Power your Surveillance Network with Qolty

Monitor patients remotely for symptoms


Disease surveillance is a global concern. In a world where international trade, migration of humans and animals, and ecological changes take place at a rapid pace, numerous infectious diseases and vectors have pandemic aptitudes. With life-threatening diseases spreading beyond borders, global health security is at risk, especially in developing countries. For instance, infectious viruses like Zika, SARS, and H5N1 have become a pandemic threat, revealing that adequate disease surveillance networks can save lives and economies. To set an example, it’s been documented that an effective surveillance system can decrease the magnitude of a SARS outbreak by one-third and its duration by one month (Mirza et al., 2013).

Disease surveillance networks have the potential to collect vital ecological and medical data and detect diseases in their early stages. A systematic review conducted by Choi and colleagues (2016) revealed that technologies could boost the capacities of standard disease surveillance systems. By integrating digital solutions, local and international networks can enhance rapid communication, engagement, early warning, and research, which will result in decreased disability, mortality, and poverty rates. To answer the newest demands of global health security, platforms like Qolty can help experts build effective surveillance networks and implement technological advancements in both practice and research.

Implement Disease Surveillance with Qolty
Disease Surveillance
How can we be helpful?
0 /

Disease Surveillance and Digital Solutions

Disease surveillance is essential for human-animal-ecosystem dynamics. Disease surveillance is defined as the ongoing task of collecting, analyzing and reporting data related to emerging infection outbreaks. From the accurate prediction of epidemics to the effective development of action plans, information can be utilized across different areas. One of the main applications surveillance systems have is an early warning, which is the process of informing and connecting individuals, institutions, and governments (Yang, 2017).

By implementing technological solutions into disease surveillance networks, experts can foster detection, prevention, and communication. Also, there’s growing evidence that effective surveillance systems can mitigate costs (Mirza et al., 2013). Note that originally, disease surveillance networks relied on printed reports managed by experts and volunteers, which made the data prone to errors and delays. New digital approaches – such as mobiles apps, wearables, sensors, web platforms, and syndromic surveillance systems – on the other hand, can only improve health outcomes (Global Infectious Disease Surveillance and Detection: Assessing the Challenges – Finding Solutions, Workshop Summary.). Therefore, Qolty enables the successful integration of health technologies to support visibility, coordination, recovery, and prevention of health crises

Conventionally, these assessments were administered through paper and pen method used in combination with pagers or electronic wristwatches (Delespaul et al. 1995). With the advancement in technology, electronic devices (PDA’s), and smartphone apps such as Qolty surpassed the traditional pen and paper technique. The surveys are usually short and completed within 1 to 2 minutes. The items are designed for prompt and easy data collection which usually comprise of open-ended questions, checklists or self-report Likert scales, and visual analog scales (Csikszentmihalyi et al. 2013).

Collection of data: The abundance of medical data worldwide can challenge data collection. Via ongoing monitoring of multiple sources, Qolty enables noise reduction and the automatic extraction of relevant information across web content, RSS feeds, media news, social media platforms (e.g., Twitter), contact rosters, email lists (e.g., ProMed), flight manifests, science records (e.g. OpenMRS), and other sources. Consequently, analytics modules support the analysis of big data and its hidden patterns. In addition, since the human input is essential for the verification of information, users can modify the collaboration settings of their network, invite specialists and witnesses, and allow new entries and feedback. Note that the increased use of mobile phones and apps facilitates data collection and validation across borders – with data being processed in real time and validated at the source. For instance, collaborators can enter data (e.g., number of deaths) via their devices (e.g., in a simple table with a user-friendly calendar).

Search optimization: Information should be processed efficiently. Since most surveillance systems are disease-specific (e.g., swine flu), Qolty can help parties explore a disease, population or region of interest. The implementation of data mining techniques leads to structured and optimized searches. Both symptom and quarantine monitoring are targeted. To set an example, sophisticated analytics, and data mining solutions can improve the information filtering of relevant measures and quests (e.g., bioterrorism). Note that learning algorithms are robust to past processing errors, which improves the future identification of early symptoms, geocoding, and routes of transmission.

