Paula Moraga

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<p class="text-center" style = 'font-size: 56px; line-height:1.5; font-weight:bold;'>Data science for decision-making in public health</p>

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Paula Moraga, Ph.D.<br>
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Asst. Professor of Statistics
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King Abdullah University of Science and Technology (KAUST), Saudi Arabia
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<a href='http://twitter.com/Paula_Moraga_' target='_blank'> <i class='fa fa-twitter fa-fw'></i>&nbsp; @Paula_Moraga_</a><br>
<a href='https://Paula-Moraga.github.io/' target='_blank'> <i class='fa fa-globe fa-fw'></i>&nbsp; www.PaulaMoraga.com</a><br>

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## https://www.paulamoraga.com/book-geospatial/

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# Open data and collaborative analytical tools for decision-making in public health

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## Health surveillance

## Open and reliable data

## Collaborative analytical tools

## Communication and dissemination

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# One Health

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<div style="color: gray; height: 20px;bottom:80px;left: 30px;position: fixed;">
<br><br>
Marston, et al. 2014.<br>
Science Transl Medicine<br>
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# Global travel

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<br><br>
https://vimeo.com/natsaero<br>
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# It is important to acknowledge connectivity between people, animals, and their shared environment and work together to solve global health problems

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## To solve global health problems, it is important to acknowledge this connectivity between people, animals, and their shared environment and to work together with multidisciplinary teams of scientists

Show case studies where I collaborated with multidisciplinary temas in tropical disease mapping research
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# Data

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# To solve global health problems, we need data. But we do not need just any data

# We need reliable, relevant, timely and detailed data
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## Need reliable, relevant, timely and detailed data

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# Disaggregated demographic data

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- We need data to know how different groups are doing<br> (e.g., who is at risk of diseases?)

- Population registers and censuses provide information on fertility, mortality and migration

- Demographic data should be disaggregated by gender, age, geographic location and other characteristics

- This is essential for addressing<br> the needs of all groups including the most vulnerable groups which are often overlooked when populations are considered as a whole

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<center>
<a href="https://wellcomeopenresearch.org/articles/5-117/v1">
https://wellcomeopenresearch.org/articles/5-117/v1
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- Open access to existing data in order to facilitate policy making that is fully informed by evidence. The dissemination of micro-data, with adequate safeguards to protect privacy and ensure confidentiality, expands significantly the potential uses of the information.

- Counting all people, because all of them count
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# Climate and environmental data

Climate and environmental data enables to quantify risk factors

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# Satellite data

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<br><br>
European Space Agency<br>
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# Monitoring stations

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# Geographic data for planning resources

Geographic data is needed for determining how to distribute medicines, planning vaccination strategies, etc.

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# Geographic data for planning resources

Unfortunately this is not the same in all parts of the world and geographic data is an impediment when it comes to determining how to distribute resources. We need to improve availability and quality of data.

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# Open data is key for monitoring and achieving global sustainable development

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# Where to find open data?

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# https://rspatialdata.github.io/

| Data  | R package  | Database  |
|:------------- |:-------------|:-----|
| Administrative boundaries | rgeoboundaries  | [geoBoundaries](https://www.geoboundaries.org/)  |
| Population  | wopr  | [WorldPop](https://www.worldpop.org/)  |
| OpenStreetMap  | osmdata  | [OpenStreetMap (OSM)](https://www.openstreetmap.org/)   |
| Elevation  | elevatr | [AWS Terrain Tiles](https://registry.opendata.aws/terrain-tiles/)  |
| Temperature  | raster  | [WorldClim](https://www.worldclim.org/)  |
| Rainfall  | nasapower  | [NASA-POWER Project](https://power.larc.nasa.gov/)  |
| Humidity  | nasapower  | [NASA-POWER Project](https://power.larc.nasa.gov/)  |
| Vegetation  | MODIStsp  | [MODIS](https://modis.gsfc.nasa.gov/data/dataprod/)  |
| Land cover  | MODIStsp  | [MODIS](https://modis.gsfc.nasa.gov/data/dataprod/)  |
| Air pollution  | openair  | [UK Department Environment Food & Rural Affairs](https://uk-air.defra.gov.uk/)  |
| Demographic and Health Surveys (DHS)  | rdhs |  [DHS Program](https://www.dhsprogram.com/)   |
| Malaria  | malariaAtlas  |[Malaria Atlas Project (MAP)](https://malariaatlas.org/data-project/)  |

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# Where to find open data?

