The Need for Systems Approaches for Precision Communications in Public Health

Figures & data

Table 1. Mechanisms that make populations and communities complex systems

Mechanism	Some of the complexities involved	Examples
How people directly interact and mix with each other	People do not interact equally with each other, but instead, their interactions (e.g., frequency, intensity, timing, nature) are guided by the time of day/year, their relationships (e.g., family versus friends versus co-workers), shared backgrounds, locations, ages, immigration/language status, and other factors.	College students can have more frequent interactions and interact in different ways than older adults; Certain recent immigrant communities may have more limited interactions with others due to language barriers and discrimination
What platforms/information people are exposed to	Different people can get information from different sources and platforms in different ways. The sources and platforms that they use depend on a variety of factors such as what their close contacts are using, their field of work, age, education, cultural background, main spoken language.	Teenagers and young adult may be more likely to use TikTok and some newer social media platforms than older adults; Location determines the local news channels that are available; Internet access, cable, and satellite TV subscriptions affect what channels/platforms are available
What platforms/information people prioritize/favor	People have favorite platforms through which they receive information (e.g., television, radio, social media, podcasts). These platforms can deliver different information in which people will prioritize particular sources (e.g., Fox News, CNN, Facebook, iHeart Radio, Twitter) they find to be most trustworthy or favorable.	Different social media platforms are favored by those of certain ages and political affiliations; Certain English as a second language communities may have their own newspapers that use a different language; Those in particular religious groups may view religious leaders as a primary source of information; Different people may view particular newscasters as more or less reliable
How people’s histories affect how they process and interpret information	Different people have different personal histories that can affect their pre-conceived notions and how they accept, process, and interpret information.	Those who have suffered racial discrimination or oppression may be less likely to trust institutions such as healthcare systems and governments; Different people are more or less trained in interpreting scientific studies
What actions people take based on information	Different people will use a given piece of information in different ways and exhibit different behaviors as result.	Upon hearing or reading the same information, one person may follow a CDC or WHO recommendation to wear face masks, another person may not.
What is the likelihood of people disseminating information	Different people may decide to further transmit information to others in different ways.	People can share information on social media, during direct conversation, or through their work positions and channels. For example, the CEO of a company can influence his/her employees.

Table 2. Mechanisms that make different communications channels complex systems

Mechanism	Some of the complexities involved	Examples
What is the format of the information on the channel	Different channels have specifications in terms of the type (e.g., text vs. graphical vs. audio vs. video) and amount of information that can be shared at a given time.	Twitter has a character limit for a given post, whereas Facebook and LinkedIn do not; A radio broadcast does not allow for text, graphical, or video information
What information is being presented to whom	Many of the channels, especially the newer ones, do not present the same information to everyone; what one sees can be based on one’s demographics and histories.	Social media channels use algorithms to determine what to show a given person; Advertising can be personalized and targeted; Internet search histories and location can determine what an Internet search returns
What is the history of the channel	The length of time that a channel has been in existence and the events that have occurred can affect the reputation of the channel and who trusts the channel.	Major network news outlets such as NBC, ABC, and CBS have much longer histories of being go-to sources than cable news; Scandals can affect the trustworthiness of an information source
When is the information being disseminated	The timing of the release of information can affect who sees the information and what they end up doing with it.	Daytime TV shows may be more likely to reach those who are not at work; Information that comes out soon after a major event may be more likely to affect the reaction to that major event
Who is placing the information on the channel	The information shared on the channel may reflect who runs the channel and that person’s background, personality, intentions, and oversight.	A governmental organization has the obligation to serve the taxpayers; A public corporation may have the obligation to serve its shareholders
Who are the target populations for the channel	Different platforms and their messaging may target different populations and communities.	TikTok tends to attract a younger demographic compared to Facebook; Certain news organizations appear to be targeting people of a certain political orientation

