Think back to some of the things you heard in 2020 about Covid-19: details such as “risk of death” and “incubation period”; the potential for “super-spreading events”; and the fact that before symptoms arise, transmission will occur. Evidence emerged in mid-January that the Covid 19 outbreak in Wuhan was much larger than initial reports indicated, and we learned how Wuhan’s subsequent lockdown resulted in a transmission decline. What links these early results together? All of these results have to do with disease modeling, which should become an important part of the Covid 19 response.
It helps us to take the information we have, make some plausible assumptions based on that understanding, and then look at the rational consequences of those assumptions. To understand what could be driving the trends we see, we can then compare our findings to available data sets.
The public was not able to see the findings of the models during previous epidemics, such as swine flu in 2009 and Ebola in 2014-15, until they were later published in scientific papers.
In comparison, online dashboards have been regularly produced by Covid-19 researchers so that individuals can track transmission rates and compare potential scenarios, while also making pre-print reports readily accessible.
In their attempts to recognize the latest strains of coronavirus found in the U.K. Researchers exchanged real-time modeling studies of genetic data and case patterns with South Africa, with platforms such as Nextstrain helping us to see how these variants spread globally. Covid-19 has shown us that health is not just a biology problem | Devi SridharContinue readingDespite these advances, the pandemic has shown that there is much much to do. Ideally, outbreak research should be quick, accurate, and open to the public.
But the demands of Covid 19’s real-time research, which many scientists have carried out without dedicated funding in their spare time, will force difficult decisions.
Will researchers prioritize government and public health agencies’ updating scenarios, write comprehensive papers explaining their processes, or assist others to adapt models to answer other questions? Those concerns are not new, but they have been given new urgency by the pandemic.
In the U.S., for example, volunteers operated the most comprehensive Covid 19 databases. The pandemic demonstrated inefficient and impractical modeling and epidemic analysis features and made it evident that there is a clear need for improvement. Mathematical principles have now become part of daily conversations, in addition to reporting on unique modeling studies. Journalists have started to think more deeply about the complexities of epidemics, whether they speak about reproductive statistics, data lags, or how vaccinations could protect the unvaccinated through’ herd immunity.’ I never thought I would receive media requests before the influenza outbreak to address a statistical parameter such as “K” that quantifies the super-spread capacity. Sadly, problems have also been identified.
Some modeling results were widely misinterpreted, especially in the early stages of the pandemic, such as headlines in March that said half of the UK could already be infected. Research organizations have had to deal with media commentators in the summer and fall, who deceived the public with reports that the pandemic was over and ignored warnings about the potential for a massive second wave. Given previous disease waves in Europe, in the absence of prevention measures, there can be little doubt that Covid-19 would have had disastrous consequences for our health systems.
In response to the epidemics, populations around the world have changed their actions, but the extent of this unprecedented shift-and its effect on spread-was extremely difficult to forecast early last year.
While infections such as Ebola and Sars have historically contributed to behavioral changes, Covid-19 caused a shutdown in society on a scale not seen since the 1918 flu pandemic. Researchers also needed to observe the dynamics of social activity in addition to modeling the spread of disease.
Thanks to new digital footprints, they have been able to do so in more detail than ever before, gaining unprecedented insights into individual and group responses.