Session 3 of the RSS Special Topic Meeting on Covid-19 Transmission: Replies to the discussion

Entire editions of academic journals are dedicated to infectious disease modelling efforts while proper use of data to inform the modelling has been emphasised only recently (e.g. Held et al., 2020). The importance of data deserves highlighting and it is noteworthy that one of the most detailed and often analysed data sets in the field dates back to a measles outbreak in 1861 (Aaby et al., 2021). Without useful data, we will not be able to estimate the susceptible and asymptomatic proportions of the population. Strengthening and improving national and intergovernmental (coordinated by bodies such as ECDC and WHO) disease surveillance and monitoring systems allows for improved early disease outbreak detection. Such disease surveillance systems include both mandatory case reporting of notifiable disease, sentinel surveillance systems, and also internet and news media, under the umbrella of epidemic intelligence services. Disease surveillance requires certain amounts of man power and resources to function and systems have seen increases in technological capacity in recent years (Groseclose & Buckeridge, 2017; Hulth et al., 2010). Resources needed for ‘infodemic’ management also reduces the amount of human effort available for surveillance activities. Time series of infectious disease cases typically arising from a surveillance system can easily be modelled using the framework we used and presented. However, if the underlying data is flawed, so too will be the outputs. We are cognisant of the adage ‘garbage in, garbage out’. While we are aware of many funding opportunities for COVID-19 modelling, it is unclear how much emergency grant support has been given to strengthening current and future data gathering and storing infrastructure. Utilising existing data mechanisms rather than ‘re-inventing the wheel’ is paramount. Relatedly, there has recently been an attempt at re-branding the data-focused parts of infectious disease surveillance as ‘outbreak analytics’ (Polonsky et al., 2019). In our own work examining the effect of travel restrictions to neighbouring regions on cases in Switzerland we have recently considered both Italian and French case data (see Grimée et al., 2022, for an initial analysis of some of the regions) and have experienced two matters that caused us to consider the data in further detail and not simply model it as-is. The first is that certain case counts in Italian regions show changes from one day to the next which seem unrealistic.