Table 3. Examples of sources that comprise health-related information

Source of Evidence	Strengths	Limitations	Example
Material studies in the laboratory	Can determine the physical properties of something	May not incorporate various human behavioral and social factors	Studies that show how many different particles face masks can filter out but do not account for how the person may wear the mask
Laboratory molecular and cellular studies	Isolate specific mechanisms	Situation may be very different when molecules or cells are viewed in isolation vs. part of the greater system	Laboratory studies show how well a virus can enter a cell, but the virus has to travel down through the respiratory tract to reach the cell
Laboratory animal studies	May be done when human studies are not yet possible	Results may not necessarily be applicable to humans	Ferret studies that have shown that humidity may affect transmission of respiratory viruses does not necessarily apply to humans
Anecdotes	Can raise questions for further exploration	Typically, does not qualify as scientific evidence; Not verifiable or validated	Stories of a person having an adverse event after a vaccine may not be verifiable
Case studies	Can raise questions for further exploration	Unclear how unusual the findings may be	Case studies showed that a majority of people infected with SARS-CoV-2 developed some sign of myocarditis but is it actually not that common in the population
Observational cohort studies	Evaluate what is happening in the real world under real world conditions; Can show trends, patterns, and associations	Cannot demonstrate cause and effect; May overlook many factors and complexities; can oversimplify situations	Observational cohort studies suggested that Vitamin D intake may be associated with lower COVID-19 severity but there were many potential confounding factors such as geographic location, use of other COVID-19 precautions, and ability to social distance
Randomized controlled trial (RCT)	Good at evaluating medications and treatments for an individual	Not as effective at evaluating more complex interventions that are affected by many factors external to an individual; Findings may not hold in the real world	An RCT of an antiviral can show the medication may affect the duration and severity of symptoms, on the other hand, an RCT of face mask use may not be able to measure and control for factors that affect efficacy such as where the person moved, how the person wore the mask, and how many others were wearing face masks
Data-driven statistical and machine learning studies	Can help identify trends, patterns, and associations in historic data that may not be obvious; Can help extrapolate these trends; Can help identify areas that need further exploration	Cannot determine cause-and-effect relationships; can oversimplify situations; Often assumes that historical trends will hold; The past does not necessarily predict the future	Studies in early 2020 tried to predict the subsequent course of the pandemic and the resulting mortality, however, conditions changed in the latter half of 2020 and again in 2021 and 2022
Mechanistic computer modeling studies	Able to pull together, integrate, and synthesize data and evidence from different sources; Attempts to represent the underlying mechanisms; Can run hypothetical scenarios; Can help determine what may happen should circumstances change; May forecast the future; Can help evaluate multiple layered interventions	Can be substantial variation in types and quality of models; The construction of the model may not always be readily understood; Dependent on how well the actual mechanisms are represented; Ability to replicate real-world situations can vary based on the construction of the model and accompanying assumptions	Computer models from 2020 showed the anticipated course of the pandemic and impact of COVID-19 vaccines but did not always account for the emergence of new variants

Table 4. How the communications needs change over the phases of a pandemic

Phase	Examples of Communication Issues/Needs
Pre- or Inter-pandemic/epidemic/outbreak
Surveillance	Help people understand the rationale/need for surveillance and investment into such activities
Threat identification	Help people distinguish between different levels of concern (e.g., how likely is a pathogen going to be a threat)
Prevention/preparedness	Help people understand need for prevention/preparedness measures even through the threat has not yet appeared
During outbreak/epidemic/pandemic
Discovery/characterization of pathogen	Help people realize the different characteristics of the pathogen and how these different characteristics may evolve over time
Intervention design/prioritization	Help people know why a given intervention is necessary and the relative strengths and limitations of the intervention and how these may change as more information emerges
Implementation/ramp up	Help people understand what is involved in the use of an intervention (e.g., face mask effectiveness depends on the types of masks used, how they are worn, and how many other people are wearing them)
Maintenance	Help people realize the importance of continuing to employ an intervention even when fatigue may occur
Policy/intervention troubleshooting	Help people interpret new emerging information and how that may affect use of an intervention (e.g., how should rare myocarditis cases affect trust in the COVID-19 mRNA vaccines)
Transition phase/new “chapters” and adjustment	Help people anticipate what changes in goals, policies, and interventions may be needed to usher in a new phase of the emergency (e.g., setting up a routine vaccination schedule to help transition a pandemic to a seasonal virus situation; implementing face mask use to prevent another surge)
De-escalation	Help people know when the intensity and use of an intervention can be decreased (e.g., when can face mask use be decreased)
Post-outbreak/epidemic/pandemic
Hot wash/post-outbreak/epidemic/pandemic review	Help people understand what went well and what did not and how to use this information in future outbreaks/epidemics/pandemics
Post-crisis steady state (e.g., “endemic”/seasonal)	Help people realize what policies and interventions need to be continued to prevent new and additional emergencies

Figure 1. Modeling is an iterative process.

Please use version ”Figure 1_Proof.png” as we made some minor edits for consistency