Data integration: Data integration is another challenge which effective disease surveillance systems should overcome. Since digital solutions benefit data integration, Qolty can help experts take advantage of the newest information advancements. The system supports the screening, filtering, and integration of relevant information across various users, applications, and sources. The implementation of standard codes, on the other hand, allows users to track, synchronize, and change data from multiple databases (e.g., Excel tables, Google spreadsheets, and JavaScript) in order to support healthcare data exchange and interoperability.

Accuracy and accessibility: Technologies facilitate the collection and the analysis of information. Qolty provides accurate, integrated and validated information, which is monitored and analyzed by specialists 24/7. Data is not only accurate but accessible. The collection of data occurs in real time and can be processed in different languages translated into English, which makes information accessible beyond borders. Daily summary reports are also available. In addition, experts can train applications to process upcoming messages with possible mistakes (e.g., no commas or full stops) without changing user behaviors. Since many people report cases or symptoms via text messages or social media posts, the capability to analyze reports with syntax structures prone to errors becomes vital.

Visualization: Health technologies provide an interactive and engaging interface and software programs. Augmented reality improves visual experience. In fact, visualization, such as maps and reports, is another fundamental aspect Qolty supports. By producing regular graphs and extracting visuals, experts will be able to understand the particular disease of interest and its impact on global health. To be more precise, after collecting and integrating information about disease instances, experts can visualize that data on maps or graphs (according to location, time, duration, etc.). Visuals help parties spot and understand patterns at a glance.

Effective communication: Surveillance systems aim to improve global health. Therefore, communication and transparency become essential. From eyewitness reports to lab tests, Qolty brings people together. Real-time public health surveillance and availability of data (stored electronically and transmitted to the cloud) facilitate global health data exchange. The use of available Internet sources may lead to access to information, which hasn’t been censored by governments. Effective disease surveillance systems enhance communication via social networks, app notifications, emails, and free subscriptions. In addition, open-source tools can overcome literacy, language, and technological barriers. Experts can create apps (based either on built-in tools or new codes) that interact via voice and support VoIP. Note that confidentiality, one of the main principles of research management, is guaranteed.

Action steps: Tech solutions support not only data collection and analysis but real action steps, such as early warning, evaluation of current practices, and vaccines. By enhancing the continuous monitoring of datasets and overcoming potential disease surveillance challenges, Qolty can help experts create a disease surveillance network which supports an active investigation and effective prevention. The process of early detection is achieved through access to data and analysis of abundant demographic and geographic information (including visuals and maps of outbreaks). Proactive monitoring is essential. For example, if a contact shows any symptoms of an infection, findings are tracked to minimize continued disease spread. Analysists can flag events and set alerts to boost actions, as well as provide support and feedback. By using voice, GPS, web platforms, and SMS, experts can create an effective network, improve team communication, and build a response team to support public safety. Such features, including the use of simulations, can benefit activists and human rights organizations.

Digital perspectives: Digital solutions are reshaping global health. Note that, as explained above, the increased use of mobile phones and apps helps people send information, get notifications, and participate in surveys related to disease outbreaks at low infrastructure costs. Low infrastructure costs can benefit regions and economies in need. As a result, Qolty can help experts create a disease surveillance network which follows the One Health approach and embraces the future of digital health and clinical studies (Mackenzie et al., 2013). Note that apps are no longer reserved for programmers. For instance, anyone can build a unique message-based application with a user-friendly API and simple protocol (e.g., HTTP) to respond to crises or outbreaks. Apps are configurable for changing technologies and locations, while at the same time, data can be throttled to minimize potential tension with mobile companies.

Types of Disease Surveillance and Benefits of Active Surveillance

Since the health outcomes of humans, animals, and ecosystems are interconnected, disease surveillance systems can prevent pandemics. The global efforts to improve health outcomes in humans, animals, and ecosystems is known as One Health (Mackenzie et al., 2013). Note that many infectious diseases can spread across species, with 70% being zoonotic. Therefore, disease surveillance systems are designed to support the aims of the One Health approach. Note that surveillance can be classified in different groups according to four criteria. First of all, surveillance systems can vary according to their scope: systems can be divided into death surveillance, event-based surveillance, and syndromic surveillance. Targets also influence surveillance. For instance, surveillance can be community-based, lab-based or health facility-based. Although surveillance supports global health, coverage should be considered. Systems can monitor the whole population, high-risk population, or sentinels. Perhaps one of the most tech-relevant divisions is according to data collection methods: there’s active and passive surveillance. While passive systems rely on clinicians, who may notice a case, in active systems agencies, make outreach and actively search for cases (Yang, 2017).

