~Written by Theresa Majeski (Contact: firstname.lastname@example.org; Twitter: @theresamajeski)
Technology is progressively becoming a bigger part of our lives. This holds true in high-income countries and in low- and middle-income countries. By 2012, three quarters of the world’s population had gained access to mobile phones, pushing mobile communications to a new level. Of the over 6 billion mobile subscriptions in use worldwide in 2012, 5 billion of them were in developing countries. The Pew Research Center’s Spring 2014 Global Attitudes survey indicated that 84% of people owned a mobile phone in the 32 emerging and developing nations polled. Internet access is also increasing in low- and middle-income countries. The 2014 Pew Research Center survey indicated that the Internet was at least occasionally used by a median of 44% of people living in the polled countries.
The increase in Internet and mobile phone access has significant implications for how infectious diseases can be better tracked around the world. Although robust and validated traditional methods of data collection rely on established sources like governments, hospitals, environmental, or census data and thus suffer from limitations such as latency, high cost and financial barriers to care. An example of a traditional infectious disease data collection method is the US Centers for Disease Control and Prevention’s (CDC) influenza-like illness (ILI) surveillance system. This system has been the primary method of measuring national influenza activity for decades but suffers from limitations such as differences in laboratory practices, and patient populations seen by different providers, making straightforward comparisons between regions challenging. On an international scale, the WHO receives infectious disease reports from its technical institutions and organizations. However, these data are limited to areas within the WHO’s reach and may not capture outbreaks until they reach a large enough scale.
Compared to traditional global infectious diseases data collection methods, crowdsourcing data allows researchers to gather data in near real-time, as individuals are diagnosed or even before diagnosis in some instances. Furthermore, getting individuals involved in infectious disease reporting helps people become more aware of and involved in their own health. Crowdsourcing infectious disease data provides previously hard to gather information about disease dynamics such as contact patterns and the impact of the social environment. Crowd-sourced data does have some limitations, including data validation and low specificity.
Internet-based applications have resulted in new crowd-sourced infectious disease tracking websites. One example is HealthMap. HealthMap is a freely available website (and mobile app) developed by Boston Children’s Hospital which brings together informal online sources of infectious disease monitoring and surveillance. HealthMap crowd-sources data from libraries, governments, international travelers, online news aggregators, eyewitness reports, expert-curated discussions, and validated official reports to generate a comprehensive worldwide view of global infectious diseases. With HealthMap you can get a worldwide view of what is happening and also sort by twelve disease categories to see what is happening within your local area.
Another crowd-sourced infectious disease tracking platform was Google’s Flu Trends, and also their Dengue Trends. Google was using search pattern data to estimate incidence of influenza and dengue in various parts of the world. Google’s Flu Trends was designed to be a syndromic influenza surveillance system acting complementary to established methods, such as CDC’s surveillance. Google shut down Flu Trends after 2014 due to various concerns about the validity of the data. As an initial venture into using big data to predict infectious diseases, Flu (and Dengue) Trends have provided information that researchers can use to improve future big data efforts.
With the increase of mobile phone access around the world, organizations have started using short message service (SMS), also known as text messaging, as a method of infectious disease reporting and surveillance. Text messaging can be used for infectious disease reporting and surveillance in emergency situations where regular communication channels may have been disrupted. After a 2009 earthquake in Sichuan province, China, regular public health communication channels were damaged. The Chinese Center for Disease Control and Prevention distributed solar powered mobile phones to local health-care agencies in affected areas. The phones were pre-loaded with necessary software and one week after delivery, the number of reports being filed returned to pre-earthquake levels. Mobile phone reporting accounted for as much as 52.9% of total cases reported in the affected areas during about a two-month time period after the earthquake.
Text message infectious disease reporting and surveillance is also useful in non-emergency settings. In many malaria-endemic areas of Africa, health system infrastructure is poor which results in a communication gap between health services managers, health care workers, and patients. With the rapid expansion and affordability of mobile phone services, using text-messaging systems can improve malaria control. Text messages containing surveillance information, supply tracking information and information on patients’ proper use of antimalarial medications can be sent from malaria control managers out in the field to health system managers. Text messaging can also be sent by health workers to patients to remind them of medication adherence and for post-treatment review. Many text message based interventions exist, but there is a current lack of peer-reviewed studies to determine the true efficacy of text message based intervention programs.
Increasing global access to the Internet and mobile phones is changing the way infectious diseases are reported and how surveillance is conducted. Moving towards crowd-sourced infectious disease reporting allows for a wider geographical reach to underserved populations that may encounter outbreaks, which go undetected for a delayed period. While crowdsourcing such data does have limitations, more companies than ever are working on using big data and crowd-sourced data in a reliable way to inform the world about the presence of infectious diseases.