Special Issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission

Published 24 January, 2020

Note: This special issue will no longer take any new submission as of October 12, 2020. All submissions submitted by October 11, 2020 will be processed, and included (if accepted) in the special issue. IDM does encourage continuing modeling study of COVID-19 pandemic, and welcome submissions to the regular issue.

A substantial number of people infected with a novel coronavirus have been reported recently. Its rapid spread from Wuhan, the capital city of Hubei province and one of the largest cities of the People’s Republic of China, to other Chinese provinces and countries, including South Korea, Japan, Thailand and the United States of America, represents a major cause of concern. Although our understanding of the transmissibility and severity of this novel coronavirus remains limited, much has been learned from past investigations into SARS and MERS outbreaks.

While the world is significantly better prepared to respond to the spread of pathogens with pandemic potential since the 2003 “Severe Acute Respiratory Syndrome” (SARS) outbreaks, such progress has not been achieved uniformly across nations. Further, the increasing trend in population density in cities as well as in global travel volume pose significant challenges for epidemic control. One lesson learnt from our past experience during the SARS outbreaks and the 2009 A/H1N1 influenza pandemic is that mathematical models and analyses, especially timely studies that are backed-up with data and sensitivity analyses, could, should and can play a key role in informing evidence-based decisions by health decision- and policy-makers. We believe that mathematical modelling community should be ready and make efforts to assist stakeholders in the decision-making processes. Efforts should be made to carefully communicate rigorous mathematical simulations and analyses, as already is happening with some model-based analyses being released as pre-prints. We also believe global collaboration and rapid dissemination of mathematical modelling research results about this new coronavirus is important.

For this purpose, we are editing a special issue entitled “Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission” for the international peer-reviewed journal Infectious Disease Modelling. The journal is co-published by Elsevier (World’s largest information analytics group) and China Science Publishing and Media (China's largest STM publisher and the publishing arm of the Chinese Academy of Science)Infectious Disease Modelling is a peer-reviewed open access journal, which has been indexed by PubMed, Scopus, DOAJ, EMbase databases, and is also under evaluation for SCI database. All the contents published with IDM will immediate available for free downloading and reading on ScienceDirect.

The special issue welcomes both original research articles and review articles in the broad subject area of the 2019 Wuhan Coronavirus transmission dynamics and spatiotemporal patterns. Articles can be focus on developing relevant methodologies, and/or applying cutting edge methodologies motivated by and applicable to the disease transmission dynamics and spatiotemporal patterns to inform its prevention and control.

Important Dates

  • Submission deadline: 12 October 2020
  • Infectious Disease Modeling will continue to welcome modeling studies of the COVID-19 pandemic into regular issue of the journal.

Submission Instructions

Please consult  “Guide for Authors” for more details about submission. We are committed to have the full review of any submission to this special issue complete within 10 working days. All accepted articles will be published online almost immediately in the regular journal issues to ensure rapid dissemination of your research, so that your accepted articles will be indexed immediately. The special issue will then be assembled when it is complete, and there will be a dedicated website for this special issue. 

Guest Editors

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