**13. Conclusions**

Infectious diseases pose a great threat to human civilization and world economy. Therefore, constant efforts are being made to prevent their occurrence, recurrence and spread. There are certain infections which do not manifest themselves as definite symptoms in the infected host for a long period of time but can be highly lethal at the end. Research is focused on development of economically viable new intervention strategies for any type of infectious disease with the aid of mathematical modeling. Symbiotic and synergistic relationship should exist between mathematicians and biologists for detailed analysis of the biological processes involved in host-pathogen interactions. Mathematical models involve assumption, abstraction, simplifi‐ cation and description of the most complex system of infectious disease, by the use of language of mathematics. Each element of host and pathogen can be monitored and varied simultane‐ ously without actually performing any experiment to ascertain the role of the element in disease dynamics. Epidemiological modeling enables extrapolation of population behavior from individual behavior and long-term behavior from dynamics of early stage of infection following consideration of different epidemiological factors. Surveillance data, physicians' reports and data acquired from the hospitals are the sources for individual-to-individual chains of transmission. Despite the fact that mass human behavior becomes unpredictable at times, especially during a disaster, concerted rigorous approach adopted during model development can build up a strong framework for future planning. Extensive long-term data and refined mechanistic understanding of evolutionary and transmission dynamics has enriched the field of mathematical modeling and provided insights in shaping the global public 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 prediction of impact of changes in the system.