Although there are different types of surveillance systems with various purposes (e.g., early warning and evaluation of measures across countries), most traditional systems are hierarchical. In any hierarchical system, a clinician spots and reports a case, authorities conduct epidemiological and lab investigation, and governments take actions steps (Morse, 2014), which often is time-consuming and prone to errors. Therefore, advanced platforms like Qolty implement technologies to foster active outreach and improve active surveillance. It’s interesting to mention that both traditional and digital networks rely on four crucial aspects: data collection, characterization, analysis, and dissemination. When it comes to dissemination, information can be a double-edged sword: while it can benefit travelers and locals, governments and economies may be affected by unrestricted access of inaccurate information (Global Infectious Disease Surveillance and Detection: Assessing the Challenges – Finding Solutions, Workshop Summary.).

Interestingly, the tech revolutions in healthcare and surveillance have led to various newly coined terms, as revealed by a comprehensive review conducted by Choi and colleagues (2016). Syndromic surveillance is one of the recent terms which refers to systems that detect disease outbreaks promptly. Biosurveillance relies on complex algorithms to identify threats before actual diagnoses. Infodemiology epidemiology, on the other hand, supports the distribution of information to inform public health. Infoveillance refers to longitudinal tracking and analyses of vital disease-related metrics. Digital surveillance opts to provide knowledge through the accurate analysis and distribution of digital data. Last but not least, real-time surveillance refers to the identification of any early signs, which can support measures and treatments.

The Applications of Disease Surveillance Systems: Practice and Research

Practice across critical regions across Asia and Africa reveals that surveillance systems can save lives. With all the benefits of infectious disease surveillance networks, the need for sophisticated surveillance networks and digital solutions is eminent. Big data can be utilized to explore the distribution of diseases, animal reservoirs, habitats, drug resistance of pathogens, natural disasters, and political factors (Yang, 2017). Accurate information can save lives and prevent catastrophes. It can also benefit economies, especially in developing countries and rural areas, and force legal regulations.

Disease surveillance and action steps cannot be analyzed separately. By providing critical information, disease surveillance systems support early warning and action programs. Surveillance networks can be used to:

  • Collect data and explore trends of a disease
  • Identify patterns, risks, and vectors, including additional factors, such as drug sales and the number of children absent from school
  • Improve early warning. Interestingly, an analysis conducted by Yang and colleagues (2017) revealed that the majority of early warning models include temporal, spatial, and regression algorithms. Note that modern technologies and methods, such as mathematical analyses and Monte Carlo techniques, can improve conventional early warning models.
  • Evaluate measures and policies, as well as guide the use of vaccines (Yang, 2017)
  • Support the implementation of control and prevention programs, at local, national, and international levels – at low infrastructure costs
  • Benefit ecosystems and One Health approaches by connecting a wide variety of specialists (e.g., scientists, politicians, and analysts) and people (Mackenzie, 2013)
  • Develop regulated protocols to answer the demands of international health regulations and data dissemination
  • Foster digital health practices, including bioinformatics, AI, coding, systems engineering, and statistics
  • Benefit research and pathogen discovery

As there are numerous applications of disease surveillance, which is an integrated factor for global health, Qolty can help experts concentrate their research on major aspects, such as:

Mass gatherings: Disease surveillance systems, including anticipatory and enhanced surveillance, are necessary to regulate mass gatherings, such as religious celebrations and sports events (Nsoesie et al., 2015). Note that researchers show that factors like close proximity may lead to outbreaks of both vaccine-preventable and non-vaccine-preventable conditions.

Global travel: Lower costs and access to travel may lead to emerging outbreaks. For instance, MERS, which was introduced to South Korea and spread across China, was brought by a traveler. Therefore, all data collected from mobile apps and wireless sensors is essential to support early warning and surveillance.

Food safety: Food safety is another global concern. Since humans, animals, and ecosystems are interconnected, bacteria, viruses, and parasites can enter species – with the foodborne route being the most common way of contamination (Mackenzie, 2013). A surveillance system can support the development of regulations and safety practices.

Wildlife: From hunting to farming, people are in close contact with animals. To set an example, the gray squirrels introduced from the US to the UK harmed the native red squirrels and became a pest to farmers, foresters, and conversationalists. Therefore, wildlife must be considered, given the increased pathogens that can infect humans and domestic animals.

