7].

Furthermore, complex dynamics may be followed by the infections, where the causative agent can undergo mutation and can create a class of population, susceptible to the new strains. Previous infections in those cases confer only partial cross-immunity and thus, history of infection is important. Number of parameters in the model therefore, increases exponentially with the number of strains. In these circumstances, either reduced transmission approach or polarized immunity concept is applied [11]. Emergence of drug resistant strains is an issue of great concern which should be included in modeling disease dynamics. Sexually transmitted diseases unfold a different picture because of high risk of exposure of sexually active individ‐ uals, belonging to a particular age group.

Complexities may also arise in modeling of diseases in presence of an optimal control measure where there is a delay element between implementation of isolation and quaran‐ tining. Inclusion of delay increases the proportion of transmission occurring with an asymptomatic or pre-symptomatic infector and hence, necessitating adoption of stringent measure including contact tracing. Oversimplification may fail to estimate the efficacy of contact tracing in reducing the transmission. However, in this delay period, the patient may practice self-isolation depending on the nature and severity of the symptoms and the time scales involved [22].

In a modified SIR model with "skipping" dynamics for diseases like influenza, it has been shown that the immunity of hosts depends on previous exposure to the disease and immune memory. Due to mutational changes in influenza virus or antigenic evolution, hosts may be re-infected with the disease every few years, with years of 'skips' or gaps in between [31].

The simple SIR model has been extended to include the effects of saturation where the incidence rate is not bilinear in S and I but a general function *f*(S,I). It is assumed that in presence of large number of infectives in the population, the number of contacts per time diminishes [3].

The simplistic SIR model has evolved into biphasic SIR model (B-SIR) to explain the key features of multi-ennial epidemic cycle. In this model, the SIR dynamics alternates between a relatively fast epidemic phase in which there is a significant increase in the number of infected as well as a rapid decline in the count of the susceptible and a slow build-up phase characterized by continuous replenishment of susceptible. This enables estimation of threshold(bifurcation) values at which there will be a switching from biennial to annual epidemic dynamics [14].

dynamic approach can be successfully utilized in mathematical modeling of such viruses like dengue, human respiratory synctial virus, Hepatitis C, Toxoplasma etc. [19], [33], [34].

Unearthing the Complexities of Mathematical Modeling of Infectious Disease Transmission Dynamics

Several new modeling concepts are being explored for detailed description of diseases like AIDS, tuberculosis. These include differential infectivity (DI) and staged progression (SP) model [29]. For modeling of infectious diseases with long infectious period and where there are multiple alternative disease progression pathways and branching, or where there is considerable difference in virulence or when only a part of the infected population undergoes a treatment whereas the rest remains untreated, staged progression models seem to be the most suitable [35]. Individuals infected with HIV sequentially pass through a series of stages, being highly infectious in first few weeks after their own infection, then having low infectivity for many years, and finally becoming more infectious as their immune system breaks down and they progress to full-blown AIDS. Investigation of the influence of imperfect vaccine on HIV transmission by individuals in AIDS stage with the help of SP model revealed that the imperfect vaccine can eliminate HIV in a given community at vaccination reproduction number less than unity, but the disease will persist otherwise [36], [37]. Since, tuberculosis is characterized by presence of very long latent period and infectious period, time is not considered to be constant in modeling of the disease. The latent period and infectious period are divided into n-stages and stage progression model with bilinear incidence was formulated [39]. Characteristic features of DI and SP models have been given in a tabular form (Table 3).

**DI model SP model**

Healthcare-associated infections (HCAI) or nosocomial infections or hospital acquired infections are source of great concern in developed as well as developing countries. Such infections can be modeled using either deterministic or stochastic approach for evaluation of control policies. This requires sound model parameterization and sensitivity analyses [40].

Suitable for modeling of diseases exhibiting variability of infectiousness with time as in AIDS or where time-scale of

http://dx.doi.org/10.5772/57387

201

Total host population is partitioned into the following compartments: the susceptible compartment, the infectious compartment , whose members are in the *i*-th stage of the disease progression, and the terminal compartment. It is assumed that there is no recovery from the disease, and thus the only exit from the terminal

Disease-free equilibrium and unique endemic equilibrium

have also been established with these models

disease transmission is too long.

compartment is death.

