The Model Repository of the Models of Infectious Disease Agent Study

Cooley, Phillip; Roberts, Douglas J.; Bakalov, Vesselina; Bikmal, S.; Cantor, Susanna; Costandine, T.; Ganapathi, L.; Golla, Bharathi; Grubbs, George; Hollingsworth, Charles G.; Li, S.; Qin, Ying; Savage, William G.; Simoni, Diglio; Solano, Eric; Wagener, Diane K.

doi:10.1109/titb.2007.910354

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…The technology of ABMs has matured considerably over the last 20 years to the point at which many urban-scale, national-scale and even billion-agent planetary models have been developed and applied to urgent policy problems from smallpox bioterrorism to bird flu, swine flu, Ebola, Zika and the ongoing COVID-19 pandemic 92,93 . Substantial investment-including through the NIH Modelling of Infectious Disease Agent Study (https://midasnetwork.us/) 94 -has led to ABMs providing high-profile contributions to policymaking 15,[95][96][97][98][99] .…”

Section: Coupled Contagion Models With An Extension To Agent_zeromentioning

confidence: 99%

A review and agenda for integrated disease models including social and behavioural factors

et al. 2021

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Section: Coupled Contagion Models With An Extension To Agent_zeromentioning

confidence: 99%

A review and agenda for integrated disease models including social and behavioural factors

et al. 2021

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“…FluTE is written in C/C++ and is released under the GNU General Public License (GPLv3, see http://www.gnu.org/licenses/gpl.html ). The source code is available at http://www.csquid.org/software , https://www.epimodels.org/midas/flute.do , and the Models of Infectious Disease Agent Study (MIDAS) repository [26] . The software includes two source code files that are also freely distributable but may come with different licenses because they were written by others: one for the pseudorandom number generator (SIMD oriented Fast Mersenne Twister (SFMT) pseudorandom number generator [27] ) and one to generate binomially distributed random numbers (from Numerical Recipes in C [28] ).…”

Section: Modelmentioning

confidence: 99%

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

et al. 2010

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Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.

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“…The newly identified novel coronavirus is responsible for the outbreak of COVID-19 that has the potential for maximal death [2] . The effect of the pandemic (COVID-19) and its preventive measures, such as quarantine and social distancing, can be analyzed using data analysis, modeling, and representation techniques of data mining [3] , [4] . These techniques help in predicting disease’s behavior, thus providing the convenience of taking necessary preventive measures.…”

Section: Introductionmentioning

confidence: 99%

Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India

Mishra

Gupta

Dutta

2021

IEEE Trans. Comput. Soc. Syst.

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Coronavirus outbreak is one of the challenging pandemics for the entire human population on Earth. Techniques, such as the isolation of infected people and maintaining social distancing, are the only preventive measures against the pandemic. The actual estimation of the number of infected peoples with limited data is an indeterminate problem faced by data scientists. There are several techniques in the existing literature, including reproduction number and case fatality rate, for predicting the duration of a pandemic and infectious population. This article presents a case study of different techniques for analyzing, modeling, and representing the data associated with a pandemic such as COVID-19. We further propose an algorithm for estimating infection transmission states in a particular area. This work also presents an algorithm for estimating end time of a pandemic from the susceptible infectious and recovered model. Finally, this article presents the empirical and data analysis to study the impact of transmission probability, rate of contact, infectious, and susceptible population on the pandemic spread.

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The Model Repository of the Models of Infectious Disease Agent Study

Cited by 5 publications

References 19 publications

A review and agenda for integrated disease models including social and behavioural factors

A review and agenda for integrated disease models including social and behavioural factors

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India

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