The path out of a pandemic

Norman Swan: You're listening to RN's Health Report with me, Norman Swan.

So let's get down to this debate about whether we should target elimination of spread in Australia. The modelling released by the government last week doesn't help much, apart from reminding us how bad COVID-19 could be. Professor Jodie McVernon led the modelling exercise for the government at the Doherty Institute in Melbourne.

Jodie McVernon: What we were trying to achieve with this modelling was to give our government some perspective on what this epidemic might look like without control, and then to think about how we could use the various public health measures available to us in the absence of definitive treatments or vaccines to reduce its impact. And the models that we had developed in the preparedness phase were expressly focused on the impact on the healthcare system, and being able to ensure that we would have sufficient capacity in the healthcare system to manage cases, particularly severe cases, was a real priority.

All of us as modellers were putting scenarios to our governments, those of us who were working with policy, that were jaw-dropping. It was very hard to countenance this threat. I think all of us saw the lockdown in Wuhan and thought, well, obviously we could never do that here, and then in a very short space of time we realised this was part of the toolkit. You know, we'd had a discussion about what social distancing would look like in Australia, and most pandemic plans talked about things like school closure or less intrusive measures but talked about how economically costly those measures were, and suddenly we were in a ballpark that was completely different. The problem was clearly one nobody wanted to have, but the solutions really became universally clear that this needed a really combined suite of interventions.

Norman Swan: Was there a lightbulb moment for you where you were really shocked?

Jodie McVernon: Most of us in the modelling community were really, really worried early on and I think one of the things that worried us was the public complacency in many situations, and we've seen that spill out in policy in other parts of the world. I think when something is so bad and so hard to believe, it's easier to wish it away. But seeing then the further epidemics rolling out in other parts of the world made it absolutely clear that this was not a bad dream.

Norman Swan: Professor Jodie McVernon of the Doherty Institute.

It's important to appreciate that our pandemic planning, like New Zealand's, had been based on influenza, which helps to explain the slow ramp-up of control measures rather than going hard early. For a while the government appeared to be stuck in the flu mode of thinking, but the COVID-19 virus isn't the flu, it's a SARS-like virus and needs tough, prolonged action with the tantalising prospect, perhaps, like SARS, of elimination of spread within our borders, assuming our borders stay closed for many months.

The difference though with SARS-CoV-2 is that it spreads from asymptomatic people who can be hard to find. Associate Professor Kamalini Lokuge is in the Research School of Public Health at the Australian National University. There is probably no Australian public health researcher with the range and depth of on-the-ground experience of controlling outbreaks and the health consequences of humanitarian crises. For example, she was heavily involved with Ebola control in Africa. Kamalini Lokuge is a strong proponent, when there is no vaccine, no treatment and a non-immune population, of aiming for elimination of spread, having seen it work on the ground, and also argues that modelling won't help you much in getting there. What's required is traditional epidemic control.

Kamalini Lokuge: What we need to look at, Norman, for achieving elimination and staying there, is particularly how do we make sure we are finding out about community transmission of COVID-19. So the way we do that is to expand our testing capacity to do very effective, thorough contact tracing, so following up of those who may have been in contact, and quarantining and effectively managing those contacts. Modelling is a part of that process of knowing how many we might need to look at, where we need to look, but really the key to controlling the disease is the public health response, and by the public health response I also mean the public's response. And the Australian community has, through taking on these very strong measures, bought us, the public health expert community, time for us to put in place those measures. And we need to recognise the value of that gift they've given us and really work hard to make sure that we have the systems in place that allow us to know we've eliminated disease and then make sure we know, once we start lifting containment measures, that we are still on top of it.

Norman Swan: Associate Professor Kamalini Lokuge.

Having said that modelling has limited value in working towards elimination, a group at the University of Sydney has indeed modelled how we get to elimination of spread in Australia, and social distancing is the core strategy. Professor Mikhail Prokopenko is director of the Centre for Complex Systems at Sydney University.