Suitable for diseases where viral levels differ between individuals as in sexually transmitted diseases and also diseases where infectivity depends on parasite or viral loads in infected hosts or vectors e.g. malaria, dengue

groups according to their infectivities. Total population

Infection-free equilibrium is globally stable and there exists

a unique endemic equilibrium for these models

**Table 3.** Characteristic features of DI and SP model

Infectives are divided into a number of a

size is assumed constant.

fever.

A likelihood-based methodology has been developed which assumes the generation interval to follow Weibull distribution and a specific infection network underlies the observed epidemic curve [23].

An agent-based model in the study of tuberculosis has revealed that recruitment of increased number of resting macrophages to the infection site in tuberculosis increases bacterial load. This suggests that the inflammatory response may be detrimental to the host [32].

Since, application of mathematical modeling approach shows that vaccination decreases the mean age at infection, it deduces that implementation of vaccination programs can actually increase the incidences of absolute number of serious cases, if the probability of disease complications increases with age. This is an interesting paradigm of mathematical modeling. In case of imperfect or ineffective vaccination, a backward transfer between the susceptible and recovered compartments must be considered because the vaccinated individuals may come back to the susceptible category or may become directly infected through nonlinear transmission. This behavior gives rise to bi-stability and backward bifurcation [26].

Exponentially growing population and incidences of mortality due to childhood infections in developing countries are not considered in modeling of infections where the host population is assumed to be of constant size. The component of heterogeneity also exists in cases of sexually transmitted diseases. This is further complicated by the fact that, unlike other infections, recovery from STD does not guarantee development of acquired immunity. The currently existing rationale of mathematical modeling may need to be modified while investigating mother-to-child diseases and diseases transmitted by multiple users of syringe as in AIDS [6].

A hierarchical dynamics has been observed in epidemic outbreaks of complex heterogene‐ ous networks. Propagation of infection occurs via a cascade from higher to lower degree classes [10].

In most of the cases, epidemiological processes within the population and immunological processes involved within the individual host are considered separately, with no explicit interaction between the two. Infectious diseases, caused by RNA viruses are characterized by high mutation rate and short generation time of the viruses indicating that evolutionary processes occur rapidly and evolutionary outcomes depend on the fitness of viral mutants at different stages of viral lifecycles and interactions between viral variants. Moreover, ecological and epidemiological factors, such as host contact patterns, transmission routes, host move‐ ments also govern the success of viral transmission in a population. In this approach, three different time scales are considered-a fast time scale for within-host dynamics, an intermediate time scale for epidemiological process and a slow time scale for the environmental contami‐ nation [5]. Link between the evolutionary process at the pathogen level and the ecological processes at the host population level can be established by phylodynamics [11]. The phylo‐ dynamic approach can be successfully utilized in mathematical modeling of such viruses like dengue, human respiratory synctial virus, Hepatitis C, Toxoplasma etc. [19], [33], [34].

Several new modeling concepts are being explored for detailed description of diseases like AIDS, tuberculosis. These include differential infectivity (DI) and staged progression (SP) model [29]. For modeling of infectious diseases with long infectious period and where there are multiple alternative disease progression pathways and branching, or where there is considerable difference in virulence or when only a part of the infected population undergoes a treatment whereas the rest remains untreated, staged progression models seem to be the most suitable [35]. Individuals infected with HIV sequentially pass through a series of stages, being highly infectious in first few weeks after their own infection, then having low infectivity for many years, and finally becoming more infectious as their immune system breaks down and they progress to full-blown AIDS. Investigation of the influence of imperfect vaccine on HIV transmission by individuals in AIDS stage with the help of SP model revealed that the imperfect vaccine can eliminate HIV in a given community at vaccination reproduction number less than unity, but the disease will persist otherwise [36], [37]. Since, tuberculosis is characterized by presence of very long latent period and infectious period, time is not considered to be constant in modeling of the disease. The latent period and infectious period are divided into n-stages and stage progression model with bilinear incidence was formulated [39]. Characteristic features of DI and SP models have been given in a tabular form (Table 3).

#### **Table 3.** Characteristic features of DI and SP model

characterized by continuous replenishment of susceptible. This enables estimation of threshold(bifurcation) values at which there will be a switching from biennial to annual

A likelihood-based methodology has been developed which assumes the generation interval to follow Weibull distribution and a specific infection network underlies the observed

An agent-based model in the study of tuberculosis has revealed that recruitment of increased number of resting macrophages to the infection site in tuberculosis increases bacterial load.

