Productive disruption: opportunities and challenges for innovation in infectious disease surveillance

• Published in: BMJ global health 2018-01, Vol.3 (1), p.e000538-e000538
• Main Authors: Buckee, Caroline O., Cardenas, Maria I E, Corpuz, June, Ghosh, Arpita, Haque, Farhana, Karim, Jahirul, Mahmud, Ayesha S., Maude, Richard J, Mensah, Keitly, Motaze, Nkengafac Villyen, Nabaggala, Maria, Metcalf, Charlotte Jessica Eland, Mioramalala, Sedera Aurélien, Mubiru, Frank, Peak, Corey M., Pramanik, Santanu, Rakotondramanga, Jean Marius, Remera, Eric, Sinha, Ipsita, Sovannaroth, Siv, Tatem, Andrew J, Zaw, Win
• Format: Article
• Language: English
• Subjects: Age; Big Data; Disease control; Epidemics; Epidemiology; Global health; Health surveillance; Immunization; Infections; Infectious diseases; Influenza; Intervention; Low income groups; Pathogens; Population; Regions
• ISSN: 2059-7908; 2059-7908
• DOI: 10.1136/bmjgh-2017-000538

Thoughtful deployment of interpolation or geostatistical tools can be used to create smooth maps of burden or intervention efforts across space, also allowing extrapolation to unmeasured contexts.1 4 5 Autocorrelation models are also powerful tools, building on surveillance data to guide predictions about outbreaks of dengue, for example.6 Moving from statistical to mechanistic approaches, even if incidence reporting is erratic, dynamical signatures of the infectious process might still be detectable if additional data on features of cases are available, such as age, geographic location and gender. [...]the growth rate of an epidemic can be extracted from incidence, allowing estimates of the net reproduction number, or R0, which captures the degree to which an outbreak is expected to grow (R0 >1) or shrink (R0 1 then becomes possible—although maps of the locations and densities of rural populations (ie, denominator challenges) are also necessary. Infectious disease models can allow characteristics of the surveillance system, such as the magnitude of under-reporting, to be estimated where the susceptible population can be inferred (eg, via susceptible reconstruction).10 Where only syndromic surveillance is available, it may be possible to correct for background rates of focal syndromes to pull out the dynamics associated with a particular infection. Alternatively, simulation tools based on known epidemiological parameters of particular pathogens can establish the degree to which the data are reliable and forecast outbreaks and emergence events and/or the impact of interventions like vaccination (eg, roll-out of cholera vaccination to contain an epidemic).13 Complementing existing epidemiological information with new data sources While these analytical strategies can compensate for the limitations of different data quality issues, a range of promising new data streams are also available.