- Open spatial data sources

https://rspatialdata.github.io/

- Administrative boundaries

https://www.geoboundaries.org/

- Population

https://www.worldpop.org/

- Climate and weather data

https://www.worldclim.org/data/index.html

- Google Earth Engine

Satellite imagery and geospatial datasets
 
https://earthengine.google.com/

- Open Street Map

https://www.openstreetmap.org/

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- The Demographic and Health Surveys (DHS) Program

https://dhsprogram.com/data/

- Data for SDGs

https://www.data4sdgs.org/
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# Analysis

## Need robust analytical tools to integrate complex data from different sources and different spatial and spatio-temporal resolutions

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# Combining data at different resolutions

### Air pollution. Fine particulate matter PM2.5

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# Satellite derived measurements (grid)

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# Ground measurements (points)

How to predict air pollution (PM2.5) using point-level data from monitoring stations and area-level data from satellites?

<a href="https://doi.org/10.1016/j.spasta.2017.04.006", style="height: 30px;bottom:10px;left: 80px;position: fixed;">Moraga, et al. Spatial Statistics, 2017</a>

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# Disease mapping

Disease maps help decision-makers to target health interventions and directing resources where most needed

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Map shows malaria prevalence in Mozambique.
Area-level data unable to show how disease risk varies within areas

Areal estimates make difficult targeting health interventions and directing resources where most needed

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<img src="./figures/mozambique1.png"  style="width:150%;"/>
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# Disaggregate area-level data

High-resolution estimates permit to
find differences in disease risk within study regions, and
identify areas and groups of people at higher risk

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# Quantifying risk factors

Investigate relationship between a response variable and explanatory variables available at different spatial scales
(e.g., lung cancer at county level and smoking at state level)

https://vizhub.healthdata.org/subnational/usa

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# Disease surveillance systems

Disease surveillance systems are critical to early detection of epidemics and the design of control
strategies

Traditional surveillance systems rely on data gathered with a considerable delay and
make surveillance systems ineffective for real-time surveillance

<br>

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# Digital data sources

Real-time digital information may enable to detect outbreaks earlier

<br>

<span style="color: gray">"Flu plus fever, not a good way to start the weekend"</span>

<br>

<span style="color: gray">"I'm so irritated at this cough and fever"</span>

<br>

<span style="color: gray">"This flu, fever & throat ache won't let me sleep"</span>

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# Demographic and environmental risk factors

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## Existing approaches do not produce inferences at spatial resolutions actionable by policymakers

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[The New York Times, 2008](https://www.nytimes.com/),
[Yang et al., 2015, PNAS](https://doi.org/10.1073/pnas.1515373112),
[HealthMap, 2018, PNAS](http://www.healthmap.org/en/)

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# Disease surveillance systems

Methods to integrate data from multiple sources and at different spatial and spatio-temporal resolutions taking into account all biases and uncertainties to produce local predictions of disease activity in real-time

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# Software

## Sharing data and code enables reproducibility, reliability and to advance science faster

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# [rOpenSci](https://ropensci.org/) - open tools for open science

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[rOpenSci](https://ropensci.org/) is an initiative that fosters a culture that values **open** and **reproducible** research using **shared** data and reusable software

- Computing infrastructure
- Data access
- Visualization
- Bayesian inference
- Machine learning
- Spatial and time analyses, etc

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<br><br>
<a href='https://www.repidemicsconsortium.org/' target='_blank'>https://www.repidemicsconsortium.org/</a><br>
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# epiflows. Travel-related spread of disease

