Modeling infectious diseases: Understanding social connectivity to control infectious diseases

Contagious diseases and their impacts can be modulated to a higher extent by implementing proper mathematical or statistical models that comprehend connectivity and the spreading patterns among populations. An approach has been presented throughout this paper to find the optimal connectivity. And thus, this is possible only by analyzing the models of the network formation and comparing them with the real-world data. The study uses the connectivity coefficient for improving the infectious disease modelling and testing the Susceptible-Infectious-Removed stochastic model. In addition, the results of the proposed model are discussed in the context of quantification of risk as it helps trace and avoid infections to prevent any further outbreak of the diseases.

[1]  Hae-Wol Cho,et al.  A Tale of Two Fields: Mathematical and Statistical Modeling of Infectious Diseases , 2011, Osong public health and research perspectives.

[2]  J. Yorke,et al.  A Deterministic Model for Gonorrhea in a Nonhomogeneous Population , 1976 .

[3]  L. Mertz.  Quick Thinking Turns out Low-Cost Ventilators , 2020, IEEE Pulse.

[4]  Steven Riley,et al.  Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome , 2019, Computational and Structural Biotechnology Journal.

[5]  Martina Morris,et al.  Epidemiology and Social Networks: , 1993 .

[6]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[7]  Mimmo Iannelli,et al.  Mathematical modeling of epidemics , 2014 .

[8]  Pradipta Maji,et al.  Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images , 2015, IEEE Transactions on Image Processing.

[9]  Aaron Clauset,et al.  Scale-free networks are rare , 2018, Nature Communications.

[10]  A. Fauci,et al.  Emerging Infectious Diseases: Threats to Human Health and Global Stability , 2013, PLoS pathogens.

[11]  Ramon Huerta,et al.  Contact tracing and epidemics control in social networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  P. Klepac,et al.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study , 2020, The Lancet Infectious Diseases.

[13]  Araxi O. Urrutia,et al.  Conditional expression explains molecular evolution of social genes in a microbe , 2019, Nature Communications.

[14]  Bernice A. Pescosolido,et al.  The role of social networks in health, illness, disease and healing: the accepting present, the forgotten past, and the dangerous potential for a complacent future , 2002 .

[15]  Ramananda Ningthoujam,et al.  WHO statement – “Older people are at highest risk from COVID-19”: Should the hypothesis be corroborated or rejected? , 2020, Medical Hypotheses.

[16]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[17]  Wenbin Chen,et al.  Dynamic models for Coronavirus Disease 2019 and data analysis , 2020, Mathematical methods in the applied sciences.

[18]  Linhao Linhao Zhong Zhong,et al.  Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model , 2020, Ieee Access.

[19]  Hsinchun Chen,et al.  Prospective Infectious Disease Outbreak Detection Using Markov Switching Models , 2010, IEEE Transactions on Knowledge and Data Engineering.

[20]  A. A. Zaidan,et al.  Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases , 2021, Journal of Ambient Intelligence and Humanized Computing.

[21]  F. Karim,et al.  Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study , 2021, Journal of Medical Internet Research.

[22]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Hemant D. Tagare,et al.  Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images , 2006, IEEE Transactions on Medical Imaging.

[24]  Bilwaj Gaonkar,et al.  CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns , 2016, IEEE Transactions on Medical Imaging.

[25]  Majid Sarrafzadeh,et al.  A Prediction Model for Functional Outcomes in Spinal Cord Disorder Patients Using Gaussian Process Regression , 2016, IEEE Journal of Biomedical and Health Informatics.

[26]  B. B. Zaidan,et al.  New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR , 2021, Journal of Ambient Intelligence and Humanized Computing.

[27]  Peter J. Basser,et al.  A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI , 2003, IEEE Transactions on Medical Imaging.

[28]  Faryad Darabi Sahneh,et al.  Contact Adaption During Epidemics: A Multilayer Network Formulation Approach , 2017, IEEE Transactions on Network Science and Engineering.

[29]  Ping Niu,et al.  The novel coronavirus outbreak in Wuhan, China , 2020, Global Health Research and Policy.

[30]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[31]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .