1. Modelling the COVID-19 pandemic in context: an international participatory approach
- Author
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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, Ariana, Proochista, Hancock, Penny, Kraenkel, Roberto A, Saralamba, Sompob, Luangasanatip, Nantasit, Silal, Sheetal Prakash, Norman, Jared, Hounsell, Rachel, Tun, Sai, Aung, Yu Nandar, Emmanuel, A Bakare, Getachew, Biniam, Adele, Sandra, Omoleke, Semeeh A, Zaman, Rashid U, Letchford, Nicholas, Parker, Daniel M, Pokharel, Sunil, Lata, Dipti, Chen, Siyu, Kyaw, Shwe Sin, Lubis, Inke ND, Alona, Ivana, Medina, John Robert C, Mercado, Chris Erwin G, Eybpoosh, Sana, Mamadu, Ibrahim, Marzouk, Manar, de Colombi, Nicole Feune, Suárez-Idueta, Lorena, Obando, Francisco, Freitas, Luzia, Klein, Michael G, Scales, David, Aizhan, Dooronbekova, Zhumalieva, Chynar, Estebesova, Aida, Mukambetov, Aibek, Ibragimov, Shamil, Kubatova, Aisuluu, Chanthavialy, Phetsavanh, Salim, Amel H, Venkatesan, Sudhir, Sarin, KC, Shrestha, Priyanka, Saeedzai, Sayed Ataullah, Hsieh, Jenny, Soukavong, Mick, Yunanda, Yuki, Harsono, Handoyo, Fariba, Mahnaz Hossain, Mabombo, Viviana, Advani, Nicole, Jabin, Nusrat, Naidoo, Reshania, Wattanasri, Parinda, Nwosu, Amen-Patrick, and Obiesie, Sopuruchukwu
- Subjects
Health Services and Systems ,Public Health ,Health Sciences ,Generic health relevance ,Good Health and Well Being ,COVID-19 ,Culture ,Delivery of Health Care ,Global Health ,Health Policy ,Humans ,Internationality ,Models ,Theoretical ,Pandemics ,Research ,SARS-CoV-2 ,Social Class ,Uncertainty ,health policy ,respiratory infections ,control strategies ,SARS ,CoMo Consortium ,Health services and systems ,Public health - 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.
- Published
- 2020