Mathematical models describing the COVID-19 pandemic
- Funded by Fundação de Amparo à Pesquisa do Estado de São Paulo [São Paulo Research Foundation] (FAPESP)
- Total publications:0 publications
Grant number: 20/14357-1
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Key facts
Disease
COVID-19Start & end year
20212023Funder
Fundação de Amparo à Pesquisa do Estado de São Paulo [São Paulo Research Foundation] (FAPESP)Principal Investigator
N/A
Research Location
BrazilLead Research Institution
Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos CamposResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease transmission dynamics
Special Interest Tags
Data Management and Data Sharing
Study Type
Clinical
Clinical Trial Details
Not applicable
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
The objective of this project is to propose new mathematical models to describe the COVID-19 pandemic, which will be valid for different regions of the world, at different territorial levels (countries, states, municipalities). An automatic methodology to calibrate the models, without requiring intervention from experts, will be proposed, such that the analysis could be performed even in regions with limited financial resources. New techniques for trend analysis will be developed, in order to provide relevant information to health authorities. Predictions will be performed concerning the future behavior of the pandemic, with special focus on the number of nursing beds and intensive care units (ICUs). The models to be proposed will be such that they may be generalized to other epidemics in the future. The analyses will use information from the websites of the European Center for Disease Prevention and Control (ECDC), Johns Hopkins University, São Paulo State Department of Health and Brasil.io Project. The results will be assessed through theoretical analyses, comparisons with similar results from the literature and, mainly, comparison between simulated and real results. Articles will be published in journals and conferences. At least two master's theses will be supervised.