Since, application of mathematical modeling approach shows that vaccination decreases the mean age at infection, it deduces that implementation of vaccination programs can actually increase the incidences of absolute number of serious cases, if the probability of disease complications increases with age. This is an interesting paradigm of mathematical modeling. In case of imperfect or ineffective vaccination, a backward transfer between the susceptible and recovered compartments must be considered because the vaccinated individuals may come back to the susceptible category or may become directly infected through nonlinear

Exponentially growing population and incidences of mortality due to childhood infections in developing countries are not considered in modeling of infections where the host population is assumed to be of constant size. The component of heterogeneity also exists in cases of sexually transmitted diseases. This is further complicated by the fact that, unlike other infections, recovery from STD does not guarantee development of acquired immunity. The currently existing rationale of mathematical modeling may need to be modified while investigating mother-to-child diseases and diseases transmitted by multiple users of syringe

A hierarchical dynamics has been observed in epidemic outbreaks of complex heterogene‐ ous networks. Propagation of infection occurs via a cascade from higher to lower degree

In most of the cases, epidemiological processes within the population and immunological processes involved within the individual host are considered separately, with no explicit interaction between the two. Infectious diseases, caused by RNA viruses are characterized by high mutation rate and short generation time of the viruses indicating that evolutionary processes occur rapidly and evolutionary outcomes depend on the fitness of viral mutants at different stages of viral lifecycles and interactions between viral variants. Moreover, ecological and epidemiological factors, such as host contact patterns, transmission routes, host move‐ ments also govern the success of viral transmission in a population. In this approach, three different time scales are considered-a fast time scale for within-host dynamics, an intermediate time scale for epidemiological process and a slow time scale for the environmental contami‐ nation [5]. Link between the evolutionary process at the pathogen level and the ecological processes at the host population level can be established by phylodynamics [11]. The phylo‐

This suggests that the inflammatory response may be detrimental to the host [32].

transmission. This behavior gives rise to bi-stability and backward bifurcation [26].

epidemic dynamics [14].

epidemic curve [23].

200 Trends in Infectious Diseases

as in AIDS [6].

classes [10].

Healthcare-associated infections (HCAI) or nosocomial infections or hospital acquired infections are source of great concern in developed as well as developing countries. Such infections can be modeled using either deterministic or stochastic approach for evaluation of control policies. This requires sound model parameterization and sensitivity analyses [40].

Influenza control is a challenge since it is difficult to predict the predominant strain that will be circulating each season. Successful vaccination program can be designed from a model which includes the drift process (via the emergence of new strains), the co-circulation of existing strains and pre-existing immunity in the population. A good model is able to repro‐ duce the herald wave phenomenon of strain persistence from one influenza season to the next in temperate regions [41].

health response to a pandemic. The foremost important contribution of mathematical epi‐ demiology to healthcare professionals is the concept of basic reproduction ratio and epidemic threshold. From a medical or public health perspective, models are instrumental in policymaking, service planning, risk assessment and monitoring performance of infection control programs. Decision of whom, how and when to quarantine, vaccinate and initiate therapy can be planned and implemented successfully from the outcomes of a model of high quality. The source of complexity in models is the occurrence of huge variability in infection profiles, parameter values and time scales. However, complexity ensures model accuracy. Selection of type of model and its degree of complexity depends on the purpose of modeling and questions to be addressed. Failure to understand, forecast and control an epidemic outbreak in a particular locality can indicate that some of the key disease parameters and elements of biological complexity might have been overlooked while developing the model. One under‐ lying reason for this may be traced to a key fundamental feature of mathematical model. Unless the model is tightly fitted to the experimental data, there remains a doubt on the reliability of the parameter estimates obtained from the model. At the end, it can be said that mathematical model helps in integrating several disease-related facts and factors into a cohesive structure, better visualization of a complex system, determining the plausibility of epidemiological explanations, prediction of unexpected interrelationships among empirical observations and

Unearthing the Complexities of Mathematical Modeling of Infectious Disease Transmission Dynamics

http://dx.doi.org/10.5772/57387

203

prediction of impact of changes in the system.

of of Institutions Kolkata, West Bengal, India

11(doi:10.1186/1471-2105-11-S1-S67).

