Exploring the Seasonality of COVID-19

Project leads: Shengjie Lai

Team: Andy Tatem, Nick Ruktanonchai, Corrine Ruktanonchai, Alessandro Sorichetta, Eimear Cleary, Alexander Cunningham, Fatumah Atuhaire, Tim O’Riordan, Jessica Floyd, Alessandra Carioli

Funding: Bill and Melinda Gates Foundation

Start: Jan 2021
Completion: Dec 2022

Building on a series of inter-linked COVID-19 studies conducted by WorldPop, this project is working to improve our understanding of how seasonal factors such as mobility and temperature influence COVID-19 transmission. Outputs from the study are supporting COVID-19 intervention strategy design, health system planning and epidemic preparedness for secondary waves in different settings across the world.

We have produced a range of developments throughout the project. These include datasets that inform the changes of seasonal population mobility, international travel patterns and connected communities between countries and regions of the world, and potential COVID-19 transmission rates under different intervention scenarios, accounting for seasonal, demographic and socioeconomic factors. Where possible all of these developments, plus supporting code as well as training and presentation materials, are made available via open access, or distributed to key stakeholders for use and evaluation.

This study is providing timely and consolidated evidence and data for:

  • improving our understanding on the dependence of COVID-19 on seasonal factors,
  • tailoring time-dependent COVID-19 containment strategies using non-pharmaceutical interventions and their coordination between regions, and
  • health system planning and preparedness for both any new waves of COVID-19 and epidemic preparedness in general.

Additionally, we are integrating relevant datasets to adapt to the ongoing changes in the impact and control of COVID-19, thereby updating results and improving our models.

Our key contributions are to:

  • Assemble harmonized datasets, including seasonal population mobility, environment and climate, NPIs and adherence over time, COVID-19 cases.
  • Identify changes in travel patterns and connected communities of countries and regions driven by seasonal population mobility.
  • Compare datasets – using spatiotemporal models to quantify the dependence of local transmission on potentially interdependent seasonal factors (i.e., climate and population mobility), to identify the most important of these factors that are driving COVID-19 transmission and resurgence.
  • Develop models – propagating predicted seasonal dynamics of COVID-19 transmission in existing epidemiological models and accounting for sociodemographic context of mobility & risk.
  • Produce model scenarios; simulating transmission of COVID-19 in targeted regions of the world accounting for seasonal dynamics, and simulating interventions on these spatiotemporally variable landscapes., thereby providing data on COVID-19 resurgence risk through time for key countries, and recommendations on effective intervention strategies/key factors to target to limit resurgence risk in key countries.
  • Produce a transmission model code library.