COVID-19 second wave risk: models for Ireland, Italy, US and Singapore

We've taken another look at ECDC data to date and updated the models for the US, Italy and Ireland with data to 13 June.  We've also added a model fitted to Singapore data.

To date, the same model structure has fitted every region and inferred rates of physical contact between those infected with the disease and the broader community have tallied qualitatively with mobility data from Apple and Google.  That continues to be true for Ireland, Italy and the United States.  

Now that many countries are easing restrictions, the risk of a second wave of infections is growing.  In the case of the US, model parameters are tending to suggest that a second wave could be nearer than in the other regions.  Every feasible way to reduce transmission should be considered and it is possible that relatively small measures such as widespread wearing of masks could make the difference between a manageable and unmanageable second wave.

We've added some factors to the scenarios tabs of the models that define how restrictions are lifted.  That makes it easier for you to change those (in Simulator>Set Parameters) and you can use them to make response surfaces.  We took Ireland as an example and estimated the number of reported deaths at end 2020 for a variety of 'new normal' levels of contact (m_normal) and periods over which we adjust from lockdown to those levels (t_normal).  

Response surface (contour plot) for projected Year End 2020 reported deaths in Ireland from COVID-19, as a function of 'new normal' levels of movement / transmission (m_normal) relative to baseline and the length of time over which we move from lockdown to new normal (t_normal).  While there is some sensitivity to t_normal, the main sensitivity is to m_normal (vertical axis).  Every reasonable step should be taken to keep this value as low as possible.

Here's hoping that our greater knowledge about COVID-19 puts us in a position to avoid the less favourable regions on this diagram.

We added the Singapore model at the request of a customer there.  Singapore was highly praised in the early stages of the outbreak, with a small number of cases, extensive testing and vigilance and a very low mortality rate from COVID-19.  However an outbreak in dormitories used by migrant workers led to a spike in cases and a period of lockdown referred to as the 'circuit breaker'.  Mortality rates remain low and this is attributed to both compliance and the young age profile of the majority of cases / migrant workers.  To fit the data, we had to allow for a burst of increased contact (the breakout in dormitories) and the model now fits the data well.

All models are available here as usual.  All you need to run them is the Excel file and Dynochem.