Mikhail Prokopenko: We tried to focus on modelling covid at the scale of all of Australia but using an individual-based approach. So essentially we have in a digital form 24 million agents, each of which represents anonymously a single individual and distributions of households, household compositions, places of work and schools, to a large level of detail. Then once the pandemic starts, we try to model every single day by estimating interactions within different social contexts, households, neighbours, communities, working groups, schools, different grades and so on. Then we can estimate the dynamics and jumps of the new daily cases and correlate it with the actual plan lines.

Norman Swan: And what do you find when you plug in for, say, social distancing and effectiveness of current policies?

Mikhail Prokopenko: So this model allows us to specifically evaluate different degrees of compliance with social distancing and different degrees of compliance with case isolation. We've focused on comparing compliance with social distancing between, say, 70%, 80%, 90% of the population following these rules, and one interesting result was that there is a sharp divide, a transition between 70% compliance and 80%. So if we as a population follow the 80% or, better, 90% compliance, we could hope to get the numbers of cases down by July to pretty much near zero trajectory. However, if we relax it and stay on a 70% trajectory, the numbers don't even go down, they keep rising, even after some inflection point. What it means is that we can't really relax what we have been doing so far in the near future, so the models show us that we are pretty much at the peak, not only at the incidence peak, the new cases, but we are pretty much as we speak sitting on the prevalence peak, so the active cases are probably reducing. So if we are continuing on this path, we are quite likely to get to a really controlled situation by July. If we relax it, the curve will start shooting up.

Norman Swan: One of the arguments is that you could mitigate that by a very wide testing regime, more effective isolation and quarantining and contact tracing.

Mikhail Prokopenko: I understand that it is a necessary part but it would not be sufficient if we only stay with case isolation and home quarantine and tracing immediate contacts, and the reason is that there are those asymptomatic cases which do have probability of passing this infection in a stealthy, silent way. If we just reduce our intervention to pure mitigation, like what you just said, that would not be sufficient. We have to realise, if I use this analogy of playing a football game, we started this game against an opponent which overplayed us and we were conceding goals left and right, and now we are getting to half-time and we are almost equalised, but should we start the second half and again relax, we will be, at best, just maintaining control, we will end up in extra time and who wants to be in extra time. So what I'm saying is we have to be vigilant, we have to go on the offensive, and that means we have to be aggressively stopping these social interactions and maintaining social distancing. Then, by the end of maybe June, by early July we should be in a winning position.

Norman Swan: What does winning look like when the virus is still around?

Mikhail Prokopenko: That is also a very good question because we are in a preferential position here, being isolated naturally geographically from land traffic, and if we maintain this international travel restrictions on air, we should be able to get back our economy to some decent level, maybe not 100% level but to some decent level and maintain that for as long as needed to get an efficient vaccine available. Our model showed that even a three-day delay in introducing the tough social distancing measures means that three weeks need to be added at the end of this suppression period. So essentially have a week for a day, approximately.

Norman Swan: Just to clarify that, what you're saying is for every three days delay in getting to lockdown, you get three weeks extension to the epidemic at the other end?

Mikhail Prokopenko: That's right, basically it's an approximate equation here which came out of the model…for one extra day of a delay you get one extra week at the end of the epidemic that we need to be in this social distancing, almost in lockdown.

Norman Swan: Just pursuing that for a moment, our community transmission levels are at low levels, doesn't that make us like New Zealand, that we are actually at low numbers and we've got the chance to turn it down quickly, or is that just wishful thinking on my part?

Mikhail Prokopenko: It's a good situation to be in, but you could still multiply those numbers to a sufficient level. You know, in modelling there is this old saying that all models are wrong but some are more useful than others. For example, in the model I was describing, the one which we've done at the University of Sydney, there is a clear divide between 70% compliance and 80% compliance. It's a transition. So when you have those sharp divisions, it means you've found a gem which clearly could be used in decision-making, and that is something which could be immediately actioned by the society. One message here is that it's not just reliant on the government, it's our own social behaviour that has to change, and change to some extent which will get us out of it.

Norman Swan: Professor Mikhail Prokopenko is director of the Centre for Complex Systems at the University of Sydney.