Integrative modelling approaches for COVID-19 and beyond

We will build novel modelling and analytic frameworks to rigorously integrate multiple infectious disease datastreams.  Our frameworks will design optimal sampling schemes for contact tracing studies, infectious disease surveillance, and allocation of control measures for the current pandemic and provide an analytic infrastructure responding future outbreaks of other novel pathogens.

Dr. Max Lau, Assistant Professor, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University

The current COVID-19 pandemic reveals considerable scope for improving outbreak preparedness and response. Specific challenges include: (1) a lack of robust, whole-system approaches to optimize use of multiple sources of highly resolved and heterogeneous outbreak data to obtain fine-grained understanding of transmission dynamics, (2) inadequate understanding of optimal long-term vaccination strategies, (3) a lack of systematic understanding of under-reporting patterns in space and time, hindering scientific inference, and therefore, resource allocation and control measures implementation, and (4) a lack of optimized designs for key sampling tools such as contact tracing, particularly in an on-going, rapidly growing outbreak size.

Multiple datastreams have become increasingly available throughout the pandemic (e.g., individual-level epidemiological and geolocation data, contact tracing data, serological data, mobility data, and local vaccination coverage data). These data provide opportunities for addressing the challenges above. However, current approaches remain largely ad-hoc and lack whole-system thinking, optimization, or evaluation.

Our project will address these specific challenges aforementioned.

Along with the development of our specific aims, we will develop and disseminate comprehensive and user-friendly software packages (in R) implementing our frameworks for use by the scientific community.

Taken together, our proposal will provide important tools for designing optimal sampling schemes in contact tracing studies, surveillance approaches and control measures (including population-wide vaccination) for epidemic outbreaks. These tools are useful for managing the current COVID-19 pandemic and, more broadly, would better equip us in responding to future outbreaks of other novel pathogens.

In both the short and long terms, our analytic frameworks and results will enable agile, data-based public health policy to guide focused designs of sampling schemes to perform contact tracing studies, target surveillance and control measures (including wide-scale vaccination) for the current COVID-19 pandemic and, more broadly, future outbreaks of recurrent or other novel pathogens. We will accomplish this by proposing a new integrative modelling framework which is able to rigorously synthesize multiple sources of outbreak data (including unique individual-level surveillance data for COVID-19 in Georgia, USA and Victoria, Australia), extracting maximal information relevant for outbreak response and preparedness. We will also collaborate closely with Georgia Department of Public Health (USA) and Department of Health and Human Services (Victoria, Australia) on our project.

Our results will be submitted to peer-reviewed internationally recognized journals. Our compute code will be made publicly available (e.g. using Github).

We will develop comprehensive and user-friendly software packages (in R) implementing our frameworks, which can be readily used by the scientific and public health communities to better respond to the current COVID-19 pandemic and prepare for future outbreaks of other pathogens.

Our proposal will provide whole-system analytic approaches and tools for improving response to current COVID-19 pandemic and preparedness for future outbreaks of novel pathogens, by improving contact tracing study designs, surveillance approaches and control measures (including population-wide vaccination) for epidemic outbreaks in various demographic and geographic settings.

Along with the development of our methods, we will also develop and disseminate comprehensive and user-friendly software packages (in R) implementing our developed methods. Our goal is to make our methods easily accessible and usable for a broad range of audience with different levels of proficiency in computer programming. 

We will follow an open-source, open-development philosophy. Open-source means that all code from our research is freely available on our Github online repositories. This facilitates access to and installation of these tools by external users. Open-development means that the ongoing process of code building, testing, and revision is made public in real time. This allows for external users to see the changes to our code as they evolve, track issues that note software bugs or feature requests, and contribute code that may be merged into our work. 

In the next one year, we will continue to collaborate closely with Health departments in Georgia, USA and in Victoria, Australia, who will provide the research team with detailed surveillance data and provide comments to our model outcomes. Then, we will also seek to collaborate with other institutes (e.g. Clinton Health Access Initiative) to refine and scale up our methods and software so that they could be useful for health agencies in low- and medium-income countries.

We believe the success of our project needs to be evaluated by at least 2 metrics:

(1) publications: we believe important work should be made known to peers in the scientific community, and we will seek to publish our work at internationally recognized peer-reviewed journals. Our previous work has been published at top journals including Proceedings of National Academy of Sciences of the United States of America.

(2) usability of our tools for end-users with different backgrounds: we believe important work should be made available and easily usable for a wider range of end-users, including researchers and practitioners in the community. Therefore, we seek to develop and disseminate open-source and user-friendly software packages (in R) implementing our developed methods.  We will work with collaborators including Health Departments and public health agencies in different countries to make sure our methods will be useful and easily deployable for them.

Mostly financial in terms of supporting key personnel to perform and scale our work.

Emory University, USA

We would like to partner with Clinton Health Access Initiative who has has great connection with local public health agencies in low- and middle-income countries. We believe such a partnership will help deliver our products to many end-users in these countries who are in great need of useful and robust analytical tools for preparing for and managing pandemic outbreaks. Clinton Health Access Initiative will also be able to provide us valuable feedback on how we may refine our products to better suit these countries.