<div style="color: gray; height: 30px;bottom:10px;left: 80px;position: fixed;">
<a href='https://f1000research.com/articles/7-1374/v3' target='_blank'>Moraga, et al. F1000 Research, 7:1374, 2019</a><br>
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Mathematical model that predicts the number of cases that could be spread to other locations from an infectious location together with uncertainty measures

Integrates information about the number of infectious cases, population flows, lengths of stay, incubation and infectious periods

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# SpatialEpiApp

Shiny app for disease risk estimation, cluster detection, and interactive viz

- Risk estimates by fitting Bayesian models with [INLA](http://www.r-inla.org/)
- Detection of clusters by using the scan statistics in [SaTScan](https://www.satscan.org/)

http://www.paulamoraga.com/software/

```r
library(devtools)
install_github("Paula-Moraga/SpatialEpiApp")
library(SpatialEpiApp)
run_app()
```

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# Communication and dissemination

## Effective communication and proper and timely dissemination are crucial for the development and implementation of population policies

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## Effective communication and proper and timely dissemination of information to those responsible for disease prevention and control is crucial for the development and implementation of population health policies

## There are tools available to share information and explore data in an interactive and approachable way by using interactive visualizations, dashboards and web applications
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# Interactive visualizations

Data exploration in an interactive and approachable way using maps, time plots, tables, and other visualizations

<br>

http://www.htmlwidgets.org/

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# Interactive visualizations

Overlay of maps of health data, risk factors, political boundaries and other geospatial information useful to put data into context

http://rstudio.github.io/leaflet/

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# Interactive dashboards and web applications

Identify information for specific regions, understand how disease
patterns change over time, compare risks between populations, measure inequalities, and
anticipate health threats for better planning and response

https://www.paulamoraga.com/book-geospatial/sec-flexdashboard.html

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https://rmarkdown.rstudio.com/flexdashboard/  
https://shiny.rstudio.com/

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<h3>Open and reliable data and analytical tools as well as collaborative research are crucial for solving global health challenges, achieving global sustainable development and leaving no one behind</h3>

<center><a href="https://sdgs.un.org/goals">https://sdgs.un.org/goals
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# KAUST Fellowship

## Full free tuition support, monthly living allowance, free housing and medical insurance

## KAUST http://kaust.edu.sa

## Statistics Program http://stat.kaust.edu.sa

## Admissions http://admissions.kaust.edu.sa

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# References

<small>

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Moraga, P. (2019). *Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny*. Chapman & Hall/CRC Press

Moraga, P., et al. (2019). epiflows: an R package for risk assessment of travel-related spread of disease. *F1000Research*, 7:1374

Moraga, P. (2018). Small Area Disease Risk Estimation and Visualization Using R. *The R Journal*, 10(1):495-506

Moraga, P. (2017). SpatialEpiApp: A Shiny Web Application for the analysis of Spatial and Spatio-Temporal Disease Data. *Spatial and Spatio-temporal Epidemiology*, 23:47-57

Moraga, P., et al. (2017). A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE. *Spatial Statistics*, 21:27-41

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Moraga, P. and Kulldorff, M. (2016). Detection of spatial variations in temporal trends with a quadratic function. *Statistical Methods for Medical Research*, 25(4):1422-1437

Hagan, J. E., Moraga, P., et al. (2016). Spatio-temporal determinants of urban leptospirosis transmission: Four-year prospective cohort study of slum residents in Brazil. *PLOS Neglected Tropical Diseases*, 10(1): e0004275

Moraga, P., et al. (2015). Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. *Parasites & Vectors*, 8:560

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Thanks!<br>
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<span style = 'font-size: 38px; font-weight:bold'>
Paula Moraga<br>
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<a href='http://twitter.com/Paula_Moraga_' target='_blank'> <i class='fa fa-twitter fa-fw'></i>&nbsp; @Paula_Moraga_</a><br>
<a href='https://Paula-Moraga.github.io/' target='_blank'><i class='fa fa-globe fa-fw'></i>&nbsp; www.PaulaMoraga.com</a><br>
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