Modelling the COVID-19 pandemic in context: an international participatory approach. Issue 12 (23rd December 2020)

• Record Type: Journal Article
• Authors: Aguas, Ricardo; White, Lisa; Hupert, Nathaniel; Shretta, Rima; Pan-Ngum, Wirichada; Celhay, Olivier; Moldokmatova, Ainura; Arifi, Fatima; Mirzazadeh, Ali; Sharifi, Hamid; Adib, Keyrellous; Sahak, Mohammad Nadir; Franco, Caroline; Coutinho, Renato
• Other Names: author non-byline.; Ariana Proochista author non-byline.; Hancock Penny author non-byline.; Kraenkel Roberto A. author non-byline.; Saralamba Sompob author non-byline.; Luangasanatip Nantasit author non-byline.; Silal Sheetal Prakash author non-byline.; Norman Jared author non-byline.; Hounsell Rachel author non-byline.; Tun Sai author non-byline.; Aung Yu Nandar author non-byline.; Bakare Emmanuel A author non-byline.; Getachew Biniam author non-byline.; Adele Sandra author non-byline.; Omoleke Semeeh A. author non-byline.; Zaman Rashid U author non-byline.; Letchford Nicholas author non-byline.; Parker Daniel M. author non-byline.; Pokharel Sunil author non-byline.; Lata Dipti author non-byline.; Chen Siyu author non-byline.; Kyaw Shwe Sin author non-byline.; Lubis Inke N D author non-byline.; Alona Ivana author non-byline.; Medina John Robert C. author non-byline.; Mercado Chris Erwin G. author non-byline.; Eybpoosh Sana author non-byline.; Mamadu Ibrahim author non-byline.; Marzouk Manar author non-byline.; Colombi Nicole Feune de author non-byline.; Suárez-Idueta Lorena author non-byline.; Obando Francisco author non-byline.; Freitas Luzia author non-byline.; Klein Michael G. author non-byline.; Scales David author non-byline.; Aizhan Dooronbekova author non-byline.; Zhumalieva Chynar author non-byline.; Estebesova Aida author non-byline.; Mukambetov Aibek author non-byline.; Ibragimov Shamil author non-byline.; Kubatova Aisuluu author non-byline.; Chanthavialy Phetsavanh author non-byline.; Salim Amel H. author non-byline.; Venkatesan Sudhir author non-byline.; Sarin KC author non-byline.; Shrestha Priyanka author non-byline.; Saeedzai Sayed Ataullah author non-byline.; Hsieh Jenny author non-byline.; Soukavong Mick author non-byline.; Yunanda Yuki author non-byline.; Harsono Handoyo author non-byline.; Fariba Mahnaz Hossain author non-byline.; Mabombo Viviana author non-byline.; Advani Nicole author non-byline.; Jabin Nusrat author non-byline.; Naidoo Reshania author non-byline.; Wattanasri Parinda author non-byline.; Nwosu Amen-Patrick author non-byline.; Obiesie Sopuruchukwu author non-byline.;
• Is Part Of: BMJ global health. Volume 5:Issue 12(2020)
• Publication Date: 2020-12-23
• Subjects: health policy -- respiratory infections -- control strategies -- SARS; World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2020-003126
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.