Research, INFORMS (ISBN: 978-0-9843378-0-4).

and Gopa Roy Biswas

\*Address all correspondence to<EMAIL_ADDRESS>
NSHM College Of Pharmaceutical Technology, NSHM Knowledge Campus, Kolkata-Group

[1] A Sorathiya, A Bracciali and P Liò (2010). Formal reasoning on qualitative models of co-infection of HIV and Tuberculosis and HAART therapy*.* BMC Bioinformatics

[2] N B Dimitrov and L A M Dimitrov (2010). Mathematical approaches to infectious disease prediction and control. In Infectious Disease Models Tutorials in Operations

[3] S Funk, M Salathe and V A A Jansen (2010). Modeling the influence of human behav‐ ior on the spread of infectious diseases: a review J. R. Soc. Interface 7, 1247–1256.

**Author details**

**References**

Sutapa Biswas Majee\*

Recently, multi-scale models are being developed which will help in predicting virulence, transmissibility, at the population level, susceptibility or resistance to drugs without conduct‐ ing tough and time-consuming laboratory experiments. For development of these types of models, complete genome sequence of the pathogen should be made available, which will allow mapping from genotype to complex phenotypes.

Numerous modeling approaches contain data estimated from the literature and for such cases extensive sensitivity analysis may prove beneficial. In uni-variate sensitivity analysis, impact of variation of one parameter by a certain percent on the outcome of the model is measured while all other parameters are held constant. Such analysis can be graphically represented on a tornado plot. In multivariate analysis, impact of multiple parameters is studied through Monte Carlo simulations [38]. Other multilevel fitting schemes and Bayesian/Markov chain Monte Carlo frameworks may also be useful [30].


**3.2.1 Environment**

**Infections.** Systemic analysis of epidemiological data pointed towards an infectious agent as a potential cause for Hodgkin's lymphoma. Recognition of an association of infectious

Filipino, and Asian Indian in the US and in Asia reports HL incidence rates were quite low in all Asian subgroups but approximately double in US Asian. The consistently low rates of HL in Asians suggest genetic resistance to the disease development, possibly associated with HLA type. In addition environmental and lifestyle differences between the USA and Asia are important. In some study from Eastern Asia and among Chinese immigrants in North America indicate increasing incidence trends for HL being associated with westernization (Caporaso, Goldin, Anderson, & Landgren, 2009). International and interethnic differences and risk factor patterns in case-control data, implicate environmental

Incidence rate of HL are usually grater in male than in female (Correa & O'Conor, 1971). In western countries the young adult peak largely consist of nodular sclerosis tumors, whereas the rise at older ages are largely mixed cellularity and lymphocytic-depleted histology (Spitz, et al., 1986). Hodgkin's lymphoma tends to be more common in young adult with higher socio-economic classes (Correa & O'Conor, 1971). Pattern of low social class determinants in children and older adult with HL, the age groups at risk for mixed cellularity (MC), support involvement of underling infectious agent given intense exposure, and EBV is a likely candidate based on its high prevalence in these groups (Glaser & Jarrett, 1996). Based on different studies the most common subtype of HL in the most Asian countries such as Iran, Korea, Thailand, Japan is mixed cellularity and relative paucity of NS subtype, particularly in males (Glaser & Hsu, 2002), which seems to be related to the etiologic factors (environment and/or inheritance) of disease. Subtypes of HL in different

> **Thailand No (%)**

**NS** 10 (31.2) 26 (31.7) 58 (36.9) 29 (69) 70 (42.4) Up to 08 60 **MC** 16 (50) 38 (46.3) 64 (40.8) 2 (4.7) 51 (31) <10 15 **LD** 3 (9.3) 6 (7.3) 14 (8.9) 0 8 (5) 1 rare **LP** 1 (3.1) 4 (2.6) 18 (11.5) 2 (4.7) 18 (11) 6 10 **NLPHD** 2 (6.3) 8 (9.8) 3 (1.9) 3 (7.1) 8 (5) 5 5

A shift from MC-dominant histologic subtype of HL was observed over 20-year period within Japan, particularly in young adults (Aozasa, Ueda, Tamai, & Tsujimura, 1986). As NS and MC have been shown to have different environmental cofactors, including socioeconomic status and degree of *EBV* tumor-cell presence, geographic variation in HL is likely to reflect change in socioeconomically determined exposures whenever possible.

The differences in descriptive epidemiology of Hodgkin's lymphoma between children, young adults and older adults may reflect differences in etiology between these age groups.

