Contact tracing is critical to controlling COVID-19, but most protocols only “forward-trace” to notify people who were recently exposed. Using a stochastic branching-process model, we show that “bidirectional” tracing to identify infector individuals and their other infectees robustly improves outbreak control, reducing the effective reproduction number (Reff) by at least ~0.3 while dramatically increasing resilience to low case ascertainment and test sensitivity. Adding smartphone-based exposure notification can further reduce Reff by 0.25, but only if nearly all smartphones can detect exposure events. Our results suggest that with or without digital approaches, implementing bidirectional tracing will enable health agencies to control COVID-19 more effectively without requiring high-cost interventions.
Ohio State University
"Dynamical Survival Analysis for COVID-19 Predictions in Ohio"
Over the last several weeks many mathematicians, statisticians and data scientists have found themselves involved with various efforts in response to the public health crisis caused by the COVID-19 pandemic. Did predictive modeling really help with COVID preparedness and decision making? Following up on my earlier lectures on the topic over the summer, I will try to give a perspective of how various mathematical methods turned out to work (or not) in practical settings of the daily predictions of the pandemic size in Ohio. In particular, I will briefly outline some new ideas and possible improvements in the methodology of 'dynamic survival analysis' developed by the OSU COVID response team to help predict COVID hospital burden.
James Cook University
"Delaying the COVID-19 epidemic in Australia: Evaluating the effectiveness of international travel bans"
Following the outbreak of novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and the disease named COVID-19, in Wuhan, China in late 2019, countries have implemented different interventions such as travel bans to slow the spread of this novel virus. This brief report evaluates the effect of travel bans imposed to prevent COVID-19 importation in the Australian context. We developed a stochastic meta-population model to capture the global dynamics and spread of COVID-19. By adjusting our model to capture the travel bans imposed globally and in Australia, the predicted COVID-19 cases imported to Australia were evaluated in comparison to observed imported cases. Our modelling results closely aligned with observed cases in Australia and elsewhere. We observed a 79% reduction in COVID-19 importation and a delay of the COVID-19 outbreak in Australia by approximately one month. Further projection of COVID-19 to May 2020 showed spread patterns depending on the basic reproduction number. Imposing the travel ban was effective in delaying widespread transmission of COVID-19. However, strengthening of the domestic control measures is needed to prevent Australia from becoming another epicentre.This report has shown the importance of border closure to pandemic control.
David JD Earn
"1918 vs 2020: Influenza vs COVID-19"
Comparisons are constantly being made between the 1918 influenza pandemic and the present COVID-19 pandemic. I will discuss our previous work on influenza pandemics, and and the tools we have used to understand the temporal patterns of those outbreaks. Applying similar tools to the COVID-19 pandemic is easier in some respects and harder in others. I will describe our current approach to modelling the spread of COVID-19, and some of the challenges and limitations of epidemic forecasting.