Despite decades of progress in lowering the burden of many communicable diseases, such pathogens remain troublingly prevalent. In some cases, such pathogens are resurgent, such as falling vaccination rates, the rise of anti—microbial resistance, and emerging infections stemming from ecosystem disruption and exposures in the animal—human interface. Despite the critical importance of effective prevention and control of communicable diseases to advancing human health, study of underlying epidemiology and the outcomes of interventions poses major challenges when pursued with traditional data collection mechanisms. For example, while contact networks (and associated contact durations) are widely recognized as important contributors to communicable disease spread, effective measurement of the structure and characteristics of such networks and their dynamics is notoriously difficult using traditional instruments, and often prohibitively burdensome and error—prone. Similarly, while knowledge of the spatial location and mobility patterns of an individual are known to offer an important proxy for direct contact network data (and can be of central importance for understanding the effects of pathogen reservoirs), self—reporting of even rough location patterns, like that for network contacts, is often highly inaccurate.
Often outbreaks of communicable diseases is further shaped in important ways by evolving risk perception, changes in attitudes towards vaccination, changes in contact and mobility patterns due to social distancing, or alterations to hygienic practices. Timely and ongoing measurement of such alternations in perception and behavior is typically infeasibly burdensome using typical instrument. For many infections, understanding the size and characteristics of the pool of subclinical carriers and infectives is challenging, due to the lack of contact with the health care system. Within the context of many communicable (and some subclinical) illnesses, even understanding observed changes in incident case counts can be challenging. For example, an uptick in the counts of reported illnesses over a given period could be representative of higher underlying infection incidence in the population, but could instead represent elevated care—seeking by individuals (for example, possibly stimulated by enhanced perception of risk).
Typically one of the foremost motivations for studying communicable pathogens is the desire to create more effective efforts to control or prevent the spread of such pathogens. When considering traditional epidemiological collection methods, a final set of challenges mentioned here revolve around understanding the impact of such interventions. When interventions designed to lower incidence are not successful, there is often an acutely felt need to understand why the intervention fell short of of its promise: whether it was due to the failure to alter the targeted causal pathways, or due to compensatory effects in other pathways. For example, if no decrease in incident case counts is observed in the target demographic following an outbreak response intervention campaign, there is a natural desire to understand whether the enhanced vaccine levels failed to secure such a decrease due to elevated care—seeking levels, higher contact rates, reduced hygienic practices (potentially borne of lower risk perception), or other factors. When broad interventions undertaken in the communicable disease area are successful in achieving health outcomes, often there is a keen need to understand why such success has been secured: whether it is due to changes in hygienic behavior, mixing and/or mobility patterns, care seeking, or other factors.
Ethica provides a powerful platform for addressing the challenges above by greatly easing the effort involved in measuring factors that are unreliable, burdensome, or infeasible to measure via traditional mechanisms. Through Bluetooth technology, Ethica can automatically track inter—participant proximity at multi—minute resolution, thereby supporting an understanding of how close and how long participants are together, and thereby supporting an estimation of the potential for person—to—person transmission. Tools such as (Assisted) GPS and WiFi can be used to capture a participant’s location and mobility patterns both outside and inside buildings, thereby enhancing understanding of the risk of exposure to pathogen reservoirs. Such locationing services can be further used to detect occurrences of care seeking.
In addition, the capacity of the app to issue Ecological Momentary Assessments (EMAs) to participants can be used to provide powerful insights into the intentions behind observed changes in behaviour, for example, whether app—observed changes in contact patterns or care seeking are indicative of altered risk perception. In light of the high burden of subclinical infection associated with many infectious diseases, the capacity to enquire with the user concerning less acute symptoms, or to allow the user to report them automatically, can be important to eliciting an understanding such subclinical burden. Given the dermatological manifestations of many communicable diseases, the capacity to use on—phone camera to photograph rashes, prodromes, pox, and other dermatologic features can further add considerable insight and confirmation to participant reporting.
In the policy context, Ethica’s ability to observe post—intervention changes across multiple causal pathways can offer a formidable tool for understanding why an intervention succeeds or fails, thereby greatly enhancing learning from intervention experiencing, and consequently setting the stage for trialing of successively more effective interventions.
When used in the communicable disease area, Ethica can secure great insights by its ability to elicit each three major data collection modalities:
Sensor data to quantify key factors that are traditionally very difficult to measure such as contact and mobility patterns
Proactive self—reporting for subclinical symptomology such as discomfort or rashes
Ecological momentary assessments (EMAs) in the form of micro—surveys
The capacity to tap into this wide variety of data can provide a spectrum of insights that are traditionally hard to secure, from underlying drivers for observed changes in incident case counts to drivers of or barriers to success of interventions. Such information can offer much—needed leverage for policy makers seeking to navigate the increasingly difficult terrain of communicable diseases.
Example Problems and Questions That Can be Addressed
Assessing how a given intervention would be likely to lower the transmission of the infection in the population.
Understanding how self—reported risk attitude changes during an outbreak.
Understanding how automatically measured risk behaviour changes during an outbreak.
Identifying the role that contact duration and type (proximity, type of relation) plays in contributing to the spread of infection.
Identifying likely individuals who may be carriers or who could be asymptomatically infected.
Identifying likely reservoirs of pathogen on surfaces.
To what degree do variations in humidity or temperature account for risk of infection?
Understanding the association between pathogen burden and handwashing.
How much and how soon does the intervention affect different pathways involving risk factors (e.g., contact diversity, frequency and duration; etc.) and protective factors (hygeine; vaccination; use of personal protective equipment; cleaning and disinfection), etc.
With dynamic modeling:
How would various interventions (enhancing handwashing compliance, upon—admission patient testing and potential isolation, patient or clinical cohorting) be likely to change the spread of infection within the facility population?
For some pathogens (e.g., droplet or airborne pathogens), automatic detection of contact networks.
For other pathogens (e.g., sexually transmitted or fluid—borne pathogens):
- Automatic detection of places where exposure may take place (often an indication of contact).
- Support for manually recording such contacts.
Automatic detection of exposure to locations associated with surfaces that many carry pathogen.
Recording of subclinical symptomology (which may be important to better judge timing of infection, initiation of asymptomatic shedding, etc.)
Support for easy self—reporting of level of risk concern.
Support for easy self—reporting of level of risk and protective behaviours (e.g., condom use, washing of hands)