Regions: Insufficient resources may challenge global health and disease surveillance. Therefore, any comprehensive system should support the mapping of poor and underrepresented areas and improve health outcomes worldwide. Digital tools can benefit text messaging, data exchange, and visualization. For instance, users can send messages, enter data or upload an existing file, which can be updated, visualized, and analyzed from everywhere across the globe. Note that live map and geospatial information can benefit information about resource allocations.

Disease Surveillance Networks and Future Perspectives

Technological solutions and healthcare practices are interconnected. The recent advancements in technology allow Qolty to support experts in the development of sophisticated study designs and disease surveillance systems. Data collection and integration across sources reveal impressive capabilities. Accurate information and visualization support the effective monitoring and analysis of infectious outbreaks, action steps, and resource allocations. As a result, disease surveillance systems can benefit early warning, evaluation of measures, and prevention of outbreaks.

In the end, the increased use of mobile phones allows people and governments from all over the globe to send information, report cases, and access data – all at decreased infrastructure costs. Big data, crowd-sourced information, and SMS become essential tools in surveillance. Online networks and apps enhance communication beyond borders and improve practices and research. Note that technology can boost laboratory advancements and pathogen discovery as well. In a nutshell, disease surveillance systems with their numerous capabilities and applications have the potential to improve global health and people’s well-being.


Delespaul PAEG. (1995). Assessing schizophrenia in daily life the experience sampling method. UPM, Universitaire Pers Maastricht, University Library, Maastricht University.

Stone AA, Shiffman S. (1994). Ecological momentary assessment (EMA) in behavorial medicine. Ann Behav Med. 16:199–202.

Csikszentmihalyi M, Mehl MR, Conner TS. (2013). Handbook of research methods for studying daily life. New York, NY: Guilford Publications.

Szalai, A. et al. (1975). The use of time: Daily activities of urban and suburban population in twelve countries. The Hague: Mouton.

Robinson, J. (1977). How Americans use time: A social-psychological analysis of everyday behavior. New York: Praeger.

Yarmey, D. (1979). The psychology of eyewitness testimony. New York: Free Press.

Campbell, A. (1976). Subjective measures of well-being. American Psychologist, 31, 117–124.

Shweder, R. (1975). How relevant is an individual difference theory of personality? Journal of Personality, 43, 455–484.

Miller G. (2012). The smartphone psychology manifesto. Perspect Psychol Sci 7: 221–237.

Cohn AM, Hunter-Reel D, Hagman BT, Mitchell J (2011). Promoting behavioral change from alcohol use through mobile technology: The future of ecological momentary assessment. Alcohol Clin Ex Res 12: 2209–15.

Shiffman S, Gwaltney CJ, Shields AL. (2007). Validation of ePRO measures and using ePRO to enhance validity. Presented at Annu. Meet. New Clin. Drug Eval. Unit, 47th, Boca Raton, FL

Wheeler L, Reis H. (1991). Self-recording of everyday life events: origins, types, and uses. J. Personal. 59:339–54

Bevans, G. (1913). How working men spend their time. New York: Columbia University Press.

Todd M, Armeli S, Tennen H, Carney MA, Ball SA, et al. (2005). Drinking to cope: a comparison of questionnaire and electronic diary reports. J. Stud. Alcohol 66:121–29

Niere K, Jerak A. (2004). Measurement of headache frequency, intensity and duration: comparison of patient report by questionnaire and headache diary. Physiother. Res. Int. 9:149–56

Taylor BC, Fried L, Kenardy J. (1990). The use of a real-time diary for data acquisition and processing. Behav. Res. Ther. 28:92–97.

Jonasson G, Carlsen K, Sodal A, Jonasson C, Mowinckel P. (1999). Patient compliance in a clinical trial with inhaled budesonide in children with mild asthma. Eur. Respir. J. 14:150– 54

Lewin, K. (1935). A dynamic theory of personality. New York: McGraw-Hill.

