Big Data for Disease Control

Interdisciplinary approaches to data linkage and management

 

Shona Jane Lee

Investigating Networks of Zoonosis InnovationCentre for African Studies

• Intro to ‘Big Data’ in the context of infectious disease control

• Achieving interdisciplinarity and data linkage across disciplines and sectors - a One Health approach

• African Trypanosomiasis (AT) and the challenge of data management for Neglected Zoonotic Diseases

• My research – tracing technological innovations and data linkage and management for AT surveillance and control in rural Uganda

What is ‘Big Data’?

• ‘Big Data’ generally understood in terms of its ‘volume, variety and velocity’, however size isn’t everything…

• More about the methods of analysis, and fundamentally networked nature of the data in question – its ‘relationality’ to other data

• Value derived from patterns and connections that can be drawn between different sources, and the fresh insights this can reveal through novel methods of analysis

Big Data for Global Health & Development

• UN’s High Level Panel 2013: calls for a ‘Data revolution’ to achieve Post-2015 MDG targets – highlights importance of good data for effective policy

• Measuring impact - Allows comparisons to be made (for example between prevalence rates of disease prior to and after intervention campaigns).

• Warning! Data may be plentiful but is often:• Unstructured• Collected with varying methods and units of measurement• Inaccessible• Analysed differently between groups or individuals

Can lead to misleading interpretations…

An opportunity for disease control?

• Challenges for infectious disease surveillance and control (particularly zoonoses) surround the complex forces:

• Bringing together large datasets collected by multiple stakeholders and disciplines with various objectives and ways of collecting/managing data (vector control, livestock, public health, pharma, parasitology, epidemiology, social science etc.).

• Challenges associated with poor data collection and management in developing countries (which predominantly shoulder the burden of such diseases)

• Poor indicators and disputed methods of analysis among analysts

One Health

‘One Health’ paradigm

• Interdependency of human, animal and ecosystem health

• “addressing NZDs requires collaborative, cross-sectoral efforts of human and animal health systems and a multidisciplinary approach that considers the complexities of the eco systems where humans and animals coexist” - WHO

Okello, et al. 2014

One Health

• One Health built on a premise of collaboration, interdisciplinarity and linkage between sectors

• Big Data works on the premise of linking large data sets from different sources and drawing patterns from these links

BUT

• Beyond being openly available, data needs to be aggregated, standardised, compatible with and comparable to other data, and analysed within the context of other data.

• Challenge: collaborative networks between veterinary, medical and environmental disciplines remain compartmentalised, and constrained by the inflexibility of its actors

African Trypanosomiasisa.k.a ‘Sleeping Sickness’ –

Human African Trypanosomiasis

• Two strains:• T.b. gambiense – chronic, transmitted between

humans via tsetse fly bite, mainly in West and Central Africa

• T.b. rhodesiense – acute form, zoonotic – transmitted from animals (usually domestic livestock) to humans via tsetse fly bite, limted to East Africa

Uganda the only country where both

strains are endemic

Mapping

• Terrain• Vector distribution & density• Prevalence• Infrastructure• Demographic Factors

Modelling

Modelling

Modelling

Modelling

Challenges

Modelling

Challenges

• Modelling

Challenge

• How to translate this complex evidence base to others in your field?

• How do you translate it to those outwith your own field? Necessary for an integrated approach

• How do you translate it into actionable Policy??!

• Sometimes the data requires streamlining, simplifying, or analysing in different ways in order to produce something manageable and achievable for policy – balancing act for analysts.

• “you put garbage in, you get garbage out”…

• Need high quality, up to date data

Dealing with Data in the Absence of Active Screening

mHealth for One Health?

“provide access to further professional veterinary assistance in local areas, improve herd management and the ability to share data with key stakeholders” – Cojengo, 2014

Case Study: HAT control in Uganda

HAT poses several challenges to data collection/management and therefore policy for control:

• Different strains, requires different approaches and collaboration of more stakeholders (veterinary, parasitology, medicine, pharma, vector control etc.)

• Poor livestock data (in terms of numbers, movement and disease prevalence)

• Poor human prevalence data owing to passive surveillance approach, poor education among health staff on SS and under-reporting

• Scant data on tsetse fly population density and distribution

• Poor data management and ICT infrastructure

Case Study: HAT control in Uganda

My study will examine several questions, the following of which relate particularly to innovative approaches to data management:

• How are technological innovations in disease surveillance adopted and used by practitioners? • Ethnography of technology acceptance among veterinary health

workers trialling Cojengo’s Vet Africa tool• Mapping and network analysis of data linkage across health network• Assess feasibility of introducing mHealth approach into this system to

improve data linkage and management to enhance passive surveillance and better improve efforts to map and model disease risk

• How successful has the One Health framework been in facilitating interdisciplinary approaches and taking advantage of big data for Trypanosomiasis control?• SNA of data and resourcing pooling between disciplines and sectors

Records from Bugiri Hospital Sleeping Sickness treatment centre (Berrang-Ford et al., 2006)

Questions?