Smart science that’s helped New Zealand successfully tackle Covid-19 could soon be turned against other viral nasties that kill hundreds of Kiwis each year.
Experts say the capacity and expertise labs around the country have built up in our pandemic response can next be used to combat infectious diseases like seasonal flu.
Otago University and ESR virologist Dr Jemma Geoghegan saw particular potential to use genome sequencing on all notifiable diseases – among them influenza and respiratory syncytial virus (RSV).
“There’s also potential for it with things like norovirus, because it’s clear the genomics can provide a rich source of information to deal with it.”
Decoding or “sequencing” the genetic jigsaw that is the SARS-CoV-2 virus’ genome has been a crucial part of the public health response to Covid-19 here and overseas.
Investigating the spread of the virus into a population can leave scientists with a spaghetti-like mess to untangle – but genomic sequencing can quickly tell them where a specific case came from, and pick it apart from other cases in the community.
A study led by Geoghegan – and just published in major scientific journal Nature Communications – set out how scientists sequenced hundreds of virus genomes during New Zealand’s first wave, between and February and May.
Despite the comparatively tiny size of Covid-19’s incursion here, the team reported 277 separate introductions of the virus, out of 649 cases analysed.
Upscaling that effort to track influenza cases across New Zealand would prove a big challenge – more than 200,000 Kiwis catch the flu each year, and at least 500 people die from it.
But Geoghegan said this work was done elsewhere.
“The Peter Doherty Institute in Melbourne, for example, sequences notifiable diseases and has an amazing influenza virus dataset, so I think we could do that here, too.”
She said the bulk of this sequencing could be done in regional labs, with a central hub that collated and shared the data.
“If we were having another sort of nationwide outbreak, it could be good to have a UK-style consortium, where all genomes are uploaded to a centralised database,” she said.
“Having a clearer picture of patterns of respiratory viruses here would be really helpful for understanding what can happen, while monitoring genetic diversity would tell us more about how, where and when flu is entering the country and circulating.”
ESR virologist Dr Sue Huang said, just like coronavirus, there were ways for New Zealand to sequence the whole genome of each influenza case.
“Genome-sequencing influenza cases is on ESR’s radar, building on the capability built for Covid-19,” she said.
“The whole-genome sequencing for flu viruses will inform us where the viruses comes from, how they spread within New Zealand in a geographic and temporal perspective.
“Most importantly, it would allow us to track how viruses change and whether such a change is significant for considering new vaccine strain for the following year.
“Furthermore, by sequencing the historic collection held by ESR we could study how and if influenza was introduced through travel historically as suggested by this year’s numbers.”
It comes as researchers just published data revealing the dramatic side-effect of New Zealand’s Covid-19 elimination strategy, with flu rates down 99.9 per cent compared with four years ago.
Labs involved in national surveillance detected only 500 cases up to September 27 – and 474 of them were recorded before lockdown.
Otago University epidemiologist Professor Michael Baker said several other pandemic lessons could be applied in future flu seasons.
They included people wearing masks on buses in winter, getting vaccinated, practising good hygiene, staying home or away from others when sick, and rolling out extra health precautions around nursing homes.
How NZ could catch next outbreak early
Meanwhile, another new study has found internet searches, Healthline calls and school absenteeism data could alert health officials to a disease outbreak days ahead of other systems.
ESR researchers have found these could have offered an early heads-up to the disastrous Havelock North campylobacter outbreak.
While that outbreak began on August 8, 2016, the full extent of it wasn’t known until nearly a week later – and a recent analysis suggested it could have ultimately caused 8000 infections.
The researchers’ modelling found that they could have detected an increase in cases up to five days before the outbreak was detected via traditional pathways.
The study’s lead author, Dr Mehnaz Adnan, said the data may have allowed for an earlier intervention to curb transmission in the community.
“Early outbreak detection and magnitude prediction is critical to outbreak control, but with any disease surveillance system, there are unavoidable delays before an outbreak can be detected and the community protected,” Adnan said.
“It can take a number of days between someone becoming infected, developing symptoms, seeking healthcare and a case being diagnosed and notified to health authorities.
“During this time people might use Google searches of their symptoms or call Healthline – and we can use that information.
“As previous studies have suggested, we wanted to see if we could utilise this data as an early signal to alert health officials earlier, enabling them to investigate potential outbreak sources ahead of traditional methods.”
Adnan said they are not advocating to replace traditional methods, but rather to complement them.
“These model predictions could fill a critical time-gap in existing surveillance based on notification of cases of disease.”
ESR runs the national notifiable disease surveillance system, EpiSurv, so was best placed to investigate how to support such a system.
But the researchers warn that further work is required to assess the place of such surveillance data sources and methods in routine public health practice.
“A number of key questions will need to be systematically investigated to establish the practical role of these methods and how they could be most effectively integrated into routine public health practice,” Adnan said.
“There are a number of hurdles, this type of non-traditional surveillance carries with it the workload required to interpret and respond to signals, which can be extensive.”
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