McCarberg BH. (2007). The treatment of breakthrough pain. Pain Med. 8: S8–13

Shiffman S, Engberg J, Paty JA, Perz W, Gnys M, et al. (1997a). A day at a time: predicting smoking lapse from daily urge. J. Abnorm. Psychol. 106:104–16

Schlundt DG, Johnson WG, Jarrell MP. (1985). A naturalistic functional analysis of eating behavior in bulimia and obesity. Adv. Behav. Res. Ther. 7:149–62

Shiffman S, Paty JA, Gnys M, Kassel JD, Hickcox M. (1996b). First lapses to smoking: withinsubjects analyses of real-time reports. J. Consult. Clin. Psychol. 64:366–79

Shiffman S, Gwaltney CJ, Balabanis M, Liu KS, Paty JA, et al. (2002). Immediate antecedents of cigarette smoking: an analysis from Ecological Momentary Assessment. J. Abnorm. Psychol. 111:531–45

Shiffman S, Paty JA. (2003). Using an electronic diary to capture frequent events. Presented at From Quality of Life to Patient Outcomes Assess.: Res. Agenda for a Paradigm Shift, Drug Inf. Assoc., Baltimore, MD

Kamarck TW, Shiffman S, Smithline L, Goodie JL, Paty J, et al. (1998). Effects of task strain, social conflict, and emotional activation on ambulatory cardiovascular activity: daily life consequences of recurring stress in a multiethnic adult sample. Health Psychol. 17:17–29

Hensley MJ, Chalmers A, Clover K, Gibson PG, Toneguzzi R, Lewis PR. (2003). Symptoms of asthma: comparison of a parent-completed retrospective questionnaire with a prospective daily symptom diary. Pediatr. Pulmonol. 36:509–13

Affleck G, Tennen H, Urrows S, Higgens P, Abeles M, et al. (1998). Fibromyalgia and women’s pursuit of personal goals: a daily process analysis. Health Psychol. 17:40–47

Shiffman S, Waters AJ. (2004). Negative affect and smoking lapses: a prospective analysis. J. Consult. Clin. Psychol. 72:192–201

Shiffman S, Hickcox M, Paty JA, Gnys M, Kassel JD, Richards T. (1997b). The abstinence violation effect following smoking lapses and temptations. Cogn. Ther. Res. 21:497–523

Sheftell FD, Dahlof CG, Brandes JL, Agosti R, Jones MW, Barrett PS. (2005). Two replicate randomized, double-blind, placebo-controlled trials of the time to onset of pain relief in the acute treatment of migraine with a fast-disintegrating/rapid-release formulation of sumatriptan tablets. Clin. Ther. 27:407–17

Muraven M, Collins RL, Morshimer ET, Shiffman S, Paty JA. (2005). The morning after: limit violations and the self-regulation of alcohol consumption. Psychol. Addict. Behav. 19:253–62

McCarthy DE, Piasecki TM, Fiore MC, Baker TB. (2006). Life before and after quitting smoking: an electronic diary study. J. Abnorm. Psychol. 115:454–66

Myin-Germeys I, van Os J, Schwartz JE, Stone AA, Delespaul PA. (2001). Emotional reactivity to daily life stress in psychosis. Arch. Gen. Psychiatry 58:1137–44

Feldman-Barrett L, Russell JA. (1998). Independence and bipolarity in the structure of current affect. J. Personal. Soc. Psychol. 74:967–84

Bolger N, Laurenceau J-P. (2005). Using diary methods to study marital and family processes. J. Family Psychol. 19:86–97

Larson R, Richards MH. (1994). Divergent Realities: The Emotional Lives of Mothers, Fathers and Adolescents. New York: Basic Books

Curry S, Marlatt GA, Gordon JR. (1987). Abstinence violation effect: validation of an attributional construct with smoking cessation. J. Consult. Clin. Psychol. 55:145–49

Ingrid Kramer, Claudia JP Simons, Jessica A Hartmann, Claudia Menne-Lothmann, Wolfgang Viechtbauer, Frenk Peeters, Koen Schruers, Alex L Bemmel, Inez Myin-Germeys, Philippe Delespaul, Jim Os, Marieke Wichers. (2014).A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial. World Psychiatry. 13(1): 68–77. doi:  10.1002/wps.20090

Newman MG, Erickson T, Przeworski A, Dzus E. (2003). Self-help and minimal-contact therapies for anxiety disorders: Is human contact necessary for therapeutic efficacy? J. Clin. Psychol. 59:251–74