**Infections.** Systemic analysis of epidemiological data pointed towards an infectious agent as a potential cause for Hodgkin's lymphoma. Recognition of an association of infectious

**Taiwan Japans**

**No (%)**

**US %**

**UK %**

influences in the etiology of HL (Glaser & Hsu, 2002).

countries are showed in table 4.

**No (%)**

**Korea No (%)**

Table 4. Subtypes of Hodgkin's disease in various countries

**HL Iran**

**3.2 Etiologic epidemiology**

**3.2.1 Environment**

mononucleosis with Hodgkin's disease predate the discovery of **EBV** (Richard F. Ambinder, 2007). Investigators have reported that EBV infectious mononucleosis is associated with a lifelong "immunologic scar" (Sauce, et al., 2007). The statistical analysis suggested that HL tended to occur 2.9 years after infectious mononucleosis (R.F. Ambinder, 2007). Remarkably, the change in lymphocyte cell population is sustained over years or longer (Richard F. Ambinder, 2007). There are new insights into infectious mononucleosis and disturbances in cellular immunity, new insight relating to the role that viruses may play in molecular pathogenesis of HL, an emerging appreciation of the increased incidence of HL in *HIV* and its relationship to immune suppression (Richard F. Ambinder, 2007). A role for suppression T cell suggested in the 1970s, and increasing evidence shows a role for this cells in suppressing antitumor immune responses (Hjalgrim, et al., 2007).

In western countries, about 50% of all cases of classical HL are *EBV* positive, means the virus is carried within the tumor cells. Detection of *EBV* in tumors in these region are least common in young adult disease. In some parts of Latin America, Africa, and Asia, the percentage is much higher with the percentage in children approaching 90- 100% (Glaser, et al., 1997; Zarate Osorno, Roman, Kingma, Meneses Garcia, & Jaffe, 1995). The MC subtype harboring *EBV* DNA in up to 70% of cases and the NS subtype being positive in 15-30% of cases (Brousset, et al., 1991). Also detection of *EBV* in HL in most Asian countries are less in young adults and are more detectable in children and older ages. Because of these differences infectious cofactors other than *EBV* have been suggested, but no consensus in support of any other particular association have emerge (Wilson, et al., 2007).

As we mentioned *EBV* induce immune suppression, and in an *EBV* positive person, MC subtype is more common (like *HIV* positive), therefore in MC subtype of HL, which is more common in Asian countries, immunodeficiency has more important role in comparison with other subtypes.

**HIV.** Hodgkin's lymphoma in the setting of *HIV* has distinctive features and is usually associated with EBV infection (Glaser, et al., 2003). HL in patients with *HIV* tends to present at an advanced stage with associated B symptoms and extra-nodal involvement and is most often a mixed cellularity subtype. Model fitting suggested that for persons with AIDS with moderate immunosuppretion at the onset of AIDS, HL risk was 15-fold higher than in the general population. Lower CD4 counts were associated with less risk, the risk fall as CD4 count fall (R.F. Ambinder, 2007).

#### **3.2.2 Inheritance**

The risk of developing HL among family member of patients affected by HL increase from three-to nine-fold (Haim, Cohen, & Robinson, 1982). One study showed a significant association between HL and parental consanguinity and pointed to the possible etiologic role of recessive inheritance (Abramson, Pridan, Sacks, Avitzour, & Peritz, 1978).

The relative risk for HL among first degree relatives of cases compared with controls was 3.1. Relative risks were higher in males compared with females, and in siblings of cases compared with parents and offspring. Identifying inherited susceptibility genes is an important step towards defining the pathway leading to development of HL and

Epidemiology of Lymphoid Malignancy in Asia 343

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understanding its etiology. There are many studies of somatic mutations in HL tumor cells, but although there are associations with HLA types, specific germline genes causing susceptibility have not yet been identified. On the other hand it is not known whether or how extrinsic risk factors interact with genetic susceptibility (Goldin, et al., 2005).

Oza et al. in the single study of HL-HLA relationship found that HLA-DPB1\*0301 inreased risk of HL in all ethnic groups, while HLA-DPB1\*0401 was associated with a lowered risk of HL in Japanese and Chinese and an elevated risk for US whites and Israelis (Oza, et al., 1994). Therefore; HLA-DPB1\*1401, or factors related to it, could explain some of the lower incidence of HL in certain Asian ethnic groups, although environmental factors involves as well and indicate that HL etiology is complex.