Newman MG, Kenardy J, Herman S, Taylor CB. (1997). Comparison of cognitive behavioral treatment of panic disorder with computer-assisted brief cognitive behavior treatment. J. Consult. Clin. Psychol. 65:178–83

Norton M, Wonderlich SA, Myers T, Mitchell JE, Crosby RD. (2003). The use of palmtop computers in the treatment of bulimia nervosa. Eur. Eat. Disord. 11:231–42

Riley WT, Jerome A, Behar A, Weil J. (2002). Computer and manual self-help behavioral strategies for smoking reduction: initial feasibility and one-year follow-up. Nicotine Tobacco Res. 4:S183–88

Wichers M, Hartmann JA, Kramer IMA. (2011). Translating assessments of the film of daily life into person-tailored feedback interventions in depression. Acta Psychiatr Scand. 123:402–3.

Myin-Germeys I, Birchwood M, Kwapil T. (2011). From environment to therapy in psychosis: a real-world momentary assessment approach. Schizophr Bull; 37:244–7.

Trull TJ, Ebner-Priemer UW. (2009). Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: introduction to the special section. Psychol Assess; 21:457–62

Shiffman S, Stone AA, Hufford MR. (2008). Ecological momentary assessment. Annu Rev Clin Psychol; 4:1–32. doi: 10.1146/annurev.clinpsy.3.022806.091415

Myin-Germeys I, Oorschot M, Collip D. (2009). Experience sampling research in psychopathology: opening the black box of daily life. Psychol Med; 39:1533–47. doi:10.1017/S0033291708004947

Stone A, Shiffman S, Atienza A. (2007). The science of real-time data capture: self-reports in health research. New York, NY: Oxford University Press.

Bolger N, Laurenceau J-P. (2013). Intensive longitudinal methods: an introduction to diary and experience sampling research. New York, NY: Guilford Press.

Simons CJ, Hartmann JA, Kramer I. (2015). Effects of momentary self-monitoring on empowerment in a randomized controlled trial in patients with depression. Eur Psychiatry; 30:900 6. doi: 10.1016/j.eurpsy.2015.09.004

Tse WS, Bond AJ. (2002). Serotonergic intervention affects both social dominance and affiliative behavior. Psychopharmacology (Berl); 161:324-30.

Choi, J., Cho, Y., Shim, E., & Woo, H. (2016). Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health. Retrieved from https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-016-3893-0

Institute of Medicine (US) Forum on Microbial Threats. Global Infectious Disease Surveillance and Detection: Assessing the Challenges—Finding Solutions, Workshop Summary. Washington (DC): National Academies Press (US); 2007. 2, Surveillance Networks. Available from: https://www.ncbi.nlm.nih.gov/books/NBK52873/

Katz, R., Lopez, L., Annelli, J., Arthur, R., Carroll, D., Chapman, L, Cole, K., Gay, C., Lowe, D., Resnick, G., & Russell, K. (2010). U.S. Government engagement in support of global disease surveillance. BMC Public Health, 10 (1). Retrieved from https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-10-S1-S13

Mackenzie, J., Jeggo, M., Daszak, P., & Richt, J. (Eds) (2013). One Health: The Human-Animal-Environment Interfaces in Emerging Infectious Diseases. Food Safety and Security, and International and National Plans for Implementation of One Health Activities

Mirza, N., Reynolds, T., Coletta, M., Suda, K., Soyiri, I., Markle, A., & Streichert, L. (2013). Steps to a Sustainable Public Health Surveillance Enterprise. A Commentary from the International Society for Disease Surveillance. Online Journal of Public Health Informatics, 5 (2). Retrieved from http://doi.org/10.5210/ojphi.v5i2.4703

Morse, S. (2014). Public Health Disease Surveillance Networks. Retrieved from http://www.asmscience.org/content/journal/microbiolspec/10.1128/microbiolspec.OH-0002-2012

Nsoesie, E., Kluberg, S., Mekaru, S., Majumder, M., Khan, K., Hay, S., & Brownstein, J. (2015). New digital technologies for the surveillance of infectious diseases at mass gathering events. Clinical Microbiology and Infection, 21 (2), p. 134-140. Retrieved from https://www.sciencedirect.com/science/article/pii/S1198743X14001700

Yang, W. (2017). Early Warning for Infectious Disease Outbreak: Theory and Practice.

How can